> For the complete documentation index, see [llms.txt](https://www.aicontrolarchitecture.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.aicontrolarchitecture.org/standards-crosswalks/nist-ai-rmf-crosswalk.md). # NIST AI RMF ## NIST AI RMF Crosswalk This document maps the AI Control Architecture to the NIST AI Risk Management Framework. The purpose of this crosswalk is to show how the AI Control Architecture can help enterprises operationalize the NIST AI RMF through practical controls, evidence, assurance, and incident readiness. The AI Control Architecture is not a replacement for the NIST AI RMF. It is an implementation layer that helps translate AI risk management outcomes into enterprise control activities. *** ## 1. Positioning The NIST AI Risk Management Framework provides a structured way to manage AI risks. The AI Control Architecture helps answer: ``` How do we implement those risk management expectations inside enterprise systems, data, identity, workflows, vendors, assurance, and incident response? ``` NIST AI RMF focuses on managing AI risk. AI Control Architecture focuses on operational control. Together, they can be used as: ``` NIST AI RMF = Risk management structure AI Control Architecture = Control implementation layer ``` *** ## 2. NIST AI RMF Core Functions The NIST AI RMF Core is organized around four high-level functions: ``` Govern Map Measure Manage ``` These functions help organizations establish AI risk governance, understand AI context and risks, assess and monitor risk, and prioritize and respond to risk. *** ## 3. AI Control Architecture Pillars The AI Control Architecture uses ten pillars: ``` 1. AI inventory and classification 2. AI identity and access control 3. Data boundary control 4. Prompt and input control 5. Output and decision control 6. Tool and action control 7. Human accountability model 8. AI assurance and testing 9. Monitoring, logging, and evidence 10. Incident containment and recovery ``` These pillars provide the operational control structure that can support NIST AI RMF outcomes. *** ## 4. High-Level Crosswalk | NIST AI RMF Function | Primary AI Control Architecture Support | | -------------------- | ------------------------------------------------------------------------------------------------------------ | | Govern | AI inventory and classification; human accountability; governance and operating model; metrics and reporting | | Map | AI inventory; risk tiering; data boundary; identity and access; AI pattern classification; vendor AI review | | Measure | AI assurance and testing; output validation; monitoring, logging, and evidence; maturity model | | Manage | incident containment and recovery; tool/action control; exception management; remediation; risk acceptance | *** ## 5. Govern Function Crosswalk ### NIST AI RMF Intent The Govern function establishes the organizational structures, policies, processes, roles, responsibilities, and culture needed to manage AI risk. ### AI Control Architecture Implementation Interpretation The AI Control Architecture operationalizes Govern by requiring that AI use cases are visible, owned, risk-tiered, governed through clear decision rights, and connected to enterprise operating processes. ### Relevant AI Control Architecture Pillars | Pillar | Contribution to Govern | | --------------------------------- | ------------------------------------------------------------------------------------------ | | AI inventory and classification | Establishes visibility of AI use cases and lifecycle status. | | Human accountability model | Assigns business, technical, data, decision, risk, vendor, assurance, and incident owners. | | Monitoring, logging, and evidence | Provides governance evidence and reporting data. | | Incident containment and recovery | Defines ownership for AI incident response and recovery. | | AI assurance and testing | Provides governance confidence through control validation. | ### Implementation Activities ``` Create AI inventory. Assign business owners. Assign technical owners. Assign data owners where required. Assign decision owners where AI influences decisions. Assign vendor owners where vendor AI is involved. Define AI risk tiers. Define governance forums and decision rights. Define exception management. Define incident ownership. Define reporting cadence. Track metrics and maturity. ``` ### Example Evidence ``` AI inventory AI use case intake record Risk tier record Ownership record RACI matrix Governance meeting record Exception record Risk acceptance record Maturity report Metrics dashboard Incident ownership record ``` ### Related Project Files ``` docs/06-requirements-catalogue.md docs/18-control-maturity-model.md docs/21-adoption-playbook.md docs/22-governance-and-operating-model.md docs/23-metrics-and-reporting.md templates/ai-use-case-intake-template.md templates/ai-risk-assessment-template.md templates/ai-control-assessment-template.md templates/ai-exception-record-template.md ``` *** ## 6. Map Function Crosswalk ### NIST AI RMF Intent The Map function helps organizations understand the context, intended purpose, users, benefits, risks, impacts, and system characteristics of AI use. ### AI Control Architecture Implementation Interpretation The AI Control Architecture operationalizes Map by forcing enterprises to define what AI exists, what pattern it follows, what data it can access, what decisions it may influence, what tools or actions it can use, and which risks apply. ### Relevant AI Control Architecture Pillars | Pillar | Contribution to Map | | ------------------------------- | ------------------------------------------------------------------------------------- | | AI inventory and classification | Captures AI use case, lifecycle, pattern, owner, and risk tier. | | AI identity and access control | Maps identities, authority, delegated access, and revocation. | | Data boundary control | Maps data sources, classifications, retrieval boundaries, retention, and reuse. | | Prompt and input control | Maps input surfaces, external content, uploaded files, and prompt injection exposure. | | Output and decision control | Maps outputs, decisions, generated records, and downstream use. | | Tool and action control | Maps tools, APIs, workflows, action types, approval gates, and blast radius. | | Human accountability model | Maps owners, reviewers, approvers, decision owners, and risk owners. | ### Implementation Activities ``` Identify AI use case. Classify AI pattern. Assign lifecycle status. Identify user population. Identify vendor involvement. Map data sources. Classify data sensitivity. Map retrieval boundaries. Map prompt and input surfaces. Map output types. Identify decision impact. Identify tool and action capability. Assign initial risk tier. Identify affected stakeholders. Identify failure scenarios. ``` ### Example Evidence ``` AI use case intake AI inventory record AI pattern classification Risk assessment Data source map Data classification record Retrieval boundary record Prompt/input control record Output classification Decision impact assessment Tool inventory Action classification Vendor assessment Stakeholder map ``` ### Related Project Files ``` QUICKSTART.md docs/17-implementation-checklists.md docs/19-common-ai-control-patterns.md docs/20-common-failure-scenarios.md templates/ai-use-case-intake-template.md templates/ai-risk-assessment-template.md templates/ai-inventory-record-template.md templates/ai-risk-tiering-template.md templates/ai-data-boundary-template.md templates/ai-vendor-assessment-template.md ``` *** ## 7. Measure Function Crosswalk ### NIST AI RMF Intent The Measure function focuses on analyzing, assessing, benchmarking, testing, monitoring, and tracking AI risks and trustworthiness characteristics. ### AI Control Architecture Implementation Interpretation The AI Control Architecture operationalizes Measure by defining assurance tests, control tests, evidence requirements, monitoring expectations, metrics, maturity assessment, and reconstructability checks. ### Relevant AI Control Architecture Pillars | Pillar | Contribution to Measure | | --------------------------------- | ------------------------------------------------------------------------------ | | AI assurance and testing | Defines testing of AI behavior and control effectiveness. | | Monitoring, logging, and evidence | Defines logs, evidence, retention, event taxonomy, and reconstructability. | | Output and decision control | Defines output validation and decision evidence. | | Prompt and input control | Defines prompt injection and input control testing. | | Data boundary control | Defines retrieval boundary and data leakage testing. | | Tool and action control | Defines unauthorized tool use, approval gate, and rollback testing. | | Incident containment and recovery | Defines containment tests, kill switch tests, and incident tabletop exercises. | ### Implementation Activities ``` Define assurance scope. Create assurance test plan. Test data boundaries. Test retrieval boundaries. Test prompt injection. Test output validation. Test decision evidence. Test tool/action controls. Test approval gates. Test logging completeness. Test evidence reconstruction. Test kill switch. Test rollback or compensation. Track findings. Track maturity metrics. Track incidents and near misses. ``` ### Example Evidence ``` Assurance test plan Test cases Test results Prompt injection test results Retrieval boundary test results Data leakage test results Output validation evidence Decision evidence Tool/action test results Approval gate test results Logging completeness test Evidence reconstruction test Kill switch test Rollback test Findings register Maturity assessment Metrics dashboard ``` ### Related Project Files ``` docs/18-control-maturity-model.md docs/23-metrics-and-reporting.md docs/24-assurance-and-audit-guide.md templates/ai-assurance-test-plan-template.md templates/ai-control-evidence-package-template.md templates/ai-control-assessment-template.md ``` *** ## 8. Manage Function Crosswalk ### NIST AI RMF Intent The Manage function focuses on prioritizing, responding to, treating, monitoring, and improving risk management activities based on measured and mapped AI risks. ### AI Control Architecture Implementation Interpretation The AI Control Architecture operationalizes Manage by requiring risk-tiered controls, remediation, exception management, incident containment, recovery, restart criteria, vendor remediation, and continuous improvement. ### Relevant AI Control Architecture Pillars | Pillar | Contribution to Manage | | --------------------------------- | --------------------------------------------------------------------- | | AI inventory and classification | Prioritizes use cases by risk tier and lifecycle status. | | Tool and action control | Limits high-risk actions and supports containment. | | Human accountability model | Assigns risk acceptance, remediation, and incident ownership. | | AI assurance and testing | Produces findings and retest requirements. | | Monitoring, logging, and evidence | Supports ongoing review and detection. | | Incident containment and recovery | Defines containment, recovery, restart criteria, and lessons learned. | ### Implementation Activities ``` Prioritize high-risk AI. Apply minimum controls by risk tier. Approve, conditionally approve, defer, or reject AI use cases. Track findings. Assign remediation owners. Approve exceptions where justified. Define compensating controls. Accept residual risk where appropriate. Contain AI incidents. Disable AI capabilities where required. Revoke AI identities or tool access. Correct outputs, records, or workflows. Conduct post-incident review. Update controls and tests. Update roadmap and maturity plan. ``` ### Example Evidence ``` Risk-tiered control baseline Control assessment Approval decision Conditional approval record Finding record Remediation plan Exception record Risk acceptance record Incident record Containment evidence Recovery evidence Restart approval Post-incident review Updated requirement Updated assurance test ``` ### Related Project Files ``` docs/17-implementation-checklists.md docs/21-adoption-playbook.md docs/22-governance-and-operating-model.md docs/23-metrics-and-reporting.md docs/24-assurance-and-audit-guide.md templates/ai-control-assessment-template.md templates/ai-exception-record-template.md templates/ai-incident-record-template.md templates/ai-incident-containment-recovery-template.md ``` *** ## 9. Pillar-to-NIST Function Matrix | AI Control Architecture Pillar | Govern | Map | Measure | Manage | | --------------------------------- | ---------: | ---------: | ---------: | ---------: | | AI inventory and classification | Primary | Primary | Supporting | Primary | | AI identity and access control | Supporting | Primary | Supporting | Supporting | | Data boundary control | Supporting | Primary | Primary | Supporting | | Prompt and input control | Supporting | Primary | Primary | Supporting | | Output and decision control | Supporting | Primary | Primary | Supporting | | Tool and action control | Supporting | Primary | Primary | Primary | | Human accountability model | Primary | Primary | Supporting | Primary | | AI assurance and testing | Supporting | Supporting | Primary | Primary | | Monitoring, logging, and evidence | Primary | Supporting | Primary | Primary | | Incident containment and recovery | Supporting | Supporting | Primary | Primary | *** ## 10. NIST Function to Example AI Control Activities ### Govern | Control Activity | Example Evidence | | -------------------------- | -------------------- | | Define AI governance roles | RACI matrix | | Maintain AI inventory | AI inventory record | | Assign AI owners | ownership record | | Define AI risk tiers | risk tiering model | | Define exception process | exception record | | Report AI metrics | governance dashboard | *** ### Map | Control Activity | Example Evidence | | ------------------------------- | -------------------------- | | Identify AI pattern | intake record | | Map data sources | data source map | | Identify decision impact | decision impact assessment | | Identify tool/action capability | tool inventory | | Identify vendor involvement | vendor assessment | | Identify affected stakeholders | use case intake | *** ### Measure | Control Activity | Example Evidence | | ---------------------------- | ---------------------------- | | Test retrieval boundary | retrieval test result | | Test prompt injection | prompt injection test result | | Test output validation | output validation result | | Test approval gate | approval gate test result | | Test logging completeness | logging test result | | Test evidence reconstruction | reconstruction test result | *** ### Manage | Control Activity | Example Evidence | | ------------------------------ | --------------------------- | | Apply controls by risk tier | control assessment | | Track findings | findings register | | Approve exceptions | exception record | | Accept residual risk | risk acceptance record | | Contain incident | incident containment record | | Update controls after incident | post-incident review | *** ## 11. Example: Mapping a RAG Assistant ### Use Case ``` Internal policy knowledge assistant using retrieval-augmented generation. ``` ### NIST Function Mapping | NIST Function | AI Control Architecture Implementation | | ------------- | -------------------------------------------------------------------------------------------------------------- | | Govern | Assign business owner, technical owner, data owner, and governance review. | | Map | Map approved policy sources, data classifications, retrieval boundaries, users, and decision impact. | | Measure | Test retrieval boundaries, prompt injection, source attribution, output accuracy, and evidence reconstruction. | | Manage | Restrict retrieval sources, remediate findings, disable unsafe sources, track incidents, and update controls. | ### Example Evidence ``` AI use case intake Data source map Data owner approval Retrieval boundary test Prompt injection test Output validation result Evidence reconstruction result Incident containment path ``` Related example: ``` examples/rag-assistant-example.md ``` *** ## 12. Example: Mapping an AI Agent ### Use Case ``` IT service desk ticket triage agent. ``` ### NIST Function Mapping | NIST Function | AI Control Architecture Implementation | | ------------- | -------------------------------------------------------------------------------------------------------------------------- | | Govern | Assign business owner, technical owner, security owner, incident owner, and approval authority. | | Map | Map agent identity, autonomy level, tools, action types, data sources, and blast radius. | | Measure | Test prompt injection, tool misuse, approval bypass, logging completeness, kill switch, and evidence reconstruction. | | Manage | Enforce approval gates, restrict tools, disable agent identity, roll back incorrect routing, and update incident playbook. | ### Example Evidence ``` Agent control record Tool inventory Action classification Approval gate configuration Tool/action test results Kill switch test Rollback test Incident containment plan ``` Related example: ``` examples/agentic-ai-example.md ``` *** ## 13. How to Use This Crosswalk Use this crosswalk when: ``` An enterprise wants to align AI control work with NIST AI RMF. A risk or compliance team asks how the architecture maps to NIST. An assurance team wants to translate NIST functions into evidence. An architecture team wants to show that AI control design supports recognized AI risk management practices. A governance team wants to avoid creating a competing framework. ``` Suggested use: ``` 1. Identify the AI use case. 2. Complete intake and risk tiering. 3. Map the use case to Govern, Map, Measure, and Manage. 4. Identify which AI Control Architecture pillars apply. 5. Identify required evidence. 6. Identify assurance tests. 7. Track findings, exceptions, and incidents. 8. Report maturity and improvement. ``` *** ## 14. Limitations This crosswalk is intended to support AI risk management and control implementation. It is not: ``` A formal NIST certification A compliance guarantee A legal opinion An audit opinion A complete mapping to every NIST AI RMF category or subcategory ``` Organizations should tailor this crosswalk to their own governance, risk, compliance, legal, privacy, audit, and assurance requirements. *** ## 15. Future Enhancements Future versions may add: ``` Detailed mapping to NIST AI RMF categories and subcategories Mapping to NIST AI RMF Playbook actions Mapping to NIST AI 600-1 Generative AI Profile Machine-readable crosswalk format Evidence catalogue aligned to NIST functions Assurance test catalogue aligned to NIST functions ``` *** ## 16. Summary The NIST AI RMF helps organizations structure AI risk management. The AI Control Architecture helps enterprises implement that structure through practical controls. 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