{ "aibom": { "commit": "462388081577ba7155ace498cb7555304a01fbb7", "files": { "DATA-LICENSE.md": { "content": "# Data and editorial license\n\nOriginal comparison text and the JSON/CSV datasets in this repository are licensed under Creative Commons Attribution 4.0 International (CC BY 4.0):\n\nhttps://creativecommons.org/licenses/by/4.0/\n\nAttribution: `Five AIBOM Tools Compared, Pharos Production, 2026`.\n\nThird-party product names, documentation and specifications remain the property of their respective owners. The dataset summarizes factual statements and links to sources; it does not relicense those sources.\n", "sha256": "sha256:a1debdb6441829eeb250a34aedbe906bafae0d53487b4c16f23e2f744ca0bdf9", "url": "https://github.com/PharosProduction/aibom-tools-compared/blob/462388081577ba7155ace498cb7555304a01fbb7/DATA-LICENSE.md" }, "LICENSE": { "content": "MIT License\n\nCopyright (c) 2026 Pharos Production\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n", "sha256": "sha256:ede5a9e2b04994220b84d0e987968394fd1b0c40ee2b25925884110c039ddd71", "url": "https://github.com/PharosProduction/aibom-tools-compared/blob/462388081577ba7155ace498cb7555304a01fbb7/LICENSE" }, "README.md": { "content": "# Five AIBOM Tools Compared\n\nSource repository for the public, documentation-based AIBOM comparison at:\n\nhttps://pharosproduction.github.io/aibom-tools-compared/\n\nThe publication compares five genuine tools across discovery, AI component coverage, output standards, provenance, CI/CD and policy. It does not publish an overall winner and does not claim hands-on benchmark results.\n\n## Verify locally\n\n```bash\nnpm run verify\n```\n\n## Public data\n\n- `docs/data/tools.json`: full evidence matrix\n- `docs/data/tools.csv`: compact comparison\n- `docs/data/sources.json`: source register\n- `docs/data/claims.json`: claim-to-source mapping\n\nResearch cutoff: 2026-09-01.\n", "sha256": "sha256:ee2287de0511d41fdbf367a54374960bef60f3c6f1db456846857d6946072239", "url": "https://github.com/PharosProduction/aibom-tools-compared/blob/462388081577ba7155ace498cb7555304a01fbb7/README.md" }, "data/claims.json": { "content": "{\n \"evidenceCutoff\": \"2026-09-01\",\n \"claims\": [\n {\"id\": \"C01\", \"claim\": \"AIBOM is an inventory artifact, while generator, discovery and governance tools are different product jobs.\", \"sourceIds\": [\"S08\", \"S09\", \"S10\"], \"confidence\": \"high\", \"scope\": \"Standards and methodology\"},\n {\"id\": \"C02\", \"claim\": \"CycloneDX 1.7 can represent machine-learning models, datasets, model cards, formulation, declarations, claims and evidence.\", \"sourceIds\": [\"S08\"], \"confidence\": \"high\", \"scope\": \"Standard capability, not tool implementation\"},\n {\"id\": \"C03\", \"claim\": \"SPDX 3.0.1 defines an AI profile and a separate Dataset profile with license and relationship semantics.\", \"sourceIds\": [\"S09\", \"S10\"], \"confidence\": \"high\", \"scope\": \"Standard capability, not tool implementation\"},\n {\"id\": \"C04\", \"claim\": \"Cisco AI BOM 1.10.0 documents source, container and cloud scanning for a broad AI-asset inventory.\", \"sourceIds\": [\"S01\"], \"confidence\": \"high\", \"scope\": \"Documentation-based; not independently benchmarked\"},\n {\"id\": \"C05\", \"claim\": \"Trusera ai-bom documents broad source-visible discovery, multiple standard outputs and CI policy gates.\", \"sourceIds\": [\"S02\"], \"confidence\": \"high\", \"scope\": \"Documentation-based; not independently benchmarked\"},\n {\"id\": \"C06\", \"claim\": \"AIBoMGen CLI focuses on Hugging Face model and dataset references, completeness and CycloneDX output.\", \"sourceIds\": [\"S03\"], \"confidence\": \"high\", \"scope\": \"Documentation-based; not independently benchmarked\"},\n {\"id\": \"C07\", \"claim\": \"OWASP AIBOM Generator creates downloadable, completeness-scored records for Hugging Face models.\", \"sourceIds\": [\"S04\", \"S05\"], \"confidence\": \"high\", \"scope\": \"Official project description and live Space metadata\"},\n {\"id\": \"C08\", \"claim\": \"Mend AI-BOM exports CycloneDX 1.7 or SPDX 3.0.1 JSON after eligible scans, with explicit content and entitlement limits.\", \"sourceIds\": [\"S06\", \"S07\"], \"confidence\": \"high\", \"scope\": \"Official documentation; commercial entitlement required\"}\n ]\n}\n", "sha256": "sha256:ccecd94d3b401ba500e057ca80c9129897e6aec11b36ea92a2c1b42668eca758", "url": "https://github.com/PharosProduction/aibom-tools-compared/blob/462388081577ba7155ace498cb7555304a01fbb7/data/claims.json" }, "data/sources.json": { "content": "{\n \"evidenceCutoff\": \"2026-09-01\",\n \"sources\": [\n {\n \"id\": \"S01\",\n \"publisher\": \"Cisco AI Defense\",\n \"title\": \"Cisco AI BOM repository and documentation\",\n \"url\": \"https://github.com/cisco-ai-defense/aibom\",\n \"type\": \"official repository\",\n \"lastVerified\": \"2026-09-01\",\n \"version\": \"1.10.0\",\n \"publishedOrUpdated\": \"2026-08-17\",\n \"scope\": \"Inputs, AI asset coverage, output commands, policy and agentic-mode prerequisites.\"\n },\n {\n \"id\": \"S02\",\n \"publisher\": \"Trusera\",\n \"title\": \"ai-bom repository and documentation\",\n \"url\": \"https://github.com/Trusera/ai-bom\",\n \"type\": \"official repository\",\n \"lastVerified\": \"2026-09-01\",\n \"version\": \"v3.1.0\",\n \"publishedOrUpdated\": \"2026-05-24\",\n \"scope\": \"Scanner surfaces, formats, CI examples, risk gates and Cedar policy support.\"\n },\n {\n \"id\": \"S03\",\n \"publisher\": \"IDLab, Ghent University and imec\",\n \"title\": \"AIBoMGen CLI repository and documentation\",\n \"url\": \"https://github.com/idlab-discover/aibomgen-cli\",\n \"type\": \"official repository\",\n \"lastVerified\": \"2026-09-01\",\n \"version\": \"v0.2.1\",\n \"publishedOrUpdated\": \"2026-08-26\",\n \"scope\": \"Hugging Face discovery, CycloneDX generation, completeness, enrichment, vulnerability import and SBOM merge.\"\n },\n {\n \"id\": \"S04\",\n \"publisher\": \"OWASP GenAI Security Project\",\n \"title\": \"OWASP AIBOM Generator\",\n \"url\": \"https://genai.owasp.org/resource/owasp-aibom-generator/\",\n \"type\": \"official project page\",\n \"lastVerified\": \"2026-09-01\",\n \"version\": \"Hugging Face Space commit 6165ba9\",\n \"publishedOrUpdated\": \"2026-03-12\",\n \"scope\": \"Hugging Face model input, CycloneDX-aligned JSON, visualization and completeness scoring.\"\n },\n {\n \"id\": \"S05\",\n \"publisher\": \"OWASP GenAI Security Project\",\n \"title\": \"OWASP AIBOM Generator live Space\",\n \"url\": \"https://huggingface.co/spaces/GenAISecurityProject/OWASP-AIBOM-Generator\",\n \"type\": \"official hosted tool\",\n \"lastVerified\": \"2026-09-01\",\n \"version\": \"commit 6165ba9ef889951dc0fc5065a3cc65c45bc01f63\",\n \"publishedOrUpdated\": \"2026-03-12\",\n \"scope\": \"Public availability, hosting mode and project identity.\"\n },\n {\n \"id\": \"S06\",\n \"publisher\": \"Mend.io\",\n \"title\": \"The AI Bill of Materials Report\",\n \"url\": \"https://docs.mend.io/platform/latest/the-ai-bill-of-materials-report\",\n \"type\": \"official product documentation\",\n \"lastVerified\": \"2026-09-01\",\n \"version\": \"documentation current at cutoff\",\n \"publishedOrUpdated\": \"2026-08-24\",\n \"scope\": \"Report categories, export versions, API scopes, entitlements and explicit limitations.\"\n },\n {\n \"id\": \"S07\",\n \"publisher\": \"Mend.io\",\n \"title\": \"Mend AI release notes\",\n \"url\": \"https://docs.mend.io/platform/latest/mend-ai-release-notes\",\n \"type\": \"official release notes\",\n \"lastVerified\": \"2026-09-01\",\n \"version\": \"2026-08-24 release\",\n \"publishedOrUpdated\": \"2026-08-24\",\n \"scope\": \"Feature release date, detected AI entities, metadata-only export and triage audit trail.\"\n },\n {\n \"id\": \"S08\",\n \"publisher\": \"CycloneDX\",\n \"title\": \"Machine Learning Bill of Materials capability\",\n \"url\": \"https://cyclonedx.org/capabilities/mlbom/\",\n \"type\": \"official standard documentation\",\n \"lastVerified\": \"2026-09-01\",\n \"version\": \"CycloneDX 1.7\",\n \"publishedOrUpdated\": \"2025-10\",\n \"scope\": \"Model cards, datasets, dependencies, provenance, formulation, claims and attestations.\"\n },\n {\n \"id\": \"S09\",\n \"publisher\": \"SPDX\",\n \"title\": \"SPDX 3.0.1 AI Profile\",\n \"url\": \"https://spdx.github.io/spdx-spec/v3.0.1/model/AI/AI/\",\n \"type\": \"official standard specification\",\n \"lastVerified\": \"2026-09-01\",\n \"version\": \"3.0.1\",\n \"publishedOrUpdated\": \"2024-12\",\n \"scope\": \"AI package identity, license relationships and AI profile conformance.\"\n },\n {\n \"id\": \"S10\",\n \"publisher\": \"Linux Foundation Research and SPDX\",\n \"title\": \"Implementing AI Bill of Materials with SPDX 3.0\",\n \"url\": \"https://www.linuxfoundation.org/hubfs/LF%20Research/lfr_spdx_aibom_102524a.pdf?hsLang=en\",\n \"type\": \"official research report\",\n \"lastVerified\": \"2026-09-01\",\n \"version\": \"October 2024\",\n \"publishedOrUpdated\": \"2024-10\",\n \"scope\": \"AI and dataset field mapping, model cards, FactSheets, provenance and known profile gaps.\"\n },\n {\n \"id\": \"S11\",\n \"publisher\": \"Pharos Production\",\n \"title\": \"AI Supply Chain Security: From SBOM to AIBOM, Slopsquatting and Model Poisoning\",\n \"url\": \"https://pharosproduction.com/insights/engineering/ai-supply-chain-security-2026/\",\n \"type\": \"publisher engineering article\",\n \"lastVerified\": \"2026-09-01\",\n \"version\": \"updated 2026-07-20; reviewed 2026-08-15\",\n \"publishedOrUpdated\": \"2026-08-15\",\n \"scope\": \"Operational motivation, model poisoning controls and the publisher's scoped security-audit method.\"\n }\n ]\n}\n", "sha256": "sha256:5986c15f4d4b98c3bfe196067cd34b042afa2e5adb773b5c12f5a22063c353a5", "url": "https://github.com/PharosProduction/aibom-tools-compared/blob/462388081577ba7155ace498cb7555304a01fbb7/data/sources.json" }, "data/tools.json": { "content": "{\n \"title\": \"Five AIBOM Tools Compared\",\n \"method\": \"Documentation-based comparison; no hands-on product benchmark\",\n \"evidenceCutoff\": \"2026-09-01\",\n \"statusLegend\": {\n \"documented\": \"Explicitly supported by current official documentation at the research cutoff.\",\n \"partial\": \"Supported for a narrower scope, format or workflow than the comparison axis.\",\n \"not-public\": \"Not established in the public sources reviewed; this does not prove the feature is absent.\"\n },\n \"tools\": [\n {\n \"id\": \"aibomgen-cli\",\n \"name\": \"AIBoMGen CLI\",\n \"publisher\": \"IDLab, Ghent University and imec\",\n \"delivery\": \"Open-source Go CLI\",\n \"license\": \"AGPL-3.0\",\n \"version\": \"v0.2.1; repository active at cutoff\",\n \"scenarioFit\": [\"Hugging Face model intake\", \"AIBOM and SBOM merge\", \"Completeness review\"],\n \"disqualifiers\": [\"Your main inventory problem is agent, prompt, MCP or live-cloud discovery outside Hugging Face workflows.\"],\n \"sources\": [\"S03\"],\n \"axes\": {\n \"discovery\": {\"status\": \"partial\", \"detail\": \"Scans repositories for Hugging Face model and dataset references across several file types; it is not documented as a live-cloud or runtime discovery platform.\", \"sourceIds\": [\"S03\"]},\n \"aiCoverage\": {\"status\": \"partial\", \"detail\": \"Focused on Hugging Face models and datasets, with security-scan metadata; prompts, general agent graphs and MCP components are outside the documented core.\", \"sourceIds\": [\"S03\"]},\n \"outputs\": {\"status\": \"documented\", \"detail\": \"Generates and validates CycloneDX JSON or XML, supports versions 1.4 through 1.6, and can merge one or more AIBOMs into an existing SBOM.\", \"sourceIds\": [\"S03\"]},\n \"provenance\": {\"status\": \"partial\", \"detail\": \"Captures hub metadata, linked datasets, completeness and vulnerability evidence, but cryptographic signing and attestation issuance are not documented CLI functions.\", \"sourceIds\": [\"S03\"]},\n \"cicd\": {\"status\": \"documented\", \"detail\": \"A companion GitHub Action is documented for automated generation, and strict validation can fail on missing required fields or a minimum completeness score.\", \"sourceIds\": [\"S03\"]},\n \"policy\": {\"status\": \"partial\", \"detail\": \"Strict validation and completeness thresholds provide a build gate, but a general authorization or policy-as-code engine is not documented.\", \"sourceIds\": [\"S03\"]}\n }\n },\n {\n \"id\": \"cisco-ai-bom\",\n \"name\": \"Cisco AI BOM\",\n \"publisher\": \"Cisco AI Defense\",\n \"delivery\": \"Open-source Python CLI\",\n \"license\": \"Apache-2.0\",\n \"version\": \"1.10.0 released 2026-08-17\",\n \"scenarioFit\": [\"Polyglot source discovery\", \"Container and cloud inventory\", \"Agent and MCP inventory\"],\n \"disqualifiers\": [\"You cannot permit the configured LLM dependency required by the documented agentic classification workflow.\"],\n \"sources\": [\"S01\"],\n \"axes\": {\n \"discovery\": {\"status\": \"documented\", \"detail\": \"Scans source code, container images and cloud environments, with documented language support across Python, JavaScript or TypeScript, Java, Go, Rust, Ruby and C#.\", \"sourceIds\": [\"S01\"]},\n \"aiCoverage\": {\"status\": \"documented\", \"detail\": \"Inventories models, agents, tools, MCP servers and clients, datasets, prompts, guardrails, secrets and other AI assets detected in software.\", \"sourceIds\": [\"S01\"]},\n \"outputs\": {\"status\": \"documented\", \"detail\": \"Official examples document terminal, JSON and CSV outputs; the public comparison should not assume broader standards export without confirming the selected release.\", \"sourceIds\": [\"S01\"]},\n \"provenance\": {\"status\": \"partial\", \"detail\": \"Maps discovered assets and cross-references with source evidence, but public documentation reviewed does not establish signed attestations for generated inventories.\", \"sourceIds\": [\"S01\"]},\n \"cicd\": {\"status\": \"partial\", \"detail\": \"The CLI is scriptable, yet the reviewed release documentation does not present a first-party CI template equivalent to a documented GitHub Action workflow.\", \"sourceIds\": [\"S01\"]},\n \"policy\": {\"status\": \"partial\", \"detail\": \"Risk and authorization context can be derived from the inventory, but a general production policy-as-code gate is not established in the reviewed public documentation.\", \"sourceIds\": [\"S01\"]}\n }\n },\n {\n \"id\": \"mend-ai-bom\",\n \"name\": \"Mend AI-BOM\",\n \"publisher\": \"Mend.io\",\n \"delivery\": \"Commercial platform and CLI-assisted scan\",\n \"license\": \"Commercial; entitlement required\",\n \"version\": \"AI-BOM report released 2026-08-24\",\n \"scenarioFit\": [\"Organization-wide reporting\", \"Application and project inventory\", \"Existing Mend governance workflow\"],\n \"disqualifiers\": [\"You need a no-account, fully local generator or need framework libraries and vulnerability records inside the same AI-BOM export.\"],\n \"sources\": [\"S06\", \"S07\"],\n \"axes\": {\n \"discovery\": {\"status\": \"documented\", \"detail\": \"Uses completed Mend AI repository scans and can generate reports at organization, application, project or label scope from the platform or API.\", \"sourceIds\": [\"S06\", \"S07\"]},\n \"aiCoverage\": {\"status\": \"documented\", \"detail\": \"Models are included by default; eligible subscriptions add system prompts, agent configurations, agents, tools and MCP components when detected.\", \"sourceIds\": [\"S06\", \"S07\"]},\n \"outputs\": {\"status\": \"documented\", \"detail\": \"Exports JSON as CycloneDX 1.7 or SPDX 3.0.1, with one AI-BOM per eligible repository snapshot and ZIP packaging for multiple snapshots.\", \"sourceIds\": [\"S06\"]},\n \"provenance\": {\"status\": \"partial\", \"detail\": \"Includes file and line evidence where available plus hashes and metadata, but raw prompt or configuration content is excluded and signed attestations are not documented.\", \"sourceIds\": [\"S06\"]},\n \"cicd\": {\"status\": \"documented\", \"detail\": \"Mend CLI and repository integrations initiate scans; an asynchronous API creates reports after completed scans, fitting established platform pipelines.\", \"sourceIds\": [\"S06\", \"S07\"]},\n \"policy\": {\"status\": \"documented\", \"detail\": \"The platform documents entity triage states, suppressions with audit trail, model-usage and licensing governance workflows, subject to product entitlement.\", \"sourceIds\": [\"S07\"]}\n }\n },\n {\n \"id\": \"owasp-aibom-generator\",\n \"name\": \"OWASP AIBOM Generator\",\n \"publisher\": \"OWASP GenAI Security Project\",\n \"delivery\": \"Open-source hosted web tool\",\n \"license\": \"Apache-2.0\",\n \"version\": \"Hugging Face Space commit 6165ba9; last modified 2026-03-12\",\n \"scenarioFit\": [\"Single Hugging Face model review\", \"AIBOM field education\", \"Completeness demonstration\"],\n \"disqualifiers\": [\"You require continuous multi-repository discovery, enterprise RBAC, a CI policy gate or private artifact handling without first validating the hosting boundary.\"],\n \"sources\": [\"S04\", \"S05\"],\n \"axes\": {\n \"discovery\": {\"status\": \"partial\", \"detail\": \"Accepts Hugging Face-hosted models and extracts model metadata; it is not documented as a general source, container, cloud or runtime scanner.\", \"sourceIds\": [\"S04\", \"S05\"]},\n \"aiCoverage\": {\"status\": \"partial\", \"detail\": \"Covers model metadata and dependencies available for a selected Hugging Face model; general prompts, agents, MCP and shadow-AI discovery are not documented.\", \"sourceIds\": [\"S04\"]},\n \"outputs\": {\"status\": \"documented\", \"detail\": \"Provides an AIBOM JSON download in CycloneDX format aligned with SPDX conventions, alongside a human-readable visualization.\", \"sourceIds\": [\"S04\"]},\n \"provenance\": {\"status\": \"partial\", \"detail\": \"Exposes model metadata, field coverage and missing-field completeness, but no signed attestation or independent verification chain is documented.\", \"sourceIds\": [\"S04\"]},\n \"cicd\": {\"status\": \"not-public\", \"detail\": \"A public interactive Space is documented; a first-party supported CI integration was not established in the reviewed official project materials.\", \"sourceIds\": [\"S04\", \"S05\"]},\n \"policy\": {\"status\": \"not-public\", \"detail\": \"Completeness scoring supports review, but an enforceable policy engine, approval workflow or deployment gate is not documented.\", \"sourceIds\": [\"S04\"]}\n }\n },\n {\n \"id\": \"trusera-ai-bom\",\n \"name\": \"Trusera ai-bom\",\n \"publisher\": \"Trusera\",\n \"delivery\": \"Open-source Python CLI with optional platform\",\n \"license\": \"Apache-2.0\",\n \"version\": \"v3.1.0 released 2026-02-12; repository active at cutoff\",\n \"scenarioFit\": [\"Broad developer-side discovery\", \"GitHub or GitLab policy gate\", \"AI agent and workflow inventory\"],\n \"disqualifiers\": [\"Your primary requirement is vulnerability scanning for OS packages or complete training-data lineage rather than source-visible AI component discovery.\"],\n \"sources\": [\"S02\"],\n \"axes\": {\n \"discovery\": {\"status\": \"documented\", \"detail\": \"Documents 13 scanner classes across code, model files, Docker, cloud IaC, network, notebooks, GitHub Actions, n8n and MCP configuration.\", \"sourceIds\": [\"S02\"]},\n \"aiCoverage\": {\"status\": \"documented\", \"detail\": \"Detects LLM providers, model references, agent frameworks, MCP, A2A, n8n nodes, AI containers, cloud AI resources and model-file extensions.\", \"sourceIds\": [\"S02\"]},\n \"outputs\": {\"status\": \"documented\", \"detail\": \"Documents CycloneDX 1.6, SPDX 3.0, SARIF 2.1.0, HTML, Markdown, CSV, JUnit and terminal outputs with schema validation.\", \"sourceIds\": [\"S02\"]},\n \"provenance\": {\"status\": \"partial\", \"detail\": \"Preserves source findings and risk properties in generated artifacts, but deep training-data lineage and signed attestation issuance are not documented as core scanner outputs.\", \"sourceIds\": [\"S02\"]},\n \"cicd\": {\"status\": \"documented\", \"detail\": \"Provides a GitHub Action, GitLab CI template, SARIF upload examples and fail-on severity controls for build pipelines.\", \"sourceIds\": [\"S02\"]},\n \"policy\": {\"status\": \"documented\", \"detail\": \"Supports threshold policies, blocked flags or providers and a Cedar policy gate; standalone SDK mode can enforce local runtime HTTP policies.\", \"sourceIds\": [\"S02\"]}\n }\n }\n ]\n}\n", "sha256": "sha256:13c2f516b1aaa41bea93a799cc4999d38490e365faa1c8a31eeb4731593fd49a", "url": "https://github.com/PharosProduction/aibom-tools-compared/blob/462388081577ba7155ace498cb7555304a01fbb7/data/tools.json" } }, "independent_human_review": "NOT_REVIEWED", "pages": "https://pharosproduction.github.io/aibom-tools-compared/", "repo": "https://github.com/PharosProduction/aibom-tools-compared", "snapshot_at": "2026-09-17T06:17:24Z" }, "drift": { "commit": "f2938cfb84bbe09c5eab4f95dd404f1ca67c39ae", "files": { "LICENSE": { "content": "MIT License\n\nCopyright (c) 2026 Pharos Production\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n", "sha256": "sha256:48463cc27a86bdc41249f2e93168615728c7860d350b4dd51c7606e5c512555e", "url": "https://github.com/PharosProduction/production-ai-drift-planner/blob/f2938cfb84bbe09c5eab4f95dd404f1ca67c39ae/LICENSE" }, "README.md": { "content": "# Production AI Drift Planner\n\n[Open the planner](https://pharosproduction.github.io/production-ai-drift-planner/).\n\nCreate a local monitoring plan for production chat, RAG and tool-using agents. Choose observation policy, traffic, evaluation sampling, owner, compatible fallback and metric thresholds. Export the exact plan as JSON or Markdown.\n\n## Scope\n\nThis is planning software. It connects to no telemetry and executes no alerts or rollback. All example thresholds require local calibration. Sample estimates are not observed eligible denominators. No independent human validation or production benchmark is claimed.\n\n## Run and verify\n\nRequires Node.js 22+ and Python 3 for a local static server. There are no npm dependencies.\n\n```sh\nnpm run verify\npython3 -m http.server 4188 --bind 127.0.0.1 --directory docs\n```\n\nThe source template is `scripts/index.template.html`; the source metric and citation records live in `docs/data/metrics.json` and `docs/data/sources.json`. `npm run build` regenerates the static HTML and downloadable example plans. Do not edit generated `docs/index.html` directly. `docs/assets/planner.mjs` is the pure calculation and export module.\n\nThe browser QA script uses a local Playwright/Chrome installation, configurable through `PLAYWRIGHT_MODULE` and `CHROME_PATH`. Set `DRIFT_BASE_URL` to the live site to verify deployment. Unit and release checks run in GitHub Actions before only the `docs/` directory is deployed.\n\n## Evidence and review\n\nSee the on-page source and methodology section, the source register, `research/semantic-map.md` and compact reports in `audits/`. Source concepts, original metric definitions and illustrative parameters are distinguished. Automated implementation and technical review: Codex, 2026-09-08. The user authorized publication; that is not a claim of independent expert validation.\n\nOriginal code and worksheet content: MIT. Linked third-party documents retain their owners' rights. Report corrections through repository issues without sharing private traces or credentials.\n", "sha256": "sha256:a693969f8aa17956404eb17d32472d0c487c374ca0caf3ccec4b267dbec80a58", "url": "https://github.com/PharosProduction/production-ai-drift-planner/blob/f2938cfb84bbe09c5eab4f95dd404f1ca67c39ae/README.md" }, "docs/data/example-plan.json": { "content": "{\n \"version\": \"1.0.0\",\n \"status\": \"DRAFT_REQUIRES_CALIBRATION\",\n \"config\": {\n \"system\": \"rag\",\n \"dailyRequests\": 1000,\n \"evaluationPercent\": 10,\n \"baselineDays\": 14,\n \"windowHours\": 24,\n \"minSamples\": 100,\n \"persistence\": 2,\n \"owner\": \"\",\n \"rollbackTarget\": \"\",\n \"thresholds\": {}\n },\n \"metrics\": [\n {\n \"id\": \"input_shift\",\n \"name\": \"Input distribution shift\",\n \"systems\": [\n \"chat\",\n \"rag\",\n \"agent\"\n ],\n \"sampleClass\": \"requests\",\n \"threshold\": 0.15,\n \"min\": 0,\n \"max\": 1,\n \"unit\": \"TVD\",\n \"comparator\": \"gt\",\n \"definition\": \"Total variation distance = 0.5 × sum of absolute differences in category proportions between current and reference prompts. Freeze the same intent/language taxonomy and include an unknown category. Denominator: prompts with a recorded category.\",\n \"rationale\": \"A changed request mix can invalidate an old evaluation set without proving a quality regression.\",\n \"response\": \"Inspect affected intent and language slices. Compare downstream outcomes and expand the holdout set.\",\n \"rollback\": \"Do not roll back for input shift alone. Consider a release rollback only if a matched-cohort evaluation links degradation to that release.\",\n \"sources\": [\n \"S2\",\n \"S3\"\n ],\n \"thresholdBasis\": \"Illustrative setting. Calibrate against local baseline and error budget.\",\n \"sourceReferences\": [\n {\n \"id\": \"S2\",\n \"url\": \"https://docs.aws.amazon.com/prescriptive-guidance/latest/gen-ai-lifecycle-operational-excellence/prod-monitoring-drift.html\",\n \"checked\": \"2026-09-08\"\n },\n {\n \"id\": \"S3\",\n \"url\": \"https://docs.evidentlyai.com/metrics/explainer_drift\",\n \"checked\": \"2026-09-08\"\n }\n ],\n \"expectedSamples\": 1000,\n \"sampleStatus\": \"SAMPLE_ESTIMATE_ONLY\"\n },\n {\n \"id\": \"task_success\",\n \"name\": \"Task success rate\",\n \"systems\": [\n \"chat\",\n \"rag\",\n \"agent\"\n ],\n \"sampleClass\": \"evaluated\",\n \"threshold\": 90,\n \"min\": 0,\n \"max\": 100,\n \"unit\": \"%\",\n \"comparator\": \"lt\",\n \"definition\": \"100 × evaluated completed tasks that pass the fixed task rubric / all eligible completed tasks evaluated. Record unscored and pending labels separately.\",\n \"rationale\": \"Business outcomes can change while input distributions remain stable.\",\n \"response\": \"Re-score a matched sample, examine labels and group failures by release, task and language.\",\n \"rollback\": \"Use the last compatible release only after a repeated quality breach is attributable to a change and the candidate passes the same eval set.\",\n \"sources\": [\n \"S2\",\n \"S6\"\n ],\n \"thresholdBasis\": \"Illustrative setting. 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Do not average per-host percentiles.\",\n \"rationale\": \"A quality-preserving release can still exceed the user-facing latency budget.\",\n \"response\": \"Inspect queue time, retrieval, retries and model spans. Compare like-for-like workload slices.\",\n \"rollback\": \"If a canary release causes an SLO breach, route to a compatible known-good path. Check quality and capacity before expanding traffic.\",\n \"sources\": [\n \"S5\"\n ],\n \"thresholdBasis\": \"Illustrative setting. 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Track answers with no checkable claims separately.\",\n \"rationale\": \"The fraction measures evidence support under a rubric, not universal factual accuracy.\",\n \"response\": \"Inspect source validity, chunk boundaries and retrieval versions. Sample judge disagreements for human review.\",\n \"rollback\": \"Revert the corpus, retriever or prompt only if a replay isolates that component and the restored corpus remains valid and authorized.\",\n \"sources\": [\n \"S1\",\n \"S6\"\n ],\n \"thresholdBasis\": \"Illustrative setting. 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Exclude queries without labeled relevant documents and report their count.\",\n \"rationale\": \"A current corpus can still miss the document needed to answer the question.\",\n \"response\": \"Separate missing source documents from retriever/reranker failures. Rerun against the previous index.\",\n \"rollback\": \"Restore a compatible index or retriever only after the same relevance set improves and required document updates remain available.\",\n \"sources\": [\n \"S1\",\n \"S6\"\n ],\n \"thresholdBasis\": \"Illustrative setting. 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Measure deletion propagation separately.\",\n \"rationale\": \"An ingestion backlog can make grounded answers obsolete even when the model has not changed.\",\n \"response\": \"Inspect failed ingestion, change feeds, index jobs and outstanding deletions. Identify affected sources.\",\n \"rollback\": \"Pause answers from affected sources or use a reviewed fallback. Never restore revoked, deleted or unauthorized documents to improve a metric.\",\n \"sources\": [\n \"S1\"\n ],\n \"thresholdBasis\": \"Illustrative setting. Calibrate against local baseline and error budget.\",\n \"sourceReferences\": [\n {\n \"id\": \"S1\",\n \"url\": \"https://pharosproduction.com/insights/engineering/state-of-production-ai-engineering-2026/\",\n \"checked\": \"2026-09-08\"\n }\n ],\n \"expectedSamples\": null,\n \"sampleStatus\": \"EVENT_BASED\"\n },\n {\n \"id\": \"prompt_changes\",\n \"name\": \"Unreviewed prompt releases\",\n \"systems\": [\n \"chat\",\n \"rag\",\n \"agent\"\n ],\n \"sampleClass\": \"event\",\n \"threshold\": 0,\n \"min\": 0,\n \"max\": 0,\n \"unit\": \"releases\",\n \"comparator\": \"gt\",\n \"definition\": \"Count of active prompt-template hashes that lack a successful regression evaluation and an approved release record. Prompt entropy here means uncontrolled template changes, not a Shannon entropy metric.\",\n \"rationale\": \"Without a release record, changed behavior cannot be attributed or reproduced.\",\n \"response\": \"Stop promotion and compare active hashes with the approved prompt registry.\",\n \"rollback\": \"Restore the approved compatible prompt only after verifying tool permissions, model and schema compatibility. This event bypasses sample and persistence gates.\",\n \"sources\": [\n \"S1\"\n ],\n \"thresholdBasis\": \"Illustrative setting. Calibrate against local baseline and error budget.\",\n \"sourceReferences\": [\n {\n \"id\": \"S1\",\n \"url\": \"https://pharosproduction.com/insights/engineering/state-of-production-ai-engineering-2026/\",\n \"checked\": \"2026-09-08\"\n }\n ],\n \"expectedSamples\": null,\n \"sampleStatus\": \"EVENT_BASED\"\n },\n {\n \"id\": \"model_lifecycle\",\n \"name\": \"Provider sunset lead time\",\n \"systems\": [\n \"chat\",\n \"rag\",\n \"agent\"\n ],\n \"sampleClass\": \"event\",\n \"threshold\": 30,\n \"min\": 1,\n \"max\": 365,\n \"unit\": \"days\",\n \"comparator\": \"lt\",\n \"definition\": \"Days remaining until the provider-published retirement date for the exact deployed model snapshot. Unknown retirement dates are unknown, never zero or safe.\",\n \"rationale\": \"A scheduled model retirement requires a tested migration path. It is a lifecycle event rather than a statistical drift score.\",\n \"response\": \"Verify the provider notice and rehearse the replacement against the current eval set.\",\n \"rollback\": \"Use a supported pre-evaluated fallback if the snapshot becomes unavailable. Do not roll back to a retired model. Check daily and on notices. No sample gate.\",\n \"sources\": [\n \"S1\"\n ],\n \"thresholdBasis\": \"Illustrative setting. Calibrate against local baseline and error budget.\",\n \"sourceReferences\": [\n {\n \"id\": \"S1\",\n \"url\": \"https://pharosproduction.com/insights/engineering/state-of-production-ai-engineering-2026/\",\n \"checked\": \"2026-09-08\"\n }\n ],\n \"expectedSamples\": null,\n \"sampleStatus\": \"EVENT_BASED\"\n }\n ],\n \"warnings\": [\n \"Draft requires calibration. Expected samples are traffic estimates, not observed eligible denominators.\",\n \"Missing owner: assign a responsible reviewer.\",\n \"Missing rollback target: identify a compatible fallback.\"\n ]\n}\n", "sha256": "sha256:680ca27b09afad47cee7ee2a014f9eaeb13a97d06d69126525564bb6294855b8", "url": "https://github.com/PharosProduction/production-ai-drift-planner/blob/f2938cfb84bbe09c5eab4f95dd404f1ca67c39ae/docs/data/example-plan.json" }, "docs/data/metrics.json": { "content": "[\n {\n \"id\": \"input_shift\",\n \"name\": \"Input distribution shift\",\n \"systems\": [\n \"chat\",\n \"rag\",\n \"agent\"\n ],\n \"sampleClass\": \"requests\",\n \"threshold\": 0.15,\n \"min\": 0,\n \"max\": 1,\n \"unit\": \"TVD\",\n \"comparator\": \"gt\",\n \"definition\": \"Total variation distance = 0.5 × sum of absolute differences in category proportions between current and reference prompts. Freeze the same intent/language taxonomy and include an unknown category. 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Calibrate against local baseline and error budget.\",\n \"sourceReferences\": [\n {\n \"id\": \"S2\",\n \"url\": \"https://docs.aws.amazon.com/prescriptive-guidance/latest/gen-ai-lifecycle-operational-excellence/prod-monitoring-drift.html\",\n \"checked\": \"2026-09-08\"\n },\n {\n \"id\": \"S6\",\n \"url\": \"https://docs.evidentlyai.com/docs/platform/monitoring_overview\",\n \"checked\": \"2026-09-08\"\n }\n ]\n },\n {\n \"id\": \"refusal\",\n \"name\": \"Benign-request refusal rate\",\n \"systems\": [\n \"chat\",\n \"rag\",\n \"agent\"\n ],\n \"sampleClass\": \"evaluated\",\n \"threshold\": 10,\n \"min\": 0,\n \"max\": 100,\n \"unit\": \"%\",\n \"comparator\": \"gt\",\n \"definition\": \"100 × refused requests judged answerable and allowed / evaluated requests judged answerable and allowed. Exclude unsafe prompts from this denominator and monitor unsafe acceptance separately.\",\n \"rationale\": \"A rise may indicate a policy or prompt regression. 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Calibrate against local baseline and error budget.\",\n \"sourceReferences\": [\n {\n \"id\": \"S1\",\n \"url\": \"https://pharosproduction.com/insights/engineering/state-of-production-ai-engineering-2026/\",\n \"checked\": \"2026-09-08\"\n },\n {\n \"id\": \"S6\",\n \"url\": \"https://docs.evidentlyai.com/docs/platform/monitoring_overview\",\n \"checked\": \"2026-09-08\"\n }\n ]\n },\n {\n \"id\": \"recall\",\n \"name\": \"Retrieval recall@k\",\n \"systems\": [\n \"rag\"\n ],\n \"sampleClass\": \"evaluated\",\n \"threshold\": 80,\n \"min\": 0,\n \"max\": 100,\n \"unit\": \"%\",\n \"comparator\": \"lt\",\n \"definition\": \"Mean per-query percentage of labeled relevant documents found within the top k results, on the versioned retrieval evaluation set. Freeze k and relevance labels. Exclude queries without labeled relevant documents and report their count.\",\n \"rationale\": \"A current corpus can still miss the document needed to answer the question.\",\n \"response\": \"Separate missing source documents from retriever/reranker failures. Rerun against the previous index.\",\n \"rollback\": \"Restore a compatible index or retriever only after the same relevance set improves and required document updates remain available.\",\n \"sources\": [\n \"S1\",\n \"S6\"\n ],\n \"thresholdBasis\": \"Illustrative setting. 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Measure deletion propagation separately.\",\n \"rationale\": \"An ingestion backlog can make grounded answers obsolete even when the model has not changed.\",\n \"response\": \"Inspect failed ingestion, change feeds, index jobs and outstanding deletions. Identify affected sources.\",\n \"rollback\": \"Pause answers from affected sources or use a reviewed fallback. Never restore revoked, deleted or unauthorized documents to improve a metric.\",\n \"sources\": [\n \"S1\"\n ],\n \"thresholdBasis\": \"Illustrative setting. Calibrate against local baseline and error budget.\",\n \"sourceReferences\": [\n {\n \"id\": \"S1\",\n \"url\": \"https://pharosproduction.com/insights/engineering/state-of-production-ai-engineering-2026/\",\n \"checked\": \"2026-09-08\"\n }\n ]\n },\n {\n \"id\": \"prompt_changes\",\n \"name\": \"Unreviewed prompt releases\",\n \"systems\": [\n \"chat\",\n \"rag\",\n \"agent\"\n ],\n \"sampleClass\": \"event\",\n \"threshold\": 0,\n \"min\": 0,\n \"max\": 0,\n \"unit\": \"releases\",\n \"comparator\": \"gt\",\n \"definition\": \"Count of active prompt-template hashes that lack a successful regression evaluation and an approved release record. Prompt entropy here means uncontrolled template changes, not a Shannon entropy metric.\",\n \"rationale\": \"Without a release record, changed behavior cannot be attributed or reproduced.\",\n \"response\": \"Stop promotion and compare active hashes with the approved prompt registry.\",\n \"rollback\": \"Restore the approved compatible prompt only after verifying tool permissions, model and schema compatibility. This event bypasses sample and persistence gates.\",\n \"sources\": [\n \"S1\"\n ],\n \"thresholdBasis\": \"Illustrative setting. Calibrate against local baseline and error budget.\",\n \"sourceReferences\": [\n {\n \"id\": \"S1\",\n \"url\": \"https://pharosproduction.com/insights/engineering/state-of-production-ai-engineering-2026/\",\n \"checked\": \"2026-09-08\"\n }\n ]\n },\n {\n \"id\": \"model_lifecycle\",\n \"name\": \"Provider sunset lead time\",\n \"systems\": [\n \"chat\",\n \"rag\",\n \"agent\"\n ],\n \"sampleClass\": \"event\",\n \"threshold\": 30,\n \"min\": 1,\n \"max\": 365,\n \"unit\": \"days\",\n \"comparator\": \"lt\",\n \"definition\": \"Days remaining until the provider-published retirement date for the exact deployed model snapshot. Unknown retirement dates are unknown, never zero or safe.\",\n \"rationale\": \"A scheduled model retirement requires a tested migration path. It is a lifecycle event rather than a statistical drift score.\",\n \"response\": \"Verify the provider notice and rehearse the replacement against the current eval set.\",\n \"rollback\": \"Use a supported pre-evaluated fallback if the snapshot becomes unavailable. Do not roll back to a retired model. Check daily and on notices. No sample gate.\",\n \"sources\": [\n \"S1\"\n ],\n \"thresholdBasis\": \"Illustrative setting. Calibrate against local baseline and error budget.\",\n \"sourceReferences\": [\n {\n \"id\": \"S1\",\n \"url\": \"https://pharosproduction.com/insights/engineering/state-of-production-ai-engineering-2026/\",\n \"checked\": \"2026-09-08\"\n }\n ]\n },\n {\n \"id\": \"tool_policy\",\n \"name\": \"Unauthorized tool effects\",\n \"systems\": [\n \"agent\"\n ],\n \"sampleClass\": \"event\",\n \"threshold\": 0,\n \"min\": 0,\n \"max\": 0,\n \"unit\": \"effects\",\n \"comparator\": \"gt\",\n \"definition\": \"Count of observed tool side effects outside the allowed action, resource or approval scope. Log denied attempts separately. This signal concerns effects that occurred.\",\n \"rationale\": \"A single confirmed unauthorized effect requires incident handling regardless of traffic.\",\n \"response\": \"Disable the affected write capability, preserve evidence and assess compensation for completed effects.\",\n \"rollback\": \"Switch to a read-only or human-mediated path. Deployment rollback does not undo payments, messages or other external effects. Bypass sample and persistence gates.\",\n \"sources\": [\n \"S1\"\n ],\n \"thresholdBasis\": \"Illustrative setting. Calibrate against local baseline and error budget.\",\n \"sourceReferences\": [\n {\n \"id\": \"S1\",\n \"url\": \"https://pharosproduction.com/insights/engineering/state-of-production-ai-engineering-2026/\",\n \"checked\": \"2026-09-08\"\n }\n ]\n }\n]\n", "sha256": "sha256:30588884b6d39f24b3dbdd929b221d3bd27d936d2eb6a334c3039ded119dcf6a", "url": "https://github.com/PharosProduction/production-ai-drift-planner/blob/f2938cfb84bbe09c5eab4f95dd404f1ca67c39ae/docs/data/metrics.json" }, "docs/data/sources.json": { "content": "[\n {\n \"id\": \"S1\",\n \"title\": \"Production AI engineering: drift and retraining\",\n \"url\": \"https://pharosproduction.com/insights/engineering/state-of-production-ai-engineering-2026/\",\n \"scope\": \"Seed taxonomy and published instrumentation method only. Industry percentages and superlatives excluded.\",\n \"checked\": \"2026-09-08\"\n },\n {\n \"id\": \"S2\",\n \"title\": \"AWS: detecting drift in production applications\",\n \"url\": \"https://docs.aws.amazon.com/prescriptive-guidance/latest/gen-ai-lifecycle-operational-excellence/prod-monitoring-drift.html\",\n \"scope\": \"Data/concept distinction, reference samples and investigation after a drift signal.\",\n \"checked\": \"2026-09-08\"\n },\n {\n \"id\": \"S3\",\n \"title\": \"Evidently: data drift\",\n \"url\": \"https://docs.evidentlyai.com/metrics/explainer_drift\",\n \"scope\": \"Reference/current datasets, method dependence and separate null-rate checks.\",\n \"checked\": \"2026-09-08\"\n },\n {\n \"id\": \"S4\",\n \"title\": \"Arize: drift tracing\",\n \"url\": \"https://arize.com/docs/ax/machine-learning/machine-learning/how-to-ml/drift-tracing\",\n \"scope\": \"Reference selection, comparison of production periods and delayed ground-truth labels.\",\n \"checked\": \"2026-09-08\"\n },\n {\n \"id\": \"S5\",\n \"title\": \"OpenTelemetry GenAI conventions\",\n \"url\": \"https://github.com/open-telemetry/semantic-conventions-genai/tree/b5d8440f6f126738fd50f927752cd669772c517b/docs/gen-ai\",\n \"scope\": \"Current documentation repository, pinned at source check. Individual conventions have their own stability status.\",\n \"checked\": \"2026-09-08\"\n },\n {\n \"id\": \"S6\",\n \"title\": \"Evidently: monitoring overview\",\n \"url\": \"https://docs.evidentlyai.com/docs/platform/monitoring_overview\",\n \"scope\": \"Batch evaluation and monitoring pipelines.\",\n \"checked\": \"2026-09-08\"\n },\n {\n \"id\": \"S7\",\n \"title\": \"Pharos company homepage\",\n \"url\": \"https://pharosproduction.com\",\n \"scope\": \"Publisher identity and canonical company destination.\",\n \"checked\": \"2026-09-08\"\n }\n]\n", "sha256": "sha256:ac3ecccba1932853c615fb513846bef8ad1078357e7a0d249dfd93acbc3c04d7", "url": "https://github.com/PharosProduction/production-ai-drift-planner/blob/f2938cfb84bbe09c5eab4f95dd404f1ca67c39ae/docs/data/sources.json" } }, "independent_human_review": "NOT_REVIEWED", "pages": "https://pharosproduction.github.io/production-ai-drift-planner/", "repo": "https://github.com/PharosProduction/production-ai-drift-planner", "snapshot_at": "2026-09-17T06:17:24Z" }, "governance": { "commit": "2691dacd9318e15aa97a6bb6cb2a89327f773228", "files": { "LICENSE": { "content": "MIT License\n\nCopyright (c) 2026 Pharos Production\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n", "sha256": "sha256:48463cc27a86bdc41249f2e93168615728c7860d350b4dd51c7606e5c512555e", "url": "https://github.com/PharosProduction/ai-governance-readiness-map/blob/2691dacd9318e15aa97a6bb6cb2a89327f773228/LICENSE" }, "README.md": { "content": "# AI Governance Readiness Map\n\nA static evidence worksheet for one AI system. Twelve controls connect RACI roles, artifact references and review records. The result lists missing work and exports all controls as JSON, Markdown or CSV.\n\nThis is original engineering planning guidance. It is not legal advice, a compliance assessment or an official NIST checklist. Framework references identify topics for review. Entered evidence is not fetched or verified.\n\n## Run locally\n\nRequires Node.js 22 or later and Python 3 for the optional preview server. No npm dependencies.\n\n```sh\nnpm run build\nnpm run verify\npython3 -m http.server 4189 --bind 127.0.0.1 --directory docs\n```\n\nOpen http://127.0.0.1:4189/. Browser QA is optional and uses an externally supplied Playwright module and Chrome executable:\n\n```sh\nPLAYWRIGHT_MODULE=/absolute/path/to/playwright/index.mjs CHROME_PATH=/absolute/path/to/chrome node scripts/browser-qa.mjs\n```\n\nSet GOV_BASE_URL to test another served URL. The QA script writes screenshots, rendered HTML and compact results under audits/. It uses a separate headless browser.\n\n## Source and build\n\n- docs/data/controls.json is the authoritative control catalog.\n- docs/data/sources.json records public source scope and check dates.\n- scripts/index.template.html supplies page copy and layout.\n- scripts/build.mjs produces docs/index.html and the synthetic example exports.\n- scripts/example.mjs defines the clearly labeled fictional scenario.\n- docs/assets/readiness.mjs validates records and computes completeness, with ten behavioral tests.\n- docs/assets/app.mjs edits the in-memory draft; function filters only change visibility.\n- CONTRACT.md defines schema, statuses and export behavior.\n\nThe page never stores user input or sends it to a server. Reloading loses edits. JSON preserves exact input, Markdown escapes it and CSV defuses formula cells. Export before closing.\n\n## Release state\n\nPublished site: https://pharosproduction.github.io/ai-governance-readiness-map/\n\nPushes to main run the ten behavioral tests and release checks, then deploy the verified docs directory through GitHub Actions.\n\nOriginal code and control wording use the MIT license. External sources retain their terms. Public source scope and check dates are recorded in docs/data/sources.json. Internal review records remain in the production project archive.\n", "sha256": "sha256:850cba6f23785c775d639bfc4a8eb29dfeff2cd0ae80544b9d2fe241f40865d9", "url": "https://github.com/PharosProduction/ai-governance-readiness-map/blob/2691dacd9318e15aa97a6bb6cb2a89327f773228/README.md" }, "docs/data/controls.json": { "content": "[\n {\n \"id\": \"inventory\",\n \"title\": \"AI system inventory\",\n \"function\": \"MAP\",\n \"purpose\": \"Give the system a stable identity before assessing it. Include models, retrieval data, prompts, tools and the environments where people use them.\",\n \"evidence\": \"Versioned register entry with intended purpose, model/provider identifiers, data sources, deployment location and review triggers.\",\n \"suggestedResponsible\": \"Product owner\",\n \"suggestedAccountable\": \"System owner\",\n \"euReview\": \"Confirm system scope and the organization's role.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Map\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/map/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ]\n },\n {\n \"id\": \"scope\",\n \"title\": \"Jurisdiction and use-case review\",\n \"function\": \"MAP\",\n \"purpose\": \"Record who is affected, where outputs are used and which decisions the system can influence. Assign an explicit reviewer to the scope decision.\",\n \"evidence\": \"Dated scoping memo with intended use, affected groups, deployment geography, legal questions and unresolved assumptions.\",\n \"suggestedResponsible\": \"Legal/compliance lead\",\n \"suggestedAccountable\": \"Business owner\",\n \"euReview\": \"Check territorial scope, classification and applicable transition rules.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Map\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/map/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ]\n },\n {\n \"id\": \"risk\",\n \"title\": \"Risk register and acceptance\",\n \"function\": \"MAP\",\n \"purpose\": \"Describe a plausible failure and its consequence for each affected group. Keep open risks visible with the acceptance authority and a review trigger.\",\n \"evidence\": \"Risk register containing failure scenarios, impact analysis, mitigation owners, residual risk decisions and change triggers.\",\n \"suggestedResponsible\": \"Risk analyst\",\n \"suggestedAccountable\": \"Risk owner\",\n \"euReview\": \"Review prohibited practices and any high-risk classification.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Map\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/map/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ]\n },\n {\n \"id\": \"accountability\",\n \"title\": \"Accountability and decision authority\",\n \"function\": \"GOVERN\",\n \"purpose\": \"Name the person who performs the control and the person who accepts its outcome. Record consulted specialists and the people who receive the decision.\",\n \"evidence\": \"RACI record plus a release decision log that names risk acceptance authority, escalation route and unresolved objections.\",\n \"suggestedResponsible\": \"Governance lead\",\n \"suggestedAccountable\": \"Executive sponsor\",\n \"euReview\": \"Confirm provider/deployer responsibilities for the particular system.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Govern\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/govern/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ]\n },\n {\n \"id\": \"literacy\",\n \"title\": \"Role-specific training\",\n \"function\": \"GOVERN\",\n \"purpose\": \"Make training fit the decisions each role actually makes. Operators should rehearse escalation and understand where the system can fail.\",\n \"evidence\": \"Training record with role, relevant system limitations, completed exercises, attendance and a refresher trigger.\",\n \"suggestedResponsible\": \"Training lead\",\n \"suggestedAccountable\": \"System owner\",\n \"euReview\": \"Review AI literacy obligations and operator competence.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Govern\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/govern/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ]\n },\n {\n \"id\": \"supplier\",\n \"title\": \"Supplier and model dependencies\",\n \"function\": \"GOVERN\",\n \"purpose\": \"Record which externally supplied components can change behavior. Include access, version changes and the evidence a provider makes available.\",\n \"evidence\": \"Dependency register, supplier instructions, version policy, data-use terms, exit plan and a named owner for vendor changes.\",\n \"suggestedResponsible\": \"Vendor manager\",\n \"suggestedAccountable\": \"System owner\",\n \"euReview\": \"Check upstream model information and downstream responsibilities.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Govern\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/govern/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ]\n },\n {\n \"id\": \"data\",\n \"title\": \"Data provenance and access\",\n \"function\": \"MAP\",\n \"purpose\": \"Trace evaluation and retrieval data to a permitted source. Record sensitive fields, access rules and deletion or correction paths.\",\n \"evidence\": \"Data lineage record with allowed uses, quality checks, retention decision, access test and affected population coverage.\",\n \"suggestedResponsible\": \"Data lead\",\n \"suggestedAccountable\": \"Data owner\",\n \"euReview\": \"Review data governance and separate privacy obligations.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Map\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/map/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ]\n },\n {\n \"id\": \"validation\",\n \"title\": \"Validation and failure testing\",\n \"function\": \"MEASURE\",\n \"purpose\": \"Test the intended use and credible misuse before release. Record subgroup limitations, security failures and the reason for each acceptance threshold.\",\n \"evidence\": \"Versioned evaluation set, rubric, measured results, security tests, known limitations and an accepted release decision.\",\n \"suggestedResponsible\": \"Evaluation lead\",\n \"suggestedAccountable\": \"Release owner\",\n \"euReview\": \"Review accuracy, robustness, cybersecurity and supporting documentation.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Measure\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/measure/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ]\n },\n {\n \"id\": \"oversight\",\n \"title\": \"Human oversight and user information\",\n \"function\": \"MEASURE\",\n \"purpose\": \"Test whether an operator can understand a warning, override an action and stop the workflow. Check what users are told about AI involvement.\",\n \"evidence\": \"Observed override exercise, authority boundaries, user instructions, escalation path and accessible notices for the intended audience.\",\n \"suggestedResponsible\": \"Product/security lead\",\n \"suggestedAccountable\": \"System owner\",\n \"euReview\": \"Review oversight measures and relevant transparency duties.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Measure\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/measure/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ]\n },\n {\n \"id\": \"records\",\n \"title\": \"Traceability and change records\",\n \"function\": \"GOVERN\",\n \"purpose\": \"Keep enough context to reconstruct a decision without indiscriminately retaining sensitive payloads. Tie approvals and changes to an identifiable release.\",\n \"evidence\": \"Redacted trace sample, version/approval identifiers, retention/access rules and a tested path for retrieving a decision record.\",\n \"suggestedResponsible\": \"Platform lead\",\n \"suggestedAccountable\": \"System owner\",\n \"euReview\": \"Review logging and technical documentation requirements.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Govern\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/govern/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ]\n },\n {\n \"id\": \"monitoring\",\n \"title\": \"Monitoring and response ownership\",\n \"function\": \"MANAGE\",\n \"purpose\": \"Connect a measured signal to a response owner. Review thresholds against an evaluation baseline and test the recovery path after a breach.\",\n \"evidence\": \"Monitoring specification, baseline reference, alert routing, response exercise, rollback target and post-change review record.\",\n \"suggestedResponsible\": \"Operations lead\",\n \"suggestedAccountable\": \"Service owner\",\n \"euReview\": \"Review post-market monitoring and deployer monitoring duties.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Manage\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/manage/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ]\n },\n {\n \"id\": \"incident\",\n \"title\": \"Incident triage and notification\",\n \"function\": \"MANAGE\",\n \"purpose\": \"Rehearse how an issue becomes an incident, who contains it and who decides whether notification is required. Preserve the incident timeline.\",\n \"evidence\": \"Incident playbook and exercise record with severity criteria, containment authority, notification review, recipients and applicable clocks.\",\n \"suggestedResponsible\": \"Incident commander\",\n \"suggestedAccountable\": \"Accountable incident owner\",\n \"euReview\": \"Confirm reportability, recipient and deadline for the specific regime.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Manage\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/manage/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ]\n }\n]\n", "sha256": "sha256:d9f23d92f28c6f59b3db325b2a95257c247847c23d774baf7d154608176fcff6", "url": "https://github.com/PharosProduction/ai-governance-readiness-map/blob/2691dacd9318e15aa97a6bb6cb2a89327f773228/docs/data/controls.json" }, "docs/data/example-draft.json": { "content": "{\n \"systemName\": \"SYNTHETIC EXAMPLE: internal support copilot\",\n \"intendedUse\": \"Suggest replies for internal support staff; staff approve every response. Illustrative scenario only.\",\n \"assessedOn\": \"2026-09-11\",\n \"jurisdiction\": \"eu-review\",\n \"euRole\": \"unconfirmed\",\n \"rows\": {\n \"inventory\": {\n \"status\": \"review-recorded\",\n \"responsible\": \"Example product lead\",\n \"accountable\": \"Example system owner\",\n \"consulted\": \"Example governance specialist\",\n \"informed\": \"Example support team\",\n \"evidenceRef\": \"SYNTHETIC inventory memo v1\",\n \"reviewer\": \"Example reviewer\",\n \"reviewedOn\": \"2026-09-10\",\n \"rationale\": \"Illustrative record, not an observed assessment.\"\n },\n \"scope\": {\n \"status\": \"unknown\",\n \"responsible\": \"\",\n \"accountable\": \"\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"\",\n \"reviewer\": \"\",\n \"reviewedOn\": \"\",\n \"rationale\": \"\"\n },\n \"risk\": {\n \"status\": \"unknown\",\n \"responsible\": \"\",\n \"accountable\": \"\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"\",\n \"reviewer\": \"\",\n \"reviewedOn\": \"\",\n \"rationale\": \"\"\n },\n \"accountability\": {\n \"status\": \"review-recorded\",\n \"responsible\": \"Example product lead\",\n \"accountable\": \"Example system owner\",\n \"consulted\": \"Example governance specialist\",\n \"informed\": \"Example support team\",\n \"evidenceRef\": \"SYNTHETIC accountability memo v1\",\n \"reviewer\": \"Example reviewer\",\n \"reviewedOn\": \"2026-09-10\",\n \"rationale\": \"Illustrative record, not an observed assessment.\"\n },\n \"literacy\": {\n \"status\": \"unknown\",\n \"responsible\": \"\",\n \"accountable\": \"\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"\",\n \"reviewer\": \"\",\n \"reviewedOn\": \"\",\n \"rationale\": \"\"\n },\n \"supplier\": {\n \"status\": \"not-applicable\",\n \"responsible\": \"\",\n \"accountable\": \"Example system owner\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"\",\n \"reviewer\": \"Example reviewer\",\n \"reviewedOn\": \"2026-09-10\",\n \"rationale\": \"SYNTHETIC SCOPE: model built and hosted entirely in-house, with no external model service. External supplier review excluded; internal dependency and provenance controls remain under inventory and data.\"\n },\n \"data\": {\n \"status\": \"unknown\",\n \"responsible\": \"\",\n \"accountable\": \"\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"\",\n \"reviewer\": \"\",\n \"reviewedOn\": \"\",\n \"rationale\": \"\"\n },\n \"validation\": {\n \"status\": \"evidence-recorded\",\n \"responsible\": \"Example evaluation lead\",\n \"accountable\": \"Example release owner\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"SYNTHETIC evaluation memo v1; review pending.\",\n \"reviewer\": \"\",\n \"reviewedOn\": \"\",\n \"rationale\": \"\"\n },\n \"oversight\": {\n \"status\": \"unknown\",\n \"responsible\": \"\",\n \"accountable\": \"\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"\",\n \"reviewer\": \"\",\n \"reviewedOn\": \"\",\n \"rationale\": \"\"\n },\n \"records\": {\n \"status\": \"unknown\",\n \"responsible\": \"\",\n \"accountable\": \"\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"\",\n \"reviewer\": \"\",\n \"reviewedOn\": \"\",\n \"rationale\": \"\"\n },\n \"monitoring\": {\n \"status\": \"unknown\",\n \"responsible\": \"\",\n \"accountable\": \"\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"\",\n \"reviewer\": \"\",\n \"reviewedOn\": \"\",\n \"rationale\": \"\"\n },\n \"incident\": {\n \"status\": \"planned\",\n \"responsible\": \"\",\n \"accountable\": \"\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"SYNTHETIC draft incident playbook; owners unassigned.\",\n \"reviewer\": \"\",\n \"reviewedOn\": \"\",\n \"rationale\": \"\"\n }\n }\n}\n", "sha256": "sha256:3c841ea48168e8557529fc001dfdd84e9bf8a209c220450e1252ec6e0074210f", "url": "https://github.com/PharosProduction/ai-governance-readiness-map/blob/2691dacd9318e15aa97a6bb6cb2a89327f773228/docs/data/example-draft.json" }, "docs/data/example-report.json": { "content": "{\n \"version\": \"1.0.0\",\n \"status\": \"DRAFT_REQUIRES_REVIEW\",\n \"scope\": {\n \"systemName\": \"SYNTHETIC EXAMPLE: internal support copilot\",\n \"intendedUse\": \"Suggest replies for internal support staff; staff approve every response. Illustrative scenario only.\",\n \"assessedOn\": \"2026-09-11\",\n \"jurisdiction\": \"eu-review\",\n \"euRole\": \"unconfirmed\"\n },\n \"summary\": {\n \"total\": 12,\n \"reviewed\": 2,\n \"excluded\": 1,\n \"needsAttention\": 9\n },\n \"scopeWarnings\": [\n \"Confirm the EU role for review.\"\n ],\n \"controls\": [\n {\n \"id\": \"inventory\",\n \"title\": \"AI system inventory\",\n \"function\": \"MAP\",\n \"purpose\": \"Give the system a stable identity before assessing it. Include models, retrieval data, prompts, tools and the environments where people use them.\",\n \"evidence\": \"Versioned register entry with intended purpose, model/provider identifiers, data sources, deployment location and review triggers.\",\n \"suggestedResponsible\": \"Product owner\",\n \"suggestedAccountable\": \"System owner\",\n \"euReview\": \"Confirm system scope and the organization's role.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Map\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/map/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ],\n \"status\": \"review-recorded\",\n \"responsible\": \"Example product lead\",\n \"accountable\": \"Example system owner\",\n \"consulted\": \"Example governance specialist\",\n \"informed\": \"Example support team\",\n \"evidenceRef\": \"SYNTHETIC inventory memo v1\",\n \"reviewer\": \"Example reviewer\",\n \"reviewedOn\": \"2026-09-10\",\n \"rationale\": \"Illustrative record, not an observed assessment.\",\n \"missing\": [],\n \"nextAction\": \"Documented review recorded; revisit when the system or evidence changes.\"\n },\n {\n \"id\": \"scope\",\n \"title\": \"Jurisdiction and use-case review\",\n \"function\": \"MAP\",\n \"purpose\": \"Record who is affected, where outputs are used and which decisions the system can influence. Assign an explicit reviewer to the scope decision.\",\n \"evidence\": \"Dated scoping memo with intended use, affected groups, deployment geography, legal questions and unresolved assumptions.\",\n \"suggestedResponsible\": \"Legal/compliance lead\",\n \"suggestedAccountable\": \"Business owner\",\n \"euReview\": \"Check territorial scope, classification and applicable transition rules.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Map\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/map/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ],\n \"status\": \"unknown\",\n \"responsible\": \"\",\n \"accountable\": \"\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"\",\n \"reviewer\": \"\",\n \"reviewedOn\": \"\",\n \"rationale\": \"\",\n \"missing\": [\n \"responsible\",\n \"accountable\"\n ],\n \"nextAction\": \"Collect evidence or complete review: provide responsible, accountable.\"\n },\n {\n \"id\": \"risk\",\n \"title\": \"Risk register and acceptance\",\n \"function\": \"MAP\",\n \"purpose\": \"Describe a plausible failure and its consequence for each affected group. Keep open risks visible with the acceptance authority and a review trigger.\",\n \"evidence\": \"Risk register containing failure scenarios, impact analysis, mitigation owners, residual risk decisions and change triggers.\",\n \"suggestedResponsible\": \"Risk analyst\",\n \"suggestedAccountable\": \"Risk owner\",\n \"euReview\": \"Review prohibited practices and any high-risk classification.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Map\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/map/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ],\n \"status\": \"unknown\",\n \"responsible\": \"\",\n \"accountable\": \"\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"\",\n \"reviewer\": \"\",\n \"reviewedOn\": \"\",\n \"rationale\": \"\",\n \"missing\": [\n \"responsible\",\n \"accountable\"\n ],\n \"nextAction\": \"Collect evidence or complete review: provide responsible, accountable.\"\n },\n {\n \"id\": \"accountability\",\n \"title\": \"Accountability and decision authority\",\n \"function\": \"GOVERN\",\n \"purpose\": \"Name the person who performs the control and the person who accepts its outcome. Record consulted specialists and the people who receive the decision.\",\n \"evidence\": \"RACI record plus a release decision log that names risk acceptance authority, escalation route and unresolved objections.\",\n \"suggestedResponsible\": \"Governance lead\",\n \"suggestedAccountable\": \"Executive sponsor\",\n \"euReview\": \"Confirm provider/deployer responsibilities for the particular system.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Govern\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/govern/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ],\n \"status\": \"review-recorded\",\n \"responsible\": \"Example product lead\",\n \"accountable\": \"Example system owner\",\n \"consulted\": \"Example governance specialist\",\n \"informed\": \"Example support team\",\n \"evidenceRef\": \"SYNTHETIC accountability memo v1\",\n \"reviewer\": \"Example reviewer\",\n \"reviewedOn\": \"2026-09-10\",\n \"rationale\": \"Illustrative record, not an observed assessment.\",\n \"missing\": [],\n \"nextAction\": \"Documented review recorded; revisit when the system or evidence changes.\"\n },\n {\n \"id\": \"literacy\",\n \"title\": \"Role-specific training\",\n \"function\": \"GOVERN\",\n \"purpose\": \"Make training fit the decisions each role actually makes. Operators should rehearse escalation and understand where the system can fail.\",\n \"evidence\": \"Training record with role, relevant system limitations, completed exercises, attendance and a refresher trigger.\",\n \"suggestedResponsible\": \"Training lead\",\n \"suggestedAccountable\": \"System owner\",\n \"euReview\": \"Review AI literacy obligations and operator competence.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Govern\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/govern/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ],\n \"status\": \"unknown\",\n \"responsible\": \"\",\n \"accountable\": \"\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"\",\n \"reviewer\": \"\",\n \"reviewedOn\": \"\",\n \"rationale\": \"\",\n \"missing\": [\n \"responsible\",\n \"accountable\"\n ],\n \"nextAction\": \"Collect evidence or complete review: provide responsible, accountable.\"\n },\n {\n \"id\": \"supplier\",\n \"title\": \"Supplier and model dependencies\",\n \"function\": \"GOVERN\",\n \"purpose\": \"Record which externally supplied components can change behavior. Include access, version changes and the evidence a provider makes available.\",\n \"evidence\": \"Dependency register, supplier instructions, version policy, data-use terms, exit plan and a named owner for vendor changes.\",\n \"suggestedResponsible\": \"Vendor manager\",\n \"suggestedAccountable\": \"System owner\",\n \"euReview\": \"Check upstream model information and downstream responsibilities.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Govern\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/govern/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ],\n \"status\": \"not-applicable\",\n \"responsible\": \"\",\n \"accountable\": \"Example system owner\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"\",\n \"reviewer\": \"Example reviewer\",\n \"reviewedOn\": \"2026-09-10\",\n \"rationale\": \"SYNTHETIC SCOPE: model built and hosted entirely in-house, with no external model service. External supplier review excluded; internal dependency and provenance controls remain under inventory and data.\",\n \"missing\": [],\n \"nextAction\": \"Exclusion rationale recorded; revisit applicability when the scope changes.\"\n },\n {\n \"id\": \"data\",\n \"title\": \"Data provenance and access\",\n \"function\": \"MAP\",\n \"purpose\": \"Trace evaluation and retrieval data to a permitted source. Record sensitive fields, access rules and deletion or correction paths.\",\n \"evidence\": \"Data lineage record with allowed uses, quality checks, retention decision, access test and affected population coverage.\",\n \"suggestedResponsible\": \"Data lead\",\n \"suggestedAccountable\": \"Data owner\",\n \"euReview\": \"Review data governance and separate privacy obligations.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Map\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/map/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ],\n \"status\": \"unknown\",\n \"responsible\": \"\",\n \"accountable\": \"\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"\",\n \"reviewer\": \"\",\n \"reviewedOn\": \"\",\n \"rationale\": \"\",\n \"missing\": [\n \"responsible\",\n \"accountable\"\n ],\n \"nextAction\": \"Collect evidence or complete review: provide responsible, accountable.\"\n },\n {\n \"id\": \"validation\",\n \"title\": \"Validation and failure testing\",\n \"function\": \"MEASURE\",\n \"purpose\": \"Test the intended use and credible misuse before release. Record subgroup limitations, security failures and the reason for each acceptance threshold.\",\n \"evidence\": \"Versioned evaluation set, rubric, measured results, security tests, known limitations and an accepted release decision.\",\n \"suggestedResponsible\": \"Evaluation lead\",\n \"suggestedAccountable\": \"Release owner\",\n \"euReview\": \"Review accuracy, robustness, cybersecurity and supporting documentation.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Measure\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/measure/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ],\n \"status\": \"evidence-recorded\",\n \"responsible\": \"Example evaluation lead\",\n \"accountable\": \"Example release owner\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"SYNTHETIC evaluation memo v1; review pending.\",\n \"reviewer\": \"\",\n \"reviewedOn\": \"\",\n \"rationale\": \"\",\n \"missing\": [],\n \"nextAction\": \"Complete review.\"\n },\n {\n \"id\": \"oversight\",\n \"title\": \"Human oversight and user information\",\n \"function\": \"MEASURE\",\n \"purpose\": \"Test whether an operator can understand a warning, override an action and stop the workflow. Check what users are told about AI involvement.\",\n \"evidence\": \"Observed override exercise, authority boundaries, user instructions, escalation path and accessible notices for the intended audience.\",\n \"suggestedResponsible\": \"Product/security lead\",\n \"suggestedAccountable\": \"System owner\",\n \"euReview\": \"Review oversight measures and relevant transparency duties.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Measure\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/measure/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ],\n \"status\": \"unknown\",\n \"responsible\": \"\",\n \"accountable\": \"\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"\",\n \"reviewer\": \"\",\n \"reviewedOn\": \"\",\n \"rationale\": \"\",\n \"missing\": [\n \"responsible\",\n \"accountable\"\n ],\n \"nextAction\": \"Collect evidence or complete review: provide responsible, accountable.\"\n },\n {\n \"id\": \"records\",\n \"title\": \"Traceability and change records\",\n \"function\": \"GOVERN\",\n \"purpose\": \"Keep enough context to reconstruct a decision without indiscriminately retaining sensitive payloads. Tie approvals and changes to an identifiable release.\",\n \"evidence\": \"Redacted trace sample, version/approval identifiers, retention/access rules and a tested path for retrieving a decision record.\",\n \"suggestedResponsible\": \"Platform lead\",\n \"suggestedAccountable\": \"System owner\",\n \"euReview\": \"Review logging and technical documentation requirements.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Govern\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/govern/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ],\n \"status\": \"unknown\",\n \"responsible\": \"\",\n \"accountable\": \"\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"\",\n \"reviewer\": \"\",\n \"reviewedOn\": \"\",\n \"rationale\": \"\",\n \"missing\": [\n \"responsible\",\n \"accountable\"\n ],\n \"nextAction\": \"Collect evidence or complete review: provide responsible, accountable.\"\n },\n {\n \"id\": \"monitoring\",\n \"title\": \"Monitoring and response ownership\",\n \"function\": \"MANAGE\",\n \"purpose\": \"Connect a measured signal to a response owner. Review thresholds against an evaluation baseline and test the recovery path after a breach.\",\n \"evidence\": \"Monitoring specification, baseline reference, alert routing, response exercise, rollback target and post-change review record.\",\n \"suggestedResponsible\": \"Operations lead\",\n \"suggestedAccountable\": \"Service owner\",\n \"euReview\": \"Review post-market monitoring and deployer monitoring duties.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Manage\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/manage/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ],\n \"status\": \"unknown\",\n \"responsible\": \"\",\n \"accountable\": \"\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"\",\n \"reviewer\": \"\",\n \"reviewedOn\": \"\",\n \"rationale\": \"\",\n \"missing\": [\n \"responsible\",\n \"accountable\"\n ],\n \"nextAction\": \"Collect evidence or complete review: provide responsible, accountable.\"\n },\n {\n \"id\": \"incident\",\n \"title\": \"Incident triage and notification\",\n \"function\": \"MANAGE\",\n \"purpose\": \"Rehearse how an issue becomes an incident, who contains it and who decides whether notification is required. Preserve the incident timeline.\",\n \"evidence\": \"Incident playbook and exercise record with severity criteria, containment authority, notification review, recipients and applicable clocks.\",\n \"suggestedResponsible\": \"Incident commander\",\n \"suggestedAccountable\": \"Accountable incident owner\",\n \"euReview\": \"Confirm reportability, recipient and deadline for the specific regime.\",\n \"sourceReferences\": [\n {\n \"label\": \"NIST AI RMF Playbook: Manage\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/manage/\"\n },\n {\n \"label\": \"European Commission: AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\"\n }\n ],\n \"status\": \"planned\",\n \"responsible\": \"\",\n \"accountable\": \"\",\n \"consulted\": \"\",\n \"informed\": \"\",\n \"evidenceRef\": \"SYNTHETIC draft incident playbook; owners unassigned.\",\n \"reviewer\": \"\",\n \"reviewedOn\": \"\",\n \"rationale\": \"\",\n \"missing\": [\n \"responsible\",\n \"accountable\"\n ],\n \"nextAction\": \"Collect evidence or complete review: provide responsible, accountable.\"\n }\n ]\n}\n", "sha256": "sha256:82407e46db7cbdb2912f7b558bfee13b3dd4679430550e8dd2ee6762c3909820", "url": "https://github.com/PharosProduction/ai-governance-readiness-map/blob/2691dacd9318e15aa97a6bb6cb2a89327f773228/docs/data/example-report.json" }, "docs/data/sources.json": { "content": "[\n {\n \"id\": \"nist\",\n \"title\": \"NIST AI Risk Management Framework\",\n \"url\": \"https://www.nist.gov/itl/ai-risk-management-framework\",\n \"checkedOn\": \"2026-09-11\",\n \"supports\": \"Voluntary framework; AI RMF 1.0 remains the reference while revision is underway.\"\n },\n {\n \"id\": \"playbook\",\n \"title\": \"NIST AI RMF Playbook\",\n \"url\": \"https://airc.nist.gov/airmf-resources/playbook/\",\n \"checkedOn\": \"2026-09-11\",\n \"supports\": \"Suggested actions organized under Govern, Map, Measure and Manage; organizations tailor their use.\"\n },\n {\n \"id\": \"commission\",\n \"title\": \"European Commission AI Act overview\",\n \"url\": \"https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai\",\n \"checkedOn\": \"2026-09-11\",\n \"supports\": \"Scope-sensitive duties and differentiated application dates; review actual role and use case.\"\n },\n {\n \"id\": \"pharos-method\",\n \"title\": \"AI Governance Framework: Building Responsible AI Systems\",\n \"url\": \"https://pharosproduction.com/insights/engineering/ai-governance-framework/\",\n \"checkedOn\": \"2026-09-11\",\n \"supports\": \"Engineering structure only: inventory, validation, monitoring and accountability. No outcome statistics or blanket reporting deadline reused.\"\n }\n]\n", "sha256": "sha256:b576c6ef4e4bccab80902f7f54fa16cc89ba13fa5ce0856a8fa8dc89987238ee", "url": "https://github.com/PharosProduction/ai-governance-readiness-map/blob/2691dacd9318e15aa97a6bb6cb2a89327f773228/docs/data/sources.json" } }, "independent_human_review": "NOT_REVIEWED", "pages": "https://pharosproduction.github.io/ai-governance-readiness-map/", "repo": "https://github.com/PharosProduction/ai-governance-readiness-map", "snapshot_at": "2026-09-17T06:17:24Z" }, "security": { "commit": "a35b9c30594162eadf4d790d33bd18aaa785bcee", "files": { "DATA-LICENSE.md": { "content": "# Data license and source boundary\n\nThe original selection, arrangement and Pharos-authored descriptions in `data/` and `docs/data/` are licensed under the Creative Commons Attribution 4.0 International license (CC BY 4.0).\n\nExternal standards, specifications, incident reports and articles referenced by URL remain the property of their respective authors and publishers. This repository does not relicense those works. Source identifiers and bounded paraphrases are included for traceability; consult the linked source for its authoritative wording, current version and license.\n\nThe control map is an editorial implementation aid. It does not assert OWASP, NIST, MCP, SLSA or publisher endorsement, certification or conformance.\n", "sha256": "sha256:6b212994cef19ff7e5e61c1a02947726e28cf0958ae4dc222c493e1e45752430", "url": "https://github.com/PharosProduction/agent-security-control-mapper/blob/a35b9c30594162eadf4d790d33bd18aaa785bcee/DATA-LICENSE.md" }, "LICENSE": { "content": "MIT License\n\nCopyright (c) 2026 Pharos Production\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n", "sha256": "sha256:48463cc27a86bdc41249f2e93168615728c7860d350b4dd51c7606e5c512555e", "url": "https://github.com/PharosProduction/agent-security-control-mapper/blob/a35b9c30594162eadf4d790d33bd18aaa785bcee/LICENSE" }, "README.md": { "content": "# AI Agent Security Control Mapper\n\nA dependency-free GitHub Pages field tool that maps declared AI-agent capabilities to a deterministic union of security controls. Each selected control links the triggering threat to an implementation action, an observable verification, an evidence artifact, an accountable owner role, and bounded primary sources.\n\nThe mapper is decision support, not certification. It produces no security, confidence, maturity, or percentage score. An `unknown` answer remains visible and makes the result provisional.\n\n## Local verification\n\nNode.js 22 or newer is required. No package installation is needed.\n\n```sh\nnpm run verify\n```\n\nOptional transport-only source probes are bounded to 12 URLs by default and never evaluate or rewrite claims:\n\n```sh\nnpm run check:sources\nSOURCE_CHECK_LIMIT=6 npm run check:sources -- --strict\n```\n\nServe `docs/` with any static HTTP server to use the browser interface. Opening `index.html` directly is unsupported because browsers restrict local JSON requests.\n\n## Data contract\n\n- `data/` is the tested source of truth.\n- `docs/data/` is a byte-identical public mirror.\n- `control-matrix.csv` is a normalized, machine-readable view of the mapping graph.\n- Stable IDs preserve traceability across JSON, CSV, Markdown exports, claims and sources.\n- The browser keeps no local storage, query-string state, analytics or external runtime dependency.\n\n## Release boundary\n\nThe test workflow verifies every push and pull request. Deployment is a separate manual GitHub Actions workflow that publishes only the verified `docs/` artifact to GitHub Pages. All third-party Actions are pinned to full commit SHAs.\n\nSee `docs/methodology.html` for the union algorithm, evidence model, source boundaries and limitations.\n", "sha256": "sha256:20b75c7c2b28bfa4de3b0f4fd30769fa8f075515297abee1327386a64c88ab30", "url": "https://github.com/PharosProduction/agent-security-control-mapper/blob/a35b9c30594162eadf4d790d33bd18aaa785bcee/README.md" }, "data/claims.json": { "content": "{\n \"version\": \"1.0.0\",\n \"claims\": [\n { \"id\": \"C01\", \"statement\": \"Filtering or prompt-level detection alone does not establish a complete boundary against indirect instructions in untrusted content.\", \"sourceIds\": [\"S01\", \"S02\", \"S09\"], \"boundary\": \"Supports defense in depth; does not claim that any listed control is universally effective.\" },\n { \"id\": \"C02\", \"statement\": \"External content, tool output, persistent memory and peer-agent messages should be treated as separate trust domains rather than as authoritative instructions.\", \"sourceIds\": [\"S01\", \"S03\", \"S04\"], \"boundary\": \"A design principle that still requires system-specific data-flow analysis.\" },\n { \"id\": \"C03\", \"statement\": \"Tool authorization should be enforced deterministically outside the model for the exact operation and parameters.\", \"sourceIds\": [\"S03\", \"S04\", \"S05\"], \"boundary\": \"Does not prescribe a specific policy engine or prove policy correctness.\" },\n { \"id\": \"C04\", \"statement\": \"Agent credentials should be short-lived, audience-bound and limited to the minimum required scope, without token passthrough.\", \"sourceIds\": [\"S05\", \"S06\"], \"boundary\": \"Applies at the authorization boundary; credential theft and downstream resource controls still require separate testing.\" },\n { \"id\": \"C05\", \"statement\": \"Irreversible or high-impact actions require an explicit policy boundary and, where assigned by the organization, human approval bound to the complete action.\", \"sourceIds\": [\"S03\", \"S04\"], \"boundary\": \"The mapper does not decide an organization's legal or business impact threshold.\" },\n { \"id\": \"C06\", \"statement\": \"Generated-code execution needs isolation and enforceable limits on time, compute, memory, storage, processes and available credentials.\", \"sourceIds\": [\"S01\", \"S03\", \"S04\", \"S08\"], \"boundary\": \"Sandbox configuration and escape resistance require environment-specific verification.\" },\n { \"id\": \"C07\", \"statement\": \"Agent-controlled network egress should be denied by default, narrowly allowed and attributable in telemetry.\", \"sourceIds\": [\"S01\", \"S03\", \"S04\"], \"boundary\": \"Destination allowlisting is not a substitute for payload, identity and application-layer controls.\" },\n { \"id\": \"C08\", \"statement\": \"Persistent memory needs authorized reads and writes, input validation, provenance, versioning and defined retention.\", \"sourceIds\": [\"S01\", \"S03\", \"S04\"], \"boundary\": \"Implementation depends on memory type, tenant model and data obligations.\" },\n { \"id\": \"C09\", \"statement\": \"Memory integrity response should support anomaly detection, quarantine and rollback while retaining investigation evidence.\", \"sourceIds\": [\"S03\", \"S04\"], \"boundary\": \"Detection rules and rollback points must be validated against the deployed memory design.\" },\n { \"id\": \"C10\", \"statement\": \"Component admission should bind identity, version and digest to reviewed provenance and a release inventory.\", \"sourceIds\": [\"S07\", \"S08\"], \"boundary\": \"Inventory and signatures do not establish trust unless admission policy verifies them.\" },\n { \"id\": \"C11\", \"statement\": \"Artifact provenance provides useful evidence only when verified against expected build properties and a trusted root.\", \"sourceIds\": [\"S07\"], \"boundary\": \"No SLSA level or compliance status is assigned by this mapper.\" },\n { \"id\": \"C12\", \"statement\": \"A material agent action should be traceable across workload and user identity, policy decision, tool request and result.\", \"sourceIds\": [\"S03\", \"S04\", \"S06\"], \"boundary\": \"Log presence does not prove completeness, integrity or sufficient retention.\" },\n { \"id\": \"C13\", \"statement\": \"Request, token, iteration and action limits need a hard spend or execution stop rather than alert-only handling.\", \"sourceIds\": [\"S03\", \"S04\"], \"boundary\": \"Thresholds are system-specific and are not supplied as universal values.\" },\n { \"id\": \"C14\", \"statement\": \"Agent containment can require a circuit breaker, credential revocation, capability blocking, quarantine and preserved audit evidence.\", \"sourceIds\": [\"S03\", \"S04\"], \"boundary\": \"The necessary sequence depends on the affected architecture and incident.\" },\n { \"id\": \"C15\", \"statement\": \"Agent-hijacking evaluations should be task-specific, adaptive and include repeated attempts where the threat model warrants them.\", \"sourceIds\": [\"S02\"], \"boundary\": \"Evaluation results remain bounded to the tested tasks, attacks, models and environment.\" },\n { \"id\": \"C16\", \"statement\": \"CVE-2025-32711 documented an AI command-injection vulnerability in Microsoft 365 Copilot that could disclose information over a network; Aim Labs' researcher report demonstrated a crafted-email and Teams-proxy zero-click proof-of-concept chain.\", \"sourceIds\": [\"S10\", \"S11\"], \"boundary\": \"The MSRC record grounds the vulnerability class and network-disclosure impact; the Aim Labs report grounds the demonstrated proof-of-concept mechanism. Neither establishes a confirmed customer breach.\" },\n { \"id\": \"C17\", \"statement\": \"Aim Labs reports that Microsoft confirmed no customers were affected by EchoLeak, and the disclosure did not establish in-the-wild exploitation or a customer breach.\", \"sourceIds\": [\"S10\", \"S11\"], \"boundary\": \"The no-customer-impact statement is reported in the researcher source; it is not presented here as text directly visible in the MSRC record or as proof that comparable paths cannot exist elsewhere.\" },\n { \"id\": \"C18\", \"statement\": \"Mapping controls to public guidance is decision support, not certification, compliance, penetration-test evidence or proof that a deployed system is secure.\", \"sourceIds\": [\"S01\", \"S03\", \"S04\", \"S05\", \"S07\"], \"boundary\": \"This is the governing interpretation boundary for every output of the mapper.\" },\n { \"id\": \"C19\", \"statement\": \"Sensitive data should be minimized and filtered before it enters model context, persistent memory, logs or tool paths.\", \"sourceIds\": [\"S04\"], \"boundary\": \"The necessary classifications and redaction rules depend on the system, data obligations and authorized use.\" },\n { \"id\": \"C20\", \"statement\": \"AISVS 1.0 requirement identifiers can provide a versioned crosswalk for control review, but a curated mapping is not an AISVS assessment or conformance result.\", \"sourceIds\": [\"S12\"], \"boundary\": \"The AISVS chapters remain the source of truth; this mapper does not claim OWASP certification, conformance or endorsement.\" }\n ]\n}\n", "sha256": "sha256:3685213f7f6821444eb8892b89c94dac2a3320d248356c15caf6f3145245e305", "url": "https://github.com/PharosProduction/agent-security-control-mapper/blob/a35b9c30594162eadf4d790d33bd18aaa785bcee/data/claims.json" }, "data/controls.json": { "content": "{\n \"version\": \"1.0.0\",\n \"controls\": [\n {\n \"id\": \"CTL-IDENTITY\", \"layerId\": \"identity-access\", \"order\": 1,\n \"name\": \"Unique workload identity\", \"objective\": \"Give each agent runtime and service hop an attributable non-human identity instead of a shared account.\",\n \"functions\": [\"prevent\", \"detect\"],\n \"implementation\": \"Issue a distinct workload identity per agent role and environment; bind delegated user context separately and reject anonymous production tool calls.\",\n \"verification\": \"Attempt a production-like tool call with no workload identity, a sibling identity and the intended identity; confirm only the intended identity succeeds and each decision is attributable.\",\n \"evidenceArtifacts\": [\"Identity inventory with environment and owner\", \"Denied and allowed authorization decision records\"],\n \"suggestedOwnerRole\": \"Identity platform owner\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C9.4.1\"], \"sourceIds\": [\"S03\", \"S04\", \"S06\", \"S12\"], \"claimIds\": [\"C02\", \"C12\", \"C20\"]\n },\n {\n \"id\": \"CTL-SCOPED-TOKENS\", \"layerId\": \"identity-access\", \"order\": 2,\n \"name\": \"Short-lived audience-bound credentials\", \"objective\": \"Limit credential reuse by constraining lifetime, audience and scope to the exact resource operation.\",\n \"functions\": [\"prevent\", \"contain\", \"recover\"],\n \"implementation\": \"Mint short-lived tokens for the intended resource server and minimum operation; validate issuer, audience and expiry; prohibit token passthrough and keep revocation available.\",\n \"verification\": \"Replay expired, wrong-audience, over-scoped and forwarded tokens against the resource boundary and confirm fail-closed rejection without downstream forwarding.\",\n \"evidenceArtifacts\": [\"Token policy showing lifetime, audience and scopes\", \"Negative authorization test results\", \"Credential revocation procedure\"],\n \"suggestedOwnerRole\": \"Identity and access engineer\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C5.1.2\", \"C9.4.3\", \"C10.2.7\"], \"sourceIds\": [\"S05\", \"S06\", \"S12\"], \"claimIds\": [\"C04\", \"C20\"]\n },\n {\n \"id\": \"CTL-POLICY-DECISION\", \"layerId\": \"identity-access\", \"order\": 3,\n \"name\": \"Deterministic policy decision point\", \"objective\": \"Keep permission decisions outside probabilistic model output.\",\n \"functions\": [\"prevent\", \"detect\"],\n \"implementation\": \"Evaluate subject, tenant, tool, operation, arguments, resource, user delegation and current policy in a deterministic enforcement service before every privileged call.\",\n \"verification\": \"Submit model requests that ask to bypass policy, change arguments after approval or use an unauthorized resource; confirm the enforcement point denies each call.\",\n \"evidenceArtifacts\": [\"Versioned authorization policy\", \"Policy decision test suite\", \"Decision log samples with policy version\"],\n \"suggestedOwnerRole\": \"Authorization service owner\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C5.2.5\", \"C9.5.3\"], \"sourceIds\": [\"S03\", \"S04\", \"S05\", \"S12\"], \"claimIds\": [\"C03\", \"C20\"]\n },\n {\n \"id\": \"CTL-TENANT-BOUNDARY\", \"layerId\": \"identity-access\", \"order\": 4,\n \"name\": \"Tenant-bound authorization context\", \"objective\": \"Prevent shared agent infrastructure from resolving data, memory or tools outside the active tenant.\",\n \"functions\": [\"prevent\", \"detect\", \"contain\"],\n \"implementation\": \"Carry an immutable tenant identifier from authenticated request through retrieval, memory, policy and tool calls; separate tenant keys and deny missing or conflicting scope.\",\n \"verification\": \"Run cross-tenant negative tests for retrieval, cache, memory and tool resources, including forged and absent tenant identifiers; confirm denial and alerting.\",\n \"evidenceArtifacts\": [\"Tenant data-flow diagram\", \"Cross-tenant isolation test report\", \"Tenant-scoped key and policy inventory\"],\n \"suggestedOwnerRole\": \"Application security owner\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C8.1.3\"], \"sourceIds\": [\"S01\", \"S04\", \"S06\", \"S12\"], \"claimIds\": [\"C02\", \"C12\", \"C20\"]\n },\n\n {\n \"id\": \"CTL-CONTENT-TRUST\", \"layerId\": \"input-instruction\", \"order\": 1,\n \"name\": \"Content trust, classification and minimization\", \"objective\": \"Preserve origin and trust while classifying, minimizing and redacting sensitive fields before content reaches model context, memory, logs or tools.\",\n \"functions\": [\"prevent\", \"detect\"],\n \"implementation\": \"Normalize supported encodings, retain source metadata, label external data as untrusted, classify sensitive fields and minimize or redact them before model context, memory, logs and tool calls; preserve trust labels across later boundaries.\",\n \"verification\": \"Feed encoded, nested and relayed content containing seeded sensitive fields through every ingestion and propagation path; confirm normalization, classification and trust labels persist while disallowed fields are absent from model context, memory, logs and tool payloads.\",\n \"evidenceArtifacts\": [\"Input trust and data-classification taxonomy\", \"Normalization and sensitive-field redaction corpus results\", \"End-to-end provenance and data-minimization trace\"],\n \"suggestedOwnerRole\": \"AI application engineer\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C2.1.1\", \"C2.1.2\", \"C9.3.7\"], \"sourceIds\": [\"S01\", \"S03\", \"S04\", \"S09\", \"S12\"], \"claimIds\": [\"C01\", \"C02\", \"C19\", \"C20\"]\n },\n {\n \"id\": \"CTL-INSTRUCTION-HIERARCHY\", \"layerId\": \"input-instruction\", \"order\": 2,\n \"name\": \"Instruction and data separation\", \"objective\": \"Prevent retrieved or tool-returned content from silently acquiring system-level authority.\",\n \"functions\": [\"prevent\", \"detect\"],\n \"implementation\": \"Represent system policy, user intent and untrusted data in separate fields; delimit external content; refuse instructions originating from data fields and test conflict handling.\",\n \"verification\": \"Use direct and indirect injection cases that conflict with system and user intent; confirm external text cannot alter policy or authorize a capability.\",\n \"evidenceArtifacts\": [\"Prompt and message-boundary specification\", \"Injection evaluation results\", \"Documented conflict-handling behavior\"],\n \"suggestedOwnerRole\": \"AI application security engineer\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C2.1.3\", \"C2.1.6\"], \"sourceIds\": [\"S01\", \"S02\", \"S03\", \"S04\", \"S09\", \"S12\"], \"claimIds\": [\"C01\", \"C15\", \"C20\"]\n },\n {\n \"id\": \"CTL-OUTPUT-SCHEMA\", \"layerId\": \"input-instruction\", \"order\": 3,\n \"name\": \"Schema-bound boundary output\", \"objective\": \"Reject free-form model or tool output before it becomes executable input to another component.\",\n \"functions\": [\"prevent\", \"detect\"],\n \"implementation\": \"Define strict schemas for tool arguments, tool results and agent messages; reject unknown fields, invalid types, oversized values and instructions in data-only fields.\",\n \"verification\": \"Fuzz each schema with extra fields, type confusion, oversized payloads and instruction-bearing strings; confirm invalid records never reach an execution boundary.\",\n \"evidenceArtifacts\": [\"Versioned boundary schemas\", \"Schema fuzz-test report\", \"Rejected-payload telemetry sample\"],\n \"suggestedOwnerRole\": \"Platform API owner\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C9.3.2\", \"C10.4.1\", \"C10.4.2\", \"C10.4.3\", \"C10.4.4\", \"C10.4.5\"], \"sourceIds\": [\"S03\", \"S04\", \"S12\"], \"claimIds\": [\"C02\", \"C03\", \"C20\"]\n },\n {\n \"id\": \"CTL-UNTRUSTED-ISOLATION\", \"layerId\": \"input-instruction\", \"order\": 4,\n \"name\": \"Untrusted-content processing isolation\", \"objective\": \"Break the direct path from untrusted interpretation to privileged tool execution.\",\n \"functions\": [\"prevent\", \"contain\"],\n \"implementation\": \"Process untrusted content in a tool-free or separately privileged stage and pass only a validated structured result into an action-capable stage.\",\n \"verification\": \"Inject tool-call instructions into every external source and confirm the content-processing stage has no credential, tool or egress path capable of carrying them out.\",\n \"evidenceArtifacts\": [\"Trust-boundary architecture diagram\", \"Capability inventory for ingestion stage\", \"Injection-to-tool negative test\"],\n \"suggestedOwnerRole\": \"AI platform architect\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C9.3.5\", \"C9.3.6\"], \"sourceIds\": [\"S01\", \"S03\", \"S04\", \"S12\"], \"claimIds\": [\"C01\", \"C02\", \"C03\", \"C20\"]\n },\n\n {\n \"id\": \"CTL-TOOL-AUTH\", \"layerId\": \"tool-execution\", \"order\": 1,\n \"name\": \"Per-invocation tool authorization\", \"objective\": \"Authorize the exact tool, operation and arguments at the moment of use.\",\n \"functions\": [\"prevent\", \"detect\"],\n \"implementation\": \"Resolve an allowlisted tool manifest, validate arguments, bind user and workload context and make a fresh deterministic policy decision for every invocation.\",\n \"verification\": \"Try unauthorized tools, substituted arguments, stale approvals and calls outside delegated user scope; confirm each is denied before side effects.\",\n \"evidenceArtifacts\": [\"Tool manifest and permission matrix\", \"Per-invocation denial tests\", \"Authorization decision trace\"],\n \"suggestedOwnerRole\": \"Tool gateway owner\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C9.5.1\", \"C10.2.4\", \"C10.2.5\"], \"sourceIds\": [\"S03\", \"S04\", \"S05\", \"S12\"], \"claimIds\": [\"C03\", \"C05\", \"C20\"]\n },\n {\n \"id\": \"CTL-SANDBOX\", \"layerId\": \"tool-execution\", \"order\": 2,\n \"name\": \"Isolated execution sandbox\", \"objective\": \"Constrain generated code and commands to explicit filesystem, process, resource and lifetime boundaries.\",\n \"functions\": [\"prevent\", \"contain\", \"recover\"],\n \"implementation\": \"Run code in an ephemeral least-privilege sandbox with no inherited credentials, read-only base image, bounded CPU, memory, storage, process count and wall time.\",\n \"verification\": \"Exercise filesystem escape, process spawning, secret discovery, resource exhaustion and timeout cases; confirm containment and reliable teardown.\",\n \"evidenceArtifacts\": [\"Sandbox profile and base-image digest\", \"Escape and resource-limit test report\", \"Teardown and residue check\"],\n \"suggestedOwnerRole\": \"Runtime security engineer\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C9.3.1\", \"C10.1.3\"], \"sourceIds\": [\"S01\", \"S03\", \"S04\", \"S08\", \"S12\"], \"claimIds\": [\"C06\", \"C20\"]\n },\n {\n \"id\": \"CTL-EGRESS\", \"layerId\": \"tool-execution\", \"order\": 3,\n \"name\": \"Deny-by-default egress broker\", \"objective\": \"Restrict agent-controlled network traffic to necessary, observable destinations and methods.\",\n \"functions\": [\"prevent\", \"detect\", \"contain\"],\n \"implementation\": \"Block direct network access and broker approved destinations, protocols and methods through an allowlist with DNS and redirect controls, payload limits and destination logging.\",\n \"verification\": \"Attempt direct IP, alternate DNS, redirect, proxy and unlisted destination paths from agent execution; confirm denial and attributable telemetry.\",\n \"evidenceArtifacts\": [\"Egress allowlist with business purpose\", \"Egress bypass test results\", \"Destination and volume log sample\"],\n \"suggestedOwnerRole\": \"Cloud network security owner\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C9.3.7\"], \"sourceIds\": [\"S01\", \"S03\", \"S04\", \"S10\", \"S11\", \"S12\"], \"claimIds\": [\"C07\", \"C16\", \"C20\"]\n },\n {\n \"id\": \"CTL-IDEMPOTENCY\", \"layerId\": \"tool-execution\", \"order\": 4,\n \"name\": \"Idempotency and reversible action design\", \"objective\": \"Prevent retries or duplicated decisions from multiplying external side effects and preserve a recovery path.\",\n \"functions\": [\"prevent\", \"contain\", \"recover\"],\n \"implementation\": \"Attach an idempotency key to each logical action, reject replay, prefer staged commits and define compensating or rollback operations before enabling high-impact tools.\",\n \"verification\": \"Repeat, delay and reorder identical action requests and simulate partial failure; confirm a single committed effect and a tested recovery path.\",\n \"evidenceArtifacts\": [\"Idempotency-key contract\", \"Replay and partial-failure test report\", \"Compensating-action runbook\"],\n \"suggestedOwnerRole\": \"Workflow service owner\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C9.2.3\", \"C9.2.4\"], \"sourceIds\": [\"S03\", \"S04\", \"S12\"], \"claimIds\": [\"C05\", \"C20\"]\n },\n\n {\n \"id\": \"CTL-MEMORY-AUTH\", \"layerId\": \"memory-integrity\", \"order\": 1,\n \"name\": \"Authorized memory namespace\", \"objective\": \"Bind memory reads and writes to the current identity, tenant, purpose and retention class.\",\n \"functions\": [\"prevent\", \"detect\"],\n \"implementation\": \"Enforce separate namespaces, authorize every read and write, carry tenant and subject provenance and deny cross-purpose retrieval by default.\",\n \"verification\": \"Attempt memory reads and writes across users, tenants, roles and purposes; confirm denial and an attributable audit record.\",\n \"evidenceArtifacts\": [\"Memory authorization matrix\", \"Namespace isolation test report\", \"Denied-access audit samples\"],\n \"suggestedOwnerRole\": \"Data platform owner\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C8.1.3\"], \"sourceIds\": [\"S01\", \"S03\", \"S04\", \"S12\"], \"claimIds\": [\"C08\", \"C20\"]\n },\n {\n \"id\": \"CTL-MEMORY-VALIDATION\", \"layerId\": \"memory-integrity\", \"order\": 2,\n \"name\": \"Memory write validation and provenance\", \"objective\": \"Keep untrusted, contradictory or malformed content from silently becoming persistent decision context.\",\n \"functions\": [\"prevent\", \"detect\", \"contain\"],\n \"implementation\": \"Validate memory schemas, retain source and writer identity, classify trust, detect conflicting high-impact facts and quarantine suspicious writes before retrieval.\",\n \"verification\": \"Submit malformed, untrusted, contradictory and cross-scope memories; confirm rejection or quarantine and retained provenance.\",\n \"evidenceArtifacts\": [\"Memory schema and trust policy\", \"Poisoning test corpus results\", \"Quarantine queue sample\"],\n \"suggestedOwnerRole\": \"AI data security owner\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C8.2.1\", \"C8.2.3\", \"C8.2.4\", \"C8.2.5\"], \"sourceIds\": [\"S01\", \"S03\", \"S04\", \"S12\"], \"claimIds\": [\"C08\", \"C09\", \"C20\"]\n },\n {\n \"id\": \"CTL-MEMORY-LIFECYCLE\", \"layerId\": \"memory-integrity\", \"order\": 3,\n \"name\": \"Versioned memory lifecycle\", \"objective\": \"Make persistent agent state inspectable, expirable and attributable over time.\",\n \"functions\": [\"detect\", \"contain\", \"recover\"],\n \"implementation\": \"Version writes, apply explicit TTL and deletion rules, preserve source metadata and record which memory versions influenced each material decision.\",\n \"verification\": \"Advance expiry, delete a subject and replay a decision trace; confirm expired data is unavailable and the influencing memory versions remain identifiable.\",\n \"evidenceArtifacts\": [\"Retention and deletion schedule\", \"Memory version history sample\", \"Expiry and deletion test results\"],\n \"suggestedOwnerRole\": \"Data governance owner\", \"releaseGateWhenRequired\": false,\n \"standardRefs\": [\"C8.3.1\", \"C8.3.2\"], \"sourceIds\": [\"S01\", \"S03\", \"S04\", \"S12\"], \"claimIds\": [\"C08\", \"C20\"]\n },\n {\n \"id\": \"CTL-MEMORY-ROLLBACK\", \"layerId\": \"memory-integrity\", \"order\": 4,\n \"name\": \"Memory quarantine and rollback\", \"objective\": \"Remove suspected poisoned state and restore a known version without destroying investigation evidence.\",\n \"functions\": [\"detect\", \"contain\", \"recover\"],\n \"implementation\": \"Detect anomalous write or retrieval patterns, isolate affected namespaces, block further influence and support operator-approved rollback with preserved audit evidence.\",\n \"verification\": \"Seed a known poisoned record, trigger detection, quarantine the namespace and restore the prior version; confirm downstream retrieval no longer returns the record.\",\n \"evidenceArtifacts\": [\"Memory anomaly rules\", \"Quarantine and rollback exercise report\", \"Preserved incident evidence index\"],\n \"suggestedOwnerRole\": \"Security operations owner\", \"releaseGateWhenRequired\": false,\n \"standardRefs\": [\"C8.2.4\", \"C8.3.2\"], \"sourceIds\": [\"S03\", \"S04\", \"S12\"], \"claimIds\": [\"C09\", \"C14\", \"C20\"]\n },\n\n {\n \"id\": \"CTL-COMPONENT-ALLOWLIST\", \"layerId\": \"supply-chain\", \"order\": 1,\n \"name\": \"Component and capability allowlist\", \"objective\": \"Prevent unreviewed packages, tools, skills, models and servers from entering the agent path.\",\n \"functions\": [\"prevent\", \"detect\"],\n \"implementation\": \"Maintain a versioned allowlist of component identity, source, version, digest, permissions and owner; reject undeclared capabilities at build and connection time.\",\n \"verification\": \"Introduce an unlisted package, tool, skill and MCP server in a release candidate; confirm each is blocked before use.\",\n \"evidenceArtifacts\": [\"Approved component and capability registry\", \"Admission-policy test results\", \"Exception record with expiry when applicable\"],\n \"suggestedOwnerRole\": \"Product security owner\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C6.1.1\", \"C6.1.2\", \"C6.1.3\", \"C6.1.4\", \"C10.1.1\", \"C10.1.2\"], \"sourceIds\": [\"S03\", \"S04\", \"S07\", \"S08\", \"S12\"], \"claimIds\": [\"C10\", \"C20\"]\n },\n {\n \"id\": \"CTL-INTEGRITY-PROVENANCE\", \"layerId\": \"supply-chain\", \"order\": 2,\n \"name\": \"Digest and provenance verification\", \"objective\": \"Verify that the admitted artifact is the expected output from an identified build and trusted source.\",\n \"functions\": [\"prevent\", \"detect\"],\n \"implementation\": \"Pin immutable digests, verify signed provenance against declared build expectations and trusted roots and fail closed on mismatch or missing evidence.\",\n \"verification\": \"Substitute the artifact, builder identity, source reference and signature root independently; confirm admission fails for every mismatch.\",\n \"evidenceArtifacts\": [\"Pinned artifact digest set\", \"Verified provenance statement\", \"Trusted-root and build-expectation policy\"],\n \"suggestedOwnerRole\": \"Software supply-chain security owner\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C6.1.2\", \"C6.1.3\", \"C6.1.4\"], \"sourceIds\": [\"S07\", \"S08\", \"S12\"], \"claimIds\": [\"C10\", \"C11\", \"C20\"]\n },\n {\n \"id\": \"CTL-AIBOM\", \"layerId\": \"supply-chain\", \"order\": 3,\n \"name\": \"AI bill of materials and ownership\", \"objective\": \"Keep an actionable inventory of the models, data-dependent services, tools and software that form each release.\",\n \"functions\": [\"detect\", \"contain\", \"recover\"],\n \"implementation\": \"Generate a release-bound inventory with component version, digest, source, license, owner, privileges and transitive dependencies; retain it with the release record.\",\n \"verification\": \"Select a deployed release and reconcile its runtime components against the retained inventory; investigate every missing or unexpected item.\",\n \"evidenceArtifacts\": [\"Release-bound SBOM or AIBOM\", \"Runtime-to-inventory reconciliation report\", \"Named component owners\"],\n \"suggestedOwnerRole\": \"Release engineering owner\", \"releaseGateWhenRequired\": false,\n \"standardRefs\": [\"C6.2.1\", \"C6.2.2\", \"C6.2.3\"], \"sourceIds\": [\"S03\", \"S04\", \"S07\", \"S08\", \"S12\"], \"claimIds\": [\"C10\", \"C20\"]\n },\n {\n \"id\": \"CTL-DEPENDENCY-ADMISSION\", \"layerId\": \"supply-chain\", \"order\": 4,\n \"name\": \"Dependency admission and revocation\", \"objective\": \"Detect risky component changes before release and remove affected versions quickly after new evidence.\",\n \"functions\": [\"prevent\", \"detect\", \"contain\", \"recover\"],\n \"implementation\": \"Scan dependencies, review material version or permission changes, require policy-based admission and maintain a tested path to quarantine, revoke and rebuild affected releases.\",\n \"verification\": \"Simulate a newly disallowed component version and confirm build admission stops, deployed inventory is found and the rebuild or rollback procedure succeeds.\",\n \"evidenceArtifacts\": [\"Dependency change review\", \"Admission scan results\", \"Component revocation exercise\"],\n \"suggestedOwnerRole\": \"Application security owner\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C6.1.1\", \"C6.1.2\", \"C6.1.3\", \"C6.1.4\"], \"sourceIds\": [\"S03\", \"S04\", \"S07\", \"S08\", \"S12\"], \"claimIds\": [\"C10\", \"C11\", \"C20\"]\n },\n\n {\n \"id\": \"CTL-AUDIT\", \"layerId\": \"monitoring-oversight\", \"order\": 1,\n \"name\": \"End-to-end action audit trail\", \"objective\": \"Reconstruct which identity, input, policy, model decision, tool and result produced a material side effect.\",\n \"functions\": [\"detect\", \"contain\", \"recover\"],\n \"implementation\": \"Emit structured, access-controlled records for correlation ID, workload and user identity, tenant, input trust class, policy version and decision, tool arguments, result and approval reference.\",\n \"verification\": \"Choose a material test action and reconstruct its complete chain from ingress to result without relying on model prose or mutable application state.\",\n \"evidenceArtifacts\": [\"Structured audit event schema\", \"Sample reconstructed action trace\", \"Log access and retention policy\"],\n \"suggestedOwnerRole\": \"Observability platform owner\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C12.1.1\", \"C12.1.3\", \"C12.4.2\"], \"sourceIds\": [\"S03\", \"S04\", \"S06\", \"S12\"], \"claimIds\": [\"C12\", \"C20\"]\n },\n {\n \"id\": \"CTL-RATE-SPEND\", \"layerId\": \"monitoring-oversight\", \"order\": 2,\n \"name\": \"Hard action, iteration and spend limits\", \"objective\": \"Stop autonomous execution before it exceeds the approved operational envelope.\",\n \"functions\": [\"prevent\", \"detect\", \"contain\"],\n \"implementation\": \"Set enforceable per-task and per-identity limits for requests, tokens, iterations, tool calls, elapsed time and spend; terminate rather than merely alert at a hard boundary.\",\n \"verification\": \"Drive each meter to its configured boundary independently and confirm no further model or tool action occurs after the stop decision.\",\n \"evidenceArtifacts\": [\"Versioned limit policy\", \"Boundary and concurrency test results\", \"Stop-decision telemetry\"],\n \"suggestedOwnerRole\": \"AI platform operations owner\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C9.1.1\", \"C9.1.2\"], \"sourceIds\": [\"S03\", \"S04\", \"S12\"], \"claimIds\": [\"C13\", \"C20\"]\n },\n {\n \"id\": \"CTL-HUMAN-APPROVAL\", \"layerId\": \"monitoring-oversight\", \"order\": 3,\n \"name\": \"Bound human approval for high-impact action\", \"objective\": \"Require accountable human authorization for the exact irreversible or high-impact operation.\",\n \"functions\": [\"prevent\", \"detect\"],\n \"implementation\": \"Pause before side effects, show complete operation parameters and impact, bind approver identity and nonce to the unchanged request and expire or invalidate approval after modification.\",\n \"verification\": \"Change parameters, identity, context and timing after approval; confirm every mutation requires a new approval and the original cannot be replayed.\",\n \"evidenceArtifacts\": [\"High-impact action classification\", \"Approval binding specification\", \"Mutation and replay test report\"],\n \"suggestedOwnerRole\": \"Business risk owner\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C9.2.1\", \"C9.2.8\", \"C9.2.9\", \"C9.2.10\"], \"sourceIds\": [\"S03\", \"S04\", \"S12\"], \"claimIds\": [\"C05\", \"C20\"]\n },\n {\n \"id\": \"CTL-KILL-SWITCH\", \"layerId\": \"monitoring-oversight\", \"order\": 4,\n \"name\": \"Out-of-band containment and kill procedure\", \"objective\": \"Stop an unsafe action chain even when the agent, model or primary application path is unavailable or compromised.\",\n \"functions\": [\"detect\", \"contain\", \"recover\"],\n \"implementation\": \"Provide an independent operator path to trip circuit breakers, revoke credentials, block tool and egress access, quarantine memory or workloads and retain audit evidence.\",\n \"verification\": \"Run a timed exercise with the agent control plane unavailable; confirm operators can stop calls, revoke authority, isolate affected state and preserve evidence.\",\n \"evidenceArtifacts\": [\"Out-of-band kill runbook\", \"Credential and capability revocation test\", \"Containment exercise timeline\"],\n \"suggestedOwnerRole\": \"Incident response owner\", \"releaseGateWhenRequired\": true,\n \"standardRefs\": [\"C9.1.3\", \"C9.6.1\", \"C9.6.2\", \"C9.6.3\", \"C12.4.3\"], \"sourceIds\": [\"S03\", \"S04\", \"S12\"], \"claimIds\": [\"C14\", \"C20\"]\n }\n ]\n}\n", "sha256": "sha256:a50333997f3f9832df16329f27d8e5be608c50ba1dffea0282ee1b1eff0956a6", "url": "https://github.com/PharosProduction/agent-security-control-mapper/blob/a35b9c30594162eadf4d790d33bd18aaa785bcee/data/controls.json" }, "data/criteria.json": { "content": "{\n \"version\": \"1.0.0\",\n \"criteria\": [\n { \"id\": \"untrusted_external_content\", \"label\": \"Untrusted external content\", \"order\": 1, \"core\": true, \"prompt\": \"Can the agent read content supplied or influenced by an untrusted party?\", \"description\": \"Includes email, web pages, retrieved documents, tool output and peer-agent messages.\" },\n { \"id\": \"sensitive_data\", \"label\": \"Sensitive data\", \"order\": 2, \"core\": true, \"prompt\": \"Can the agent access confidential, regulated or security-sensitive data?\", \"description\": \"Includes credentials, personal data, source code, internal documents and tenant data.\" },\n { \"id\": \"action_capable_tools\", \"label\": \"Action-capable tools\", \"order\": 3, \"core\": true, \"prompt\": \"Can the agent invoke a tool that changes external state?\", \"description\": \"Includes sending messages, changing records, deploying code, moving funds and administering systems.\" },\n { \"id\": \"irreversible_high_impact_actions\", \"label\": \"Irreversible or high-impact actions\", \"order\": 4, \"core\": true, \"prompt\": \"Can an action be destructive, legally significant, costly or difficult to reverse?\", \"description\": \"Examples include deletion, payment, account changes, public publication and production deployment.\" },\n { \"id\": \"generated_code_execution\", \"label\": \"Generated-code execution\", \"order\": 5, \"core\": true, \"prompt\": \"Can the system execute model-generated or model-selected code or commands?\", \"description\": \"Includes shells, notebooks, interpreters, build jobs and browser scripts.\" },\n { \"id\": \"persistent_memory\", \"label\": \"Persistent memory\", \"order\": 6, \"core\": true, \"prompt\": \"Can agent-generated state influence later sessions or users?\", \"description\": \"Includes long-term memory, vector stores, profiles, summaries and learned routing state.\" },\n { \"id\": \"third_party_components\", \"label\": \"Third-party components\", \"order\": 7, \"core\": true, \"prompt\": \"Does the agent depend on external packages, models, skills, tools, containers or hosted services?\", \"description\": \"Covers build-time and runtime dependencies outside the deploying team's direct control.\" },\n { \"id\": \"mcp_a2a_connections\", \"label\": \"MCP or A2A connections\", \"order\": 8, \"core\": true, \"prompt\": \"Does the system connect through MCP, agent-to-agent protocols or equivalent capability brokers?\", \"description\": \"Covers remote capability discovery, delegated authorization and inter-agent messages.\" },\n { \"id\": \"open_network_egress\", \"label\": \"Open network egress\", \"order\": 9, \"core\": true, \"prompt\": \"Can agent-controlled execution reach arbitrary network destinations?\", \"description\": \"Answer yes when destinations are not restricted to an explicit, enforced allowlist.\" },\n { \"id\": \"multi_tenancy\", \"label\": \"Multi-tenancy\", \"order\": 10, \"core\": true, \"prompt\": \"Does one deployment process data or actions for more than one tenant?\", \"description\": \"Includes shared retrieval, memory, caches, credentials, tools and execution infrastructure.\" },\n { \"id\": \"multi_agent_delegation\", \"label\": \"Multi-agent delegation\", \"order\": 11, \"core\": true, \"prompt\": \"Can one agent delegate work or authority to another agent?\", \"description\": \"Includes supervisor-worker graphs, swarms and agent-to-agent task handoffs.\" }\n ]\n}\n", "sha256": "sha256:abcdef461ce6d8cb6488b382025a5431b05f34b34fca4f6fc095d277e6cb3eb2", "url": "https://github.com/PharosProduction/agent-security-control-mapper/blob/a35b9c30594162eadf4d790d33bd18aaa785bcee/data/criteria.json" }, "data/incidents.json": { "content": "{\n \"version\": \"1.0.0\",\n \"incidents\": [\n {\n \"id\": \"INC-ECHOLEAK-2025\", \"name\": \"EchoLeak / CVE-2025-32711\", \"researchPublishedOn\": \"2025-05-31\", \"cvePublishedOn\": \"2025-06-11\",\n \"summary\": \"A responsibly disclosed Microsoft 365 Copilot AI command-injection vulnerability had a demonstrated zero-click proof-of-concept chain: crafted email content could influence processing and use an allowed Microsoft Teams proxy path to disclose information over a network.\",\n \"evidenceBoundary\": \"This record describes a production vulnerability and proof of concept, not in-the-wild exploitation or a confirmed customer breach. Aim Labs reports that Microsoft confirmed no customers were affected.\",\n \"attackChain\": [\n { \"order\": 1, \"label\": \"Crafted email supplied untrusted instructions\" },\n { \"order\": 2, \"label\": \"The content entered the Copilot processing context\" },\n { \"order\": 3, \"label\": \"The proof of concept used an allowed Microsoft Teams proxy path\" },\n { \"order\": 4, \"label\": \"Information could be disclosed over the network\" }\n ],\n \"relevantControlIds\": [\"CTL-CONTENT-TRUST\", \"CTL-INSTRUCTION-HIERARCHY\", \"CTL-UNTRUSTED-ISOLATION\", \"CTL-EGRESS\", \"CTL-AUDIT\"],\n \"claimIds\": [\"C01\", \"C07\", \"C16\", \"C17\"], \"sourceIds\": [\"S10\", \"S11\"]\n }\n ]\n}\n", "sha256": "sha256:1f2d48e53ef2e7d66e69fd6e993e083350f177c13a35d1638d089a15ccf9a337", "url": "https://github.com/PharosProduction/agent-security-control-mapper/blob/a35b9c30594162eadf4d790d33bd18aaa785bcee/data/incidents.json" }, "data/layers.json": { "content": "{\n \"version\": \"1.0.0\",\n \"layers\": [\n { \"id\": \"identity-access\", \"label\": \"Identity and access\", \"order\": 1, \"objective\": \"Bind every principal and delegated action to explicit, minimal authority.\" },\n { \"id\": \"input-instruction\", \"label\": \"Input and instruction handling\", \"order\": 2, \"objective\": \"Keep untrusted data separate from trusted instructions and validate every boundary crossing.\" },\n { \"id\": \"tool-execution\", \"label\": \"Tool execution\", \"order\": 3, \"objective\": \"Authorize, constrain and make recoverable every action outside the model.\" },\n { \"id\": \"memory-integrity\", \"label\": \"Memory integrity\", \"order\": 4, \"objective\": \"Prevent untrusted or cross-scope state from silently shaping later behavior.\" },\n { \"id\": \"supply-chain\", \"label\": \"Supply chain\", \"order\": 5, \"objective\": \"Admit only identified, verified and revocable components into build and runtime paths.\" },\n { \"id\": \"monitoring-oversight\", \"label\": \"Monitoring and human oversight\", \"order\": 6, \"objective\": \"Detect harmful action chains, stop them within bounded cost and preserve investigation evidence.\" }\n ]\n}\n", "sha256": "sha256:b0221d37a1f01159a32b03d45bb4224678c1e05027d7ff8f21ea628e01279701", "url": "https://github.com/PharosProduction/agent-security-control-mapper/blob/a35b9c30594162eadf4d790d33bd18aaa785bcee/data/layers.json" }, "data/mappings.json": { "content": "{\n \"version\": \"1.0.0\",\n \"mappings\": [\n { \"id\": \"MAP-001\", \"priority\": 10, \"when\": { \"all\": [] }, \"threatId\": \"THR-AUTHORITY-CONFUSION\", \"controlId\": \"CTL-IDENTITY\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"AUTH-1\", \"AUTH-3\"], \"rationale\": \"Every production agent needs an attributable principal before additional capability-specific controls can be assigned.\", \"sourceIds\": [\"S03\", \"S04\", \"S06\"], \"claimIds\": [\"C02\", \"C12\"] },\n { \"id\": \"MAP-002\", \"priority\": 20, \"when\": { \"all\": [{ \"criterionId\": \"action_capable_tools\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-CREDENTIAL-EXPOSURE\", \"controlId\": \"CTL-SCOPED-TOKENS\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"CRED-2\", \"CRED-3\"], \"rationale\": \"Action-capable tools turn a reusable or misdirected token into an external side effect.\", \"sourceIds\": [\"S05\", \"S06\"], \"claimIds\": [\"C04\"] },\n { \"id\": \"MAP-003\", \"priority\": 30, \"when\": { \"all\": [{ \"criterionId\": \"action_capable_tools\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-AUTHORITY-CONFUSION\", \"controlId\": \"CTL-POLICY-DECISION\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"AUTH-2\"], \"rationale\": \"Tool permission must be decided by deterministic policy rather than model-generated intent.\", \"sourceIds\": [\"S03\", \"S04\", \"S05\"], \"claimIds\": [\"C03\"] },\n { \"id\": \"MAP-004\", \"priority\": 40, \"when\": { \"all\": [{ \"criterionId\": \"multi_tenancy\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-CROSS-TENANT\", \"controlId\": \"CTL-TENANT-BOUNDARY\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"TEN-1\", \"TEN-2\", \"TEN-3\"], \"rationale\": \"Shared infrastructure needs an enforced tenant context at every data and action boundary.\", \"sourceIds\": [\"S01\", \"S04\", \"S06\"], \"claimIds\": [\"C02\", \"C12\"] },\n\n { \"id\": \"MAP-005\", \"priority\": 50, \"when\": { \"all\": [{ \"criterionId\": \"untrusted_external_content\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-INDIRECT-INJECTION\", \"controlId\": \"CTL-CONTENT-TRUST\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"INJ-1\", \"INJ-2\"], \"rationale\": \"Untrusted instructions cannot be handled consistently if their origin and trust class disappear during ingestion.\", \"sourceIds\": [\"S01\", \"S03\", \"S04\", \"S09\"], \"claimIds\": [\"C01\", \"C02\"] },\n { \"id\": \"MAP-006\", \"priority\": 60, \"when\": { \"all\": [{ \"criterionId\": \"untrusted_external_content\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-INDIRECT-INJECTION\", \"controlId\": \"CTL-INSTRUCTION-HIERARCHY\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"INJ-2\", \"INJ-3\"], \"rationale\": \"External content must remain data when it conflicts with trusted task instructions.\", \"sourceIds\": [\"S01\", \"S02\", \"S03\", \"S04\", \"S09\"], \"claimIds\": [\"C01\", \"C15\"] },\n { \"id\": \"MAP-007\", \"priority\": 70, \"when\": { \"all\": [{ \"criterionId\": \"action_capable_tools\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-TOOL-MISUSE\", \"controlId\": \"CTL-OUTPUT-SCHEMA\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"TOOL-2\"], \"rationale\": \"A typed boundary rejects malformed or instruction-bearing output before it becomes a tool request.\", \"sourceIds\": [\"S03\", \"S04\"], \"claimIds\": [\"C02\", \"C03\"] },\n { \"id\": \"MAP-008\", \"priority\": 80, \"when\": { \"all\": [{ \"criterionId\": \"untrusted_external_content\", \"equals\": \"yes\" }, { \"criterionId\": \"action_capable_tools\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-INDIRECT-INJECTION\", \"controlId\": \"CTL-UNTRUSTED-ISOLATION\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"INJ-3\", \"INJ-4\"], \"rationale\": \"Separating content interpretation from privileged execution removes a direct injection-to-action path.\", \"sourceIds\": [\"S01\", \"S03\", \"S04\"], \"claimIds\": [\"C01\", \"C02\", \"C03\"] },\n\n { \"id\": \"MAP-009\", \"priority\": 90, \"when\": { \"all\": [{ \"criterionId\": \"action_capable_tools\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-TOOL-MISUSE\", \"controlId\": \"CTL-TOOL-AUTH\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"TOOL-2\", \"TOOL-3\"], \"rationale\": \"Every state-changing invocation needs authorization bound to the exact tool and arguments.\", \"sourceIds\": [\"S03\", \"S04\", \"S05\"], \"claimIds\": [\"C03\", \"C05\"] },\n { \"id\": \"MAP-010\", \"priority\": 100, \"when\": { \"all\": [{ \"criterionId\": \"generated_code_execution\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-CODE-EGRESS\", \"controlId\": \"CTL-SANDBOX\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"CODE-2\", \"CODE-3\"], \"rationale\": \"Generated code needs enforced runtime boundaries independent of model instructions.\", \"sourceIds\": [\"S01\", \"S03\", \"S04\", \"S08\"], \"claimIds\": [\"C06\"] },\n { \"id\": \"MAP-011\", \"priority\": 110, \"when\": { \"all\": [{ \"criterionId\": \"open_network_egress\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-CODE-EGRESS\", \"controlId\": \"CTL-EGRESS\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"CODE-3\", \"CODE-4\"], \"rationale\": \"Open egress supplies an exfiltration path that a destination-aware broker can interrupt and observe.\", \"sourceIds\": [\"S01\", \"S03\", \"S04\", \"S10\", \"S11\"], \"claimIds\": [\"C07\", \"C16\"] },\n { \"id\": \"MAP-012\", \"priority\": 120, \"when\": { \"all\": [{ \"criterionId\": \"irreversible_high_impact_actions\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-TOOL-MISUSE\", \"controlId\": \"CTL-IDEMPOTENCY\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"TOOL-4\"], \"rationale\": \"High-impact workflows need replay protection and a recovery design before side effects are enabled.\", \"sourceIds\": [\"S03\", \"S04\"], \"claimIds\": [\"C05\"] },\n\n { \"id\": \"MAP-013\", \"priority\": 130, \"when\": { \"all\": [{ \"criterionId\": \"persistent_memory\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-MEMORY-POISONING\", \"controlId\": \"CTL-MEMORY-AUTH\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"MEM-1\", \"MEM-3\"], \"rationale\": \"Persistent state needs read and write scope that is independent of generated content.\", \"sourceIds\": [\"S01\", \"S03\", \"S04\"], \"claimIds\": [\"C08\"] },\n { \"id\": \"MAP-014\", \"priority\": 140, \"when\": { \"all\": [{ \"criterionId\": \"persistent_memory\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-MEMORY-POISONING\", \"controlId\": \"CTL-MEMORY-VALIDATION\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"MEM-1\", \"MEM-2\"], \"rationale\": \"Validation, provenance and quarantine prevent candidate memory from silently becoming trusted state.\", \"sourceIds\": [\"S01\", \"S03\", \"S04\"], \"claimIds\": [\"C08\", \"C09\"] },\n { \"id\": \"MAP-015\", \"priority\": 150, \"when\": { \"all\": [{ \"criterionId\": \"persistent_memory\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-MEMORY-POISONING\", \"controlId\": \"CTL-MEMORY-LIFECYCLE\", \"requirementLevel\": \"recommended\", \"interruptsStageIds\": [\"MEM-2\", \"MEM-3\"], \"rationale\": \"Versioning and expiry bound how long a stored item can influence later decisions.\", \"sourceIds\": [\"S01\", \"S03\", \"S04\"], \"claimIds\": [\"C08\"] },\n { \"id\": \"MAP-016\", \"priority\": 160, \"when\": { \"all\": [{ \"criterionId\": \"persistent_memory\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-MEMORY-POISONING\", \"controlId\": \"CTL-MEMORY-ROLLBACK\", \"requirementLevel\": \"recommended\", \"interruptsStageIds\": [\"MEM-3\", \"MEM-4\"], \"rationale\": \"Detected poisoned state needs a reversible containment path that retains investigation evidence.\", \"sourceIds\": [\"S03\", \"S04\"], \"claimIds\": [\"C09\", \"C14\"] },\n\n { \"id\": \"MAP-017\", \"priority\": 170, \"when\": { \"all\": [{ \"criterionId\": \"third_party_components\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-SUPPLY-COMPROMISE\", \"controlId\": \"CTL-COMPONENT-ALLOWLIST\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"SUP-2\", \"SUP-3\"], \"rationale\": \"A declared allowlist establishes which components and capabilities are eligible for admission.\", \"sourceIds\": [\"S03\", \"S04\", \"S07\", \"S08\"], \"claimIds\": [\"C10\"] },\n { \"id\": \"MAP-018\", \"priority\": 180, \"when\": { \"all\": [{ \"criterionId\": \"third_party_components\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-SUPPLY-COMPROMISE\", \"controlId\": \"CTL-INTEGRITY-PROVENANCE\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"SUP-1\", \"SUP-3\"], \"rationale\": \"Digest and provenance checks detect substitution only when verified against trusted expectations and roots.\", \"sourceIds\": [\"S07\", \"S08\"], \"claimIds\": [\"C10\", \"C11\"] },\n { \"id\": \"MAP-019\", \"priority\": 190, \"when\": { \"all\": [{ \"criterionId\": \"third_party_components\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-SUPPLY-COMPROMISE\", \"controlId\": \"CTL-AIBOM\", \"requirementLevel\": \"recommended\", \"interruptsStageIds\": [\"SUP-2\", \"SUP-4\"], \"rationale\": \"A release-bound inventory connects a new component finding to affected deployments and accountable owners.\", \"sourceIds\": [\"S03\", \"S04\", \"S07\", \"S08\"], \"claimIds\": [\"C10\"] },\n { \"id\": \"MAP-020\", \"priority\": 200, \"when\": { \"all\": [{ \"criterionId\": \"third_party_components\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-SUPPLY-COMPROMISE\", \"controlId\": \"CTL-DEPENDENCY-ADMISSION\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"SUP-2\", \"SUP-3\", \"SUP-4\"], \"rationale\": \"Admission and revocation turn component evidence into enforceable release and response decisions.\", \"sourceIds\": [\"S03\", \"S04\", \"S07\", \"S08\"], \"claimIds\": [\"C10\", \"C11\"] },\n\n { \"id\": \"MAP-021\", \"priority\": 210, \"when\": { \"all\": [{ \"criterionId\": \"action_capable_tools\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-TOOL-MISUSE\", \"controlId\": \"CTL-AUDIT\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"TOOL-3\", \"TOOL-4\"], \"rationale\": \"A material tool action must remain attributable to identity, policy, arguments, approval and result.\", \"sourceIds\": [\"S03\", \"S04\", \"S06\"], \"claimIds\": [\"C12\"] },\n { \"id\": \"MAP-022\", \"priority\": 220, \"when\": { \"all\": [{ \"criterionId\": \"multi_agent_delegation\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-RUNAWAY-AUTONOMY\", \"controlId\": \"CTL-RATE-SPEND\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"RUN-2\", \"RUN-3\"], \"rationale\": \"Delegated task graphs can multiply calls and cost, so hard limits must terminate the chain.\", \"sourceIds\": [\"S03\", \"S04\"], \"claimIds\": [\"C13\"] },\n { \"id\": \"MAP-023\", \"priority\": 230, \"when\": { \"all\": [{ \"criterionId\": \"irreversible_high_impact_actions\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-RUNAWAY-AUTONOMY\", \"controlId\": \"CTL-HUMAN-APPROVAL\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"RUN-3\"], \"rationale\": \"A high-impact side effect needs approval bound to the exact operation rather than a general instruction to the agent.\", \"sourceIds\": [\"S03\", \"S04\"], \"claimIds\": [\"C05\"] },\n { \"id\": \"MAP-024\", \"priority\": 240, \"when\": { \"all\": [{ \"criterionId\": \"multi_agent_delegation\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-RUNAWAY-AUTONOMY\", \"controlId\": \"CTL-KILL-SWITCH\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"RUN-3\", \"RUN-4\"], \"rationale\": \"A delegated chain needs containment that does not depend on cooperation from the agent control plane.\", \"sourceIds\": [\"S03\", \"S04\"], \"claimIds\": [\"C14\"] },\n\n { \"id\": \"MAP-025\", \"priority\": 250, \"when\": { \"all\": [{ \"criterionId\": \"mcp_a2a_connections\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-CREDENTIAL-EXPOSURE\", \"controlId\": \"CTL-SCOPED-TOKENS\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"CRED-1\", \"CRED-2\", \"CRED-3\"], \"rationale\": \"Remote capability protocols add an audience boundary where token forwarding and overbroad scope must fail closed.\", \"sourceIds\": [\"S05\", \"S06\"], \"claimIds\": [\"C04\"] },\n { \"id\": \"MAP-026\", \"priority\": 260, \"when\": { \"all\": [{ \"criterionId\": \"sensitive_data\", \"equals\": \"yes\" }, { \"criterionId\": \"open_network_egress\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-CODE-EGRESS\", \"controlId\": \"CTL-EGRESS\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"CODE-3\", \"CODE-4\"], \"rationale\": \"Sensitive-data access raises the consequence of an unrestricted outbound path and keeps egress control at release-gate level.\", \"sourceIds\": [\"S01\", \"S03\", \"S04\", \"S10\", \"S11\"], \"claimIds\": [\"C07\", \"C16\"] },\n { \"id\": \"MAP-027\", \"priority\": 270, \"when\": { \"all\": [{ \"criterionId\": \"generated_code_execution\", \"equals\": \"yes\" }, { \"criterionId\": \"open_network_egress\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-CODE-EGRESS\", \"controlId\": \"CTL-KILL-SWITCH\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"CODE-2\", \"CODE-3\", \"CODE-4\"], \"rationale\": \"Code plus network access requires an independent path to terminate execution and revoke access during containment.\", \"sourceIds\": [\"S03\", \"S04\"], \"claimIds\": [\"C14\"] },\n { \"id\": \"MAP-028\", \"priority\": 280, \"when\": { \"all\": [{ \"criterionId\": \"sensitive_data\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-SENSITIVE-DATA-EXPOSURE\", \"controlId\": \"CTL-CONTENT-TRUST\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"DATA-1\", \"DATA-2\", \"DATA-3\"], \"rationale\": \"Sensitive fields need classification, minimization and redaction before they enter model, memory, tool or logging paths.\", \"sourceIds\": [\"S04\", \"S12\"], \"claimIds\": [\"C19\", \"C20\"] },\n { \"id\": \"MAP-029\", \"priority\": 290, \"when\": { \"all\": [{ \"criterionId\": \"sensitive_data\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-SENSITIVE-DATA-EXPOSURE\", \"controlId\": \"CTL-SCOPED-TOKENS\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"DATA-1\", \"DATA-4\"], \"rationale\": \"Minimally scoped and audience-bound authority limits which sensitive resources the agent can reach and where credentials can be reused.\", \"sourceIds\": [\"S05\", \"S06\", \"S12\"], \"claimIds\": [\"C04\", \"C20\"] },\n { \"id\": \"MAP-030\", \"priority\": 300, \"when\": { \"all\": [{ \"criterionId\": \"irreversible_high_impact_actions\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-AUTHORITY-CONFUSION\", \"controlId\": \"CTL-POLICY-DECISION\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"AUTH-2\"], \"rationale\": \"High-impact authority needs a deterministic policy decision even when the deployment declares no general action-capable tool category.\", \"sourceIds\": [\"S03\", \"S04\", \"S12\"], \"claimIds\": [\"C03\", \"C05\", \"C20\"] },\n { \"id\": \"MAP-031\", \"priority\": 310, \"when\": { \"all\": [{ \"criterionId\": \"irreversible_high_impact_actions\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-TOOL-MISUSE\", \"controlId\": \"CTL-TOOL-AUTH\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"TOOL-2\", \"TOOL-3\"], \"rationale\": \"The exact high-impact operation and parameters require authorization before the side effect is committed.\", \"sourceIds\": [\"S03\", \"S04\", \"S12\"], \"claimIds\": [\"C03\", \"C05\", \"C20\"] },\n { \"id\": \"MAP-032\", \"priority\": 320, \"when\": { \"all\": [{ \"criterionId\": \"irreversible_high_impact_actions\", \"equals\": \"yes\" }] }, \"threatId\": \"THR-TOOL-MISUSE\", \"controlId\": \"CTL-AUDIT\", \"requirementLevel\": \"required\", \"interruptsStageIds\": [\"TOOL-3\", \"TOOL-4\"], \"rationale\": \"A high-impact decision needs an attributable chain from identity and policy through approval, parameters and result.\", \"sourceIds\": [\"S03\", \"S04\", \"S06\", \"S12\"], \"claimIds\": [\"C05\", \"C12\", \"C20\"] }\n ]\n}\n", "sha256": "sha256:7df4d82cb258cfe024f6fb015196b60b87ba8b5b057b7bb54adec934d9549b51", "url": "https://github.com/PharosProduction/agent-security-control-mapper/blob/a35b9c30594162eadf4d790d33bd18aaa785bcee/data/mappings.json" }, "data/methodology.json": { "content": "{\n \"version\": \"1.0.0\",\n \"publishedOn\": \"2026-08-21\",\n \"lastVerifiedOn\": \"2026-08-21\",\n \"purpose\": \"Produce a deterministic, evidence-linked control plan from declared AI-agent capabilities and boundaries.\",\n \"statuses\": [\"CONTROL_PLAN_READY\", \"INSUFFICIENT_SCOPE_EVIDENCE\"],\n \"procedure\": [\n \"Require one yes, no or unknown answer for every criterion; never infer an omitted answer as no.\",\n \"Evaluate every mapping relation whose predicates all exactly match the supplied answers.\",\n \"Use the union of all matching mappings and retain every activating mapping ID when more than one relation selects a control.\",\n \"Resolve requirement level to required when any activating mapping is required, otherwise recommended.\",\n \"Order selected controls by first matching mapping priority, then layer order and control order.\",\n \"Return INSUFFICIENT_SCOPE_EVIDENCE and list unresolved inputs when any core answer is unknown; otherwise return CONTROL_PLAN_READY.\",\n \"Export the frozen result record to JSON, CSV or Markdown without re-running or reinterpreting the mapping.\"\n ],\n \"statusesExplained\": {\n \"CONTROL_PLAN_READY\": \"All declared core inputs are known and the deterministic control plan is ready for owner assignment and system-specific review.\",\n \"INSUFFICIENT_SCOPE_EVIDENCE\": \"At least one core capability or boundary remains unknown; the displayed plan is provisional and can omit controls whose conditions cannot yet match.\"\n },\n \"boundaries\": [\n \"Decision support, not certification, compliance, a penetration test, legal advice or proof that a system is secure.\",\n \"The mapper does not produce a security, confidence or percentage score.\",\n \"A suggested owner role is not a real assignment; an accountable person must accept the work in the delivery system.\",\n \"A control description is not implementation evidence. Release gates require artifacts and verification from the deployed environment.\",\n \"Sources define bounded claims and design inputs; their inclusion does not imply OWASP, NIST, MCP or SLSA endorsement.\",\n \"Threat modeling, regulatory interpretation, data classification and impact thresholds remain organization- and system-specific.\"\n ],\n \"registryVersions\": {\n \"criteria\": \"1.0.0\", \"layers\": \"1.0.0\", \"threats\": \"1.0.0\", \"controls\": \"1.0.0\", \"mappings\": \"1.0.0\", \"profiles\": \"1.0.0\", \"incidents\": \"1.0.0\", \"claims\": \"1.0.0\", \"sources\": \"1.0.0\"\n },\n \"exportSchemaVersion\": \"1.0.0\",\n \"maintenance\": {\n \"owner\": \"Pharos Production editorial engineering\",\n \"reviewTrigger\": \"Review when a cited source changes status, a control's evidence contract changes or a new agent capability creates a distinct mapping branch.\",\n \"corrections\": \"Report a factual or mapping correction through the contact path on the published methodology page; accepted changes receive a new registry version and date.\"\n }\n}\n", "sha256": "sha256:a11e0b43695bb227a32d521011e2133e448d11639264947ff04ac993bdcea5a1", "url": "https://github.com/PharosProduction/agent-security-control-mapper/blob/a35b9c30594162eadf4d790d33bd18aaa785bcee/data/methodology.json" }, "data/profiles.json": { "content": "{\n \"version\": \"1.0.0\",\n \"profiles\": [\n {\n \"id\": \"knowledge-assistant\", \"label\": \"Knowledge assistant\", \"description\": \"Reads external and internal knowledge but does not change external state.\",\n \"answers\": { \"untrusted_external_content\": \"yes\", \"sensitive_data\": \"yes\", \"action_capable_tools\": \"no\", \"irreversible_high_impact_actions\": \"no\", \"generated_code_execution\": \"no\", \"persistent_memory\": \"no\", \"third_party_components\": \"yes\", \"mcp_a2a_connections\": \"no\", \"open_network_egress\": \"no\", \"multi_tenancy\": \"yes\", \"multi_agent_delegation\": \"no\" }\n },\n {\n \"id\": \"workflow-agent\", \"label\": \"Workflow agent\", \"description\": \"Uses integrations to update business systems and may require approval for high-impact operations.\",\n \"answers\": { \"untrusted_external_content\": \"yes\", \"sensitive_data\": \"yes\", \"action_capable_tools\": \"yes\", \"irreversible_high_impact_actions\": \"yes\", \"generated_code_execution\": \"no\", \"persistent_memory\": \"yes\", \"third_party_components\": \"yes\", \"mcp_a2a_connections\": \"yes\", \"open_network_egress\": \"no\", \"multi_tenancy\": \"yes\", \"multi_agent_delegation\": \"no\" }\n },\n {\n \"id\": \"coding-agent\", \"label\": \"Coding agent\", \"description\": \"Generates and executes code in an isolated environment with controlled repository and network access.\",\n \"answers\": { \"untrusted_external_content\": \"yes\", \"sensitive_data\": \"yes\", \"action_capable_tools\": \"yes\", \"irreversible_high_impact_actions\": \"yes\", \"generated_code_execution\": \"yes\", \"persistent_memory\": \"no\", \"third_party_components\": \"yes\", \"mcp_a2a_connections\": \"yes\", \"open_network_egress\": \"yes\", \"multi_tenancy\": \"no\", \"multi_agent_delegation\": \"no\" }\n },\n {\n \"id\": \"multi-agent-operator\", \"label\": \"Multi-agent operator\", \"description\": \"Coordinates specialized agents, persistent state, tools and external resources across a shared platform.\",\n \"answers\": { \"untrusted_external_content\": \"yes\", \"sensitive_data\": \"yes\", \"action_capable_tools\": \"yes\", \"irreversible_high_impact_actions\": \"yes\", \"generated_code_execution\": \"yes\", \"persistent_memory\": \"yes\", \"third_party_components\": \"yes\", \"mcp_a2a_connections\": \"yes\", \"open_network_egress\": \"yes\", \"multi_tenancy\": \"yes\", \"multi_agent_delegation\": \"yes\" }\n }\n ]\n}\n", "sha256": "sha256:e17cc5ff3154e7a01a3d4834beec3b8bc8d230178d82a407d6b892e4c2863733", "url": "https://github.com/PharosProduction/agent-security-control-mapper/blob/a35b9c30594162eadf4d790d33bd18aaa785bcee/data/profiles.json" }, "data/sources.json": { "content": "{\n \"version\": \"1.0.0\",\n \"verifiedOn\": \"2026-08-21\",\n \"sources\": [\n {\n \"id\": \"S01\", \"title\": \"Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST AI 100-2e2025)\",\n \"url\": \"https://csrc.nist.gov/pubs/ai/100/2/e2025/final\", \"relatedUrl\": \"https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2025.pdf\", \"sourceType\": \"primary\", \"publisher\": \"National Institute of Standards and Technology\",\n \"publishedOn\": \"2025-03-24\", \"correctedOn\": \"2025-04-01\", \"verifiedOn\": \"2026-08-21\", \"documentStatus\": \"NIST Trustworthy and Responsible AI report\",\n \"limitations\": \"A general adversarial-machine-learning taxonomy; it does not certify an agent implementation or prescribe this mapper's control set.\"\n },\n {\n \"id\": \"S02\", \"title\": \"Strengthening AI Agent Hijacking Evaluations\",\n \"url\": \"https://www.nist.gov/news-events/news/2025/01/technical-blog-strengthening-ai-agent-hijacking-evaluations\", \"sourceType\": \"primary\", \"publisher\": \"National Institute of Standards and Technology\",\n \"publishedOn\": \"2025-01-17\", \"verifiedOn\": \"2026-08-21\", \"documentStatus\": \"NIST technical blog\",\n \"limitations\": \"Evaluation guidance and research framing, not a production assurance standard or proof that a specific control blocks every attack.\"\n },\n {\n \"id\": \"S03\", \"title\": \"OWASP Top 10 for Agentic Applications for 2026\",\n \"url\": \"https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/\", \"sourceType\": \"primary\", \"publisher\": \"OWASP GenAI Security Project\",\n \"publishedOn\": \"2025-12-09\", \"verifiedOn\": \"2026-08-21\", \"documentStatus\": \"Community risk taxonomy\",\n \"limitations\": \"A prioritized risk awareness resource, not certification, endorsement, a complete implementation specification or evidence that a deployment is secure.\"\n },\n {\n \"id\": \"S04\", \"title\": \"Securing Agentic Applications Guide 1.0\",\n \"url\": \"https://genai.owasp.org/resource/securing-agentic-applications-guide-1-0/\", \"sourceType\": \"primary\", \"publisher\": \"OWASP GenAI Security Project\",\n \"publishedOn\": \"2025-07-27\", \"verifiedOn\": \"2026-08-21\", \"documentStatus\": \"Community implementation guide\",\n \"limitations\": \"General guidance that requires system-specific threat modeling, validation and ownership; mapping it here does not create OWASP conformance.\"\n },\n {\n \"id\": \"S05\", \"title\": \"Model Context Protocol Authorization\",\n \"url\": \"https://modelcontextprotocol.io/specification/2026-07-28/basic/authorization\", \"sourceType\": \"primary\", \"publisher\": \"Model Context Protocol\",\n \"publishedOn\": \"2026-07-28\", \"verifiedOn\": \"2026-08-21\", \"documentStatus\": \"Protocol specification, 2026-07-28 revision\",\n \"limitations\": \"Scoped to MCP authorization behavior; it does not cover every application, model, memory, tool or supply-chain control in this mapper.\"\n },\n {\n \"id\": \"S06\", \"title\": \"Accelerating the Adoption of Software and AI Agent Identity and Authorization\",\n \"url\": \"https://csrc.nist.gov/pubs/other/2026/02/05/accelerating-the-adoption-of-software-and-ai-agent/ipd\", \"sourceType\": \"primary\", \"publisher\": \"NIST National Cybersecurity Center of Excellence\",\n \"publishedOn\": \"2026-02-05\", \"verifiedOn\": \"2026-08-21\", \"documentStatus\": \"Initial public draft concept paper\",\n \"limitations\": \"An initial public draft and project concept, not final NIST guidance or a conformity assessment.\"\n },\n {\n \"id\": \"S07\", \"title\": \"Supply-chain Levels for Software Artifacts specification v1.2\",\n \"url\": \"https://slsa.dev/spec/v1.2/\", \"sourceType\": \"primary\", \"publisher\": \"SLSA\",\n \"publishedOn\": \"2025-11-24\", \"verifiedOn\": \"2026-08-21\", \"documentStatus\": \"SLSA specification v1.2\",\n \"limitations\": \"Software-artifact provenance guidance; a provenance statement is useful only when verified against appropriate expectations and a trusted root.\"\n },\n {\n \"id\": \"S08\", \"title\": \"Secure Software Development Practices for Generative AI and Dual-Use Foundation Models (NIST SP 800-218A)\",\n \"url\": \"https://csrc.nist.gov/pubs/sp/800/218/a/final\", \"relatedUrl\": \"https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-218A.pdf\", \"sourceType\": \"primary\", \"publisher\": \"National Institute of Standards and Technology\",\n \"publishedOn\": \"2024-07-26\", \"verifiedOn\": \"2026-08-21\", \"documentStatus\": \"NIST Special Publication\",\n \"limitations\": \"Secure development practices for model producers and acquirers; it does not validate a particular dependency or agent release.\"\n },\n {\n \"id\": \"S09\", \"title\": \"OWASP Top 10 for Large Language Model Applications 2026\",\n \"url\": \"https://genai.owasp.org/resource/owasp-genai-llm-top-10-2026/\", \"sourceType\": \"primary\", \"publisher\": \"OWASP GenAI Security Project\",\n \"publishedOn\": \"2026-08-03\", \"verifiedOn\": \"2026-08-21\", \"documentStatus\": \"Community risk taxonomy\",\n \"limitations\": \"An LLM application risk taxonomy rather than an agent-specific certification, test result or complete control catalog.\"\n },\n {\n \"id\": \"S10\", \"title\": \"CVE-2025-32711: Microsoft 365 Copilot information disclosure vulnerability\",\n \"url\": \"https://msrc.microsoft.com/update-guide/vulnerability/CVE-2025-32711\", \"relatedUrl\": \"https://nvd.nist.gov/vuln/detail/CVE-2025-32711\", \"sourceType\": \"primary\", \"publisher\": \"Microsoft Security Response Center\",\n \"publishedOn\": \"2025-06-11\", \"verifiedOn\": \"2026-08-21\", \"documentStatus\": \"Vendor vulnerability record\",\n \"limitations\": \"A bounded record for one responsibly disclosed vulnerability; proof-of-concept behavior must not be generalized into confirmed exploitation, customer breach or universal control effectiveness.\"\n },\n {\n \"id\": \"S11\", \"title\": \"Breaking down ‘EchoLeak’\",\n \"url\": \"https://www.catonetworks.com/blog/breaking-down-echoleak/\", \"sourceType\": \"researcher disclosure\", \"publisher\": \"Cato Networks / Aim Labs research\",\n \"publishedOn\": \"2025-05-31\", \"verifiedOn\": \"2026-08-21\", \"documentStatus\": \"Researcher technical report\",\n \"limitations\": \"Supports the demonstrated crafted-email, XPIA, markdown-reference and Teams-proxy proof-of-concept chain and quotes Microsoft reporting no affected customers; it does not establish in-the-wild exploitation or a customer breach.\"\n },\n {\n \"id\": \"S12\", \"title\": \"OWASP Artificial Intelligence Security Verification Standard 1.0\",\n \"url\": \"https://owasp.org/www-project-artificial-intelligence-security-verification-standard-aisvs-docs/\", \"sourceType\": \"primary\", \"publisher\": \"OWASP\",\n \"publishedOn\": \"2026-06-24\", \"verifiedOn\": \"2026-08-21\", \"documentStatus\": \"Standard\",\n \"limitations\": \"The listed AISVS 1.0 identifiers are a curated editorial crosswalk to relevant requirements; they are not an AISVS assessment, conformance result, certification or OWASP endorsement.\"\n }\n ]\n}\n", "sha256": "sha256:d2629a5fa19901fec29aa9bba53d5c7a63a825127f1c0f2c8dbb49d47745cfba", "url": "https://github.com/PharosProduction/agent-security-control-mapper/blob/a35b9c30594162eadf4d790d33bd18aaa785bcee/data/sources.json" }, "data/threats.json": { "content": "{\n \"version\": \"1.0.0\",\n \"threats\": [\n { \"id\": \"THR-AUTHORITY-CONFUSION\", \"name\": \"Authority and identity confusion\", \"description\": \"The system acts with authority that is broader, longer-lived or less attributable than the initiating task requires.\", \"attackStages\": [{ \"id\": \"AUTH-1\", \"label\": \"Acquire or reuse authority\" }, { \"id\": \"AUTH-2\", \"label\": \"Cross policy boundary\" }, { \"id\": \"AUTH-3\", \"label\": \"Perform unattributed action\" }] },\n { \"id\": \"THR-INDIRECT-INJECTION\", \"name\": \"Indirect prompt injection\", \"description\": \"Instructions embedded in external content alter agent intent and propagate into a privileged action path.\", \"attackStages\": [{ \"id\": \"INJ-1\", \"label\": \"Plant instruction in content\" }, { \"id\": \"INJ-2\", \"label\": \"Ingest content as context\" }, { \"id\": \"INJ-3\", \"label\": \"Override task intent\" }, { \"id\": \"INJ-4\", \"label\": \"Invoke capability\" }] },\n { \"id\": \"THR-TOOL-MISUSE\", \"name\": \"Unauthorized or malformed tool use\", \"description\": \"A model-selected tool, parameter set or repeated call changes state outside approved intent.\", \"attackStages\": [{ \"id\": \"TOOL-1\", \"label\": \"Select tool\" }, { \"id\": \"TOOL-2\", \"label\": \"Construct arguments\" }, { \"id\": \"TOOL-3\", \"label\": \"Authorize invocation\" }, { \"id\": \"TOOL-4\", \"label\": \"Commit side effect\" }] },\n { \"id\": \"THR-CODE-EGRESS\", \"name\": \"Code execution and data egress\", \"description\": \"Generated code escapes its intended resource boundary or transmits data through an unnecessary network path.\", \"attackStages\": [{ \"id\": \"CODE-1\", \"label\": \"Generate or select code\" }, { \"id\": \"CODE-2\", \"label\": \"Execute workload\" }, { \"id\": \"CODE-3\", \"label\": \"Reach protected resource\" }, { \"id\": \"CODE-4\", \"label\": \"Transmit data\" }] },\n { \"id\": \"THR-MEMORY-POISONING\", \"name\": \"Persistent memory poisoning\", \"description\": \"Untrusted or incorrectly scoped state is stored and later retrieved as decision context.\", \"attackStages\": [{ \"id\": \"MEM-1\", \"label\": \"Submit candidate memory\" }, { \"id\": \"MEM-2\", \"label\": \"Persist state\" }, { \"id\": \"MEM-3\", \"label\": \"Retrieve across time or scope\" }, { \"id\": \"MEM-4\", \"label\": \"Influence later action\" }] },\n { \"id\": \"THR-SUPPLY-COMPROMISE\", \"name\": \"Component supply-chain compromise\", \"description\": \"A package, model, tool, skill or container differs from the reviewed component and gains build or runtime execution.\", \"attackStages\": [{ \"id\": \"SUP-1\", \"label\": \"Publish or substitute component\" }, { \"id\": \"SUP-2\", \"label\": \"Resolve dependency\" }, { \"id\": \"SUP-3\", \"label\": \"Admit component\" }, { \"id\": \"SUP-4\", \"label\": \"Execute in trusted environment\" }] },\n { \"id\": \"THR-CREDENTIAL-EXPOSURE\", \"name\": \"Credential exposure or token forwarding\", \"description\": \"Reusable credentials leak, cross an audience boundary or are forwarded through an intermediary.\", \"attackStages\": [{ \"id\": \"CRED-1\", \"label\": \"Obtain credential material\" }, { \"id\": \"CRED-2\", \"label\": \"Reuse outside intended audience\" }, { \"id\": \"CRED-3\", \"label\": \"Access protected resource\" }] },\n { \"id\": \"THR-CROSS-TENANT\", \"name\": \"Cross-tenant data or action bleed\", \"description\": \"Shared memory, retrieval, tools or credentials expose one tenant's state to another.\", \"attackStages\": [{ \"id\": \"TEN-1\", \"label\": \"Enter shared boundary\" }, { \"id\": \"TEN-2\", \"label\": \"Resolve incorrect tenant scope\" }, { \"id\": \"TEN-3\", \"label\": \"Read or change another tenant's state\" }] },\n { \"id\": \"THR-RUNAWAY-AUTONOMY\", \"name\": \"Runaway or unreviewed autonomy\", \"description\": \"Loops, delegation or chained actions exceed approved duration, cost, action count or human authority.\", \"attackStages\": [{ \"id\": \"RUN-1\", \"label\": \"Start task chain\" }, { \"id\": \"RUN-2\", \"label\": \"Accumulate actions or cost\" }, { \"id\": \"RUN-3\", \"label\": \"Cross impact threshold\" }, { \"id\": \"RUN-4\", \"label\": \"Continue without containment\" }] },\n { \"id\": \"THR-SENSITIVE-DATA-EXPOSURE\", \"name\": \"Sensitive data exposure\", \"description\": \"Sensitive fields enter an agent path without necessary minimization or scoped access and are disclosed through context, persistence, tools or logs.\", \"sourceIds\": [\"S04\", \"S12\"], \"claimIds\": [\"C19\", \"C20\"], \"attackStages\": [{ \"id\": \"DATA-1\", \"label\": \"Access sensitive data\" }, { \"id\": \"DATA-2\", \"label\": \"Place data in model context\" }, { \"id\": \"DATA-3\", \"label\": \"Propagate to memory, tool or log path\" }, { \"id\": \"DATA-4\", \"label\": \"Disclose beyond authorized scope\" }] }\n ]\n}\n", "sha256": "sha256:1afbcfea6ecd153622fb574efd97beaab038ef6bc7071e7f3f32a72d451f9f19", "url": "https://github.com/PharosProduction/agent-security-control-mapper/blob/a35b9c30594162eadf4d790d33bd18aaa785bcee/data/threats.json" } }, "independent_human_review": "NOT_REVIEWED", "pages": "https://pharosproduction.github.io/agent-security-control-mapper/", "repo": "https://github.com/PharosProduction/agent-security-control-mapper", "snapshot_at": "2026-09-17T06:17:24Z" } }