NIST AI Risk Management Framework 1.0 + Playbook

NIST AI Risk Management Framework 1.0 + Playbook — 93% covered.

101 requirements · 88 enforced · 9 designed · 4 advisory · 0 deferred.

Source: NIST AI 100-1 (January 2023) + AI RMF Playbook · License: US Government — public domain

By category

Category	Reqs	Enforced	Designed	Advisory	Coverage
Govern (GV)	31	24	5	2	87%
Map (MP)	23	19	3	1	90%
Measure (MS)	27	26	1	0	98%
Manage (MG)	20	19	0	1	96%

Every requirement → the KYE™ artefact that enforces it

ID	Title	Status	KYE™ enforcement
`nist-ai-rmf.GV.1.4`	Govern 1.4 — The risk-management process and its outcomes are established through transparent policies, procedures, and other controls	enforced	audit_events: `kye.compliance.attestation.v1`, `kye.purpose.permission.v1` constitution_refs: `constitution/40-IMPLEMENTATION-CANONICAL.md`
`nist-ai-rmf.GV.1.5`	Govern 1.5 — Ongoing monitoring and periodic review of the risk-management process and its outcomes are planned, and organizational roles and responsibilities clearly defined	enforced	audit_events: `kye.assurance.audit_pilot.v1`, `kye.change_calendar.v1` constitution_refs: `constitution/21-DELEGATED-AUDITABILITY.md`
`nist-ai-rmf.GV.1.6`	Govern 1.6 — Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities	enforced	audit_events: `kye.entity.model.v1`, `kye.entity.model_endpoint.v1` engines: `internal` constitution_refs: `constitution/14-AGENTS-AND-ENGINES.md`
`nist-ai-rmf.GV.1.7`	Govern 1.7 — Processes and procedures are in place for decommissioning and phasing out of AI systems safely and in a manner that does not increase risks or decrease the organization's trustworthiness	enforced	audit_events: `kye.purpose.grant.revoked.v1`, `kye.signal.revocation.cascaded.v1` engines: `internal`, `internal` constitution_refs: `constitution/12-PURPOSE-PERMISSION.md`
`nist-ai-rmf.GV.2.2`	Govern 2.2 — The organization's personnel and partners receive AI risk-management training to enable them to perform their duties and responsibilities	designed	constitution_refs: `constitution/39-LEARN-RAIL.md`, `constitution/10-PARTNER.md`
`nist-ai-rmf.GV.2.3`	Govern 2.3 — Executive leadership of the organization takes responsibility for decisions about risks associated with AI-system development and deployment	enforced	audit_events: `kye.risk.authority_register.v1`, `kye.approval_decision.v1` engines: `internal` governedui_modules: `kye.governedui.module.action_approval.v1` constitution_refs: `constitution/36-GOVERNEDUI.md`
`nist-ai-rmf.GV.3.2`	Govern 3.2 — Policies and procedures are in place to define and differentiate roles and responsibilities for human-AI configurations and oversight	enforced	audit_events: `kye.purpose.permission.v1` engines: `internal` governedui_modules: `kye.governedui.module.approval_queue.v1` constitution_refs: `constitution/36-GOVERNEDUI.md`
`nist-ai-rmf.GV.4.2`	Govern 4.2 — Organizational teams document the risks and potential impacts of AI technology they design, develop, deploy, evaluate, and use	enforced	audit_events: `kye.consequence_map.v1`, `kye.risk_assessment.v1` engines: `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.GV.4.3`	Govern 4.3 — Organizational practices are in place to enable AI testing, identification of incidents, and information sharing	enforced	audit_events: `kye.scenario_run.v1`, `kye.signal.incident.opened.v1` engines: `internal`, `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.GV.5.2`	Govern 5.2 — Mechanisms are established to enable the team that develops or deploys AI systems to regularly incorporate adjudicated feedback from relevant AI actors into system design and implementation	designed	audit_events: `kye.signal.drift.detected.v1`, `kye.approval_decision.v1` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.GV.6.2`	Govern 6.2 — Contingency processes are in place to handle failures or incidents in third-party data or AI systems deemed to be high-risk	enforced	audit_events: `kye.spof_registry.v1`, `kye.signal.incident.opened.v1` engines: `internal` constitution_refs: `constitution/51-NO-SPOF.md`
`nist-ai-rmf.MP.1.2`	Map 1.2 — Inter-disciplinary AI actors, competencies, skills and capacities for establishing context reflect demographic diversity and broad domain and user experience expertise	advisory	audit_events: `kye.stakeholder.v1` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MP.1.3`	Map 1.3 — The organization's mission and relevant goals for AI technology are understood and documented	enforced	audit_events: `kye.purpose_manifest.v1` engines: `internal` constitution_refs: `constitution/12-PURPOSE-PERMISSION.md`
`nist-ai-rmf.MP.1.4`	Map 1.4 — The business value or context of business use has been clearly defined or — in the case of assessing existing AI systems — re-evaluated	enforced	audit_events: `kye.purpose.permission.v1`, `kye.operating_model.spec.v1` constitution_refs: `constitution/18-OPERATING-MODEL.md`
`nist-ai-rmf.MP.1.5`	Map 1.5 — Organizational risk tolerances are determined and documented	enforced	audit_events: `kye.risk.authority_register.v1` engines: `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MP.1.6`	Map 1.6 — System requirements (e.g., 'the system shall respect the privacy of its users') are elicited from and understood by relevant AI actors	enforced	audit_events: `kye.model.capability_profile.v1` constitution_refs: `constitution/14-AGENTS-AND-ENGINES.md`
`nist-ai-rmf.MP.2.2`	Map 2.2 — Information about the AI system's knowledge limits and how system output may be utilized and overseen by humans is documented	enforced	audit_events: `kye.model.capability_profile.v1`, `kye.model.influence_envelope.v1` constitution_refs: `constitution/14-AGENTS-AND-ENGINES.md`
`nist-ai-rmf.MP.2.3`	Map 2.3 — Scientific integrity and TEVV considerations are identified and documented	enforced	audit_events: `kye.evidence.trace_replay_spec.v1`, `kye.assurance.audit_replay_report.v1` engines: `internal` constitution_refs: `constitution/21-DELEGATED-AUDITABILITY.md`
`nist-ai-rmf.MP.3.2`	Map 3.2 — Potential costs, including non-monetary costs, that result from expected or realized AI errors or system functionality and trustworthiness are examined and documented	enforced	audit_events: `kye.consequence_map.v1` engines: `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MP.3.3`	Map 3.3 — Targeted application scope is specified and documented	enforced	audit_events: `kye.scope.v1`, `kye.purpose.permission.v1` constitution_refs: `constitution/12-PURPOSE-PERMISSION.md`
`nist-ai-rmf.MP.3.4`	Map 3.4 — Processes for operator and practitioner proficiency with AI system performance and trustworthiness are defined, assessed, and documented	designed	constitution_refs: `constitution/10-PARTNER.md`, `constitution/39-LEARN-RAIL.md`
`nist-ai-rmf.MP.3.5`	Map 3.5 — Processes for human oversight are defined, assessed, and documented in accordance with organizational policies	enforced	audit_events: `kye.purpose.permission.v1`, `kye.approval_decision.v1` governedui_modules: `kye.governedui.module.action_approval.v1`, `kye.governedui.module.approval_queue.v1` constitution_refs: `constitution/36-GOVERNEDUI.md`
`nist-ai-rmf.MP.4.2`	Map 4.2 — Internal risk controls for components of the AI system, including third-party AI technologies, are identified and documented	enforced	audit_events: `kye.subprocessor.v1`, `kye.federation.cross_org_delegation.v1` constitution_refs: `constitution/12-PURPOSE-PERMISSION.md`, `constitution/51-NO-SPOF.md`
`nist-ai-rmf.MP.5.2`	Map 5.2 — Practices and personnel for supporting regular engagement with relevant AI actors and integrating feedback about positive, negative, and unanticipated impacts are in place and documented	designed	audit_events: `kye.comms.dispatch.v1`, `kye.signal.incident.opened.v1` constitution_refs: `constitution/38-COMMS-RAIL.md`
`nist-ai-rmf.MS.1.2`	Measure 1.2 — Appropriateness of AI metrics and effectiveness of existing controls are regularly assessed	enforced	audit_events: `kye.scenario_run.v1`, `kye.signal.drift.detected.v1` engines: `internal`, `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MS.1.3`	Measure 1.3 — Internal experts who did not serve as front-line developers for the system and/or independent assessors are involved in regular assessments and updates	enforced	audit_events: `kye.assurance.audit_pilot.v1` agents: `internal` constitution_refs: `constitution/21-DELEGATED-AUDITABILITY.md`
`nist-ai-rmf.MS.2.2`	Measure 2.2 — Evaluations involving human subjects meet applicable requirements (including human-subject protection) and are representative of the relevant population	enforced	audit_events: `kye.consent.acceptance.v1`, `kye.data_use_manifest.v1` constitution_refs: `constitution/31-DATA-GOVERNANCE-PACK.md`
`nist-ai-rmf.MS.2.3`	Measure 2.3 — AI-system performance or assurance criteria are measured qualitatively or quantitatively and demonstrated for conditions similar to deployment setting	enforced	audit_events: `kye.scenario_run.v1`, `kye.assurance.audit_replay_report.v1` engines: `internal` constitution_refs: `constitution/21-DELEGATED-AUDITABILITY.md`
`nist-ai-rmf.MS.2.4`	Measure 2.4 — The functionality and behavior of the AI system and its components — as identified in the MAP function — are monitored when in production	enforced	audit_events: `kye.audit_chain_entry.v1`, `kye.signal.drift.detected.v1` engines: `internal`, `internal` constitution_refs: `constitution/35-STREAMING-LOGS.md`
`nist-ai-rmf.MS.2.6`	Measure 2.6 — AI system is evaluated regularly for safety risks — as identified in the MAP function	enforced	audit_events: `kye.risk.score.v1`, `kye.scenario_run.v1` engines: `internal`, `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MS.2.8`	Measure 2.8 — Risks associated with transparency and accountability — as identified in the MAP function — are examined and documented	enforced	audit_events: `kye.evidence.decision_map.v1`, `kye.evidence.pack.v1` engines: `internal` constitution_refs: `constitution/21-DELEGATED-AUDITABILITY.md`
`nist-ai-rmf.MS.2.9`	Measure 2.9 — The AI model is explained, validated, and documented, and AI system output is interpreted within its context — as identified in the MAP function — and to inform responsible use and governance	enforced	audit_events: `kye.evidence.decision_map.v1`, `kye.model.capability_profile.v1` constitution_refs: `constitution/36-GOVERNEDUI.md`
`nist-ai-rmf.MS.2.10`	Measure 2.10 — Privacy risk of the AI system — as identified in the MAP function — is examined and documented	enforced	audit_events: `kye.data_use_manifest.v1`, `kye.dsar_evidence_pack.v1` engines: `internal` constitution_refs: `constitution/31-DATA-GOVERNANCE-PACK.md`
`nist-ai-rmf.MS.2.11`	Measure 2.11 — Fairness and bias — as identified in the MAP function — are evaluated and results are documented	enforced	audit_events: `kye.scenario_run.v1`, `kye.evidence.pack.v1` engines: `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MS.2.12`	Measure 2.12 — Environmental impact and sustainability of AI model training and management activities — as identified in the MAP function — are assessed and documented	designed	audit_events: `kye.evidence.model_params.v1` constitution_refs: `constitution/14-AGENTS-AND-ENGINES.md`
`nist-ai-rmf.MS.2.13`	Measure 2.13 — Effectiveness of the employed TEVV metrics and processes in the MEASURE function are evaluated and documented	enforced	audit_events: `kye.assurance.audit_replay_report.v1` engines: `internal` constitution_refs: `constitution/21-DELEGATED-AUDITABILITY.md`
`nist-ai-rmf.MS.3.2`	Measure 3.2 — Risk tracking approaches are considered for settings where AI risks are difficult to assess using currently available measurement techniques or where metrics are not yet available	enforced	audit_events: `kye.risk.score.v1`, `kye.compliance.attestation.v1` engines: `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MS.3.3`	Measure 3.3 — Feedback processes for end users and impacted communities to report problems and appeal system outcomes are established and integrated into AI system evaluation metrics	enforced	audit_events: `kye.signal.incident.opened.v1`, `kye.comms.dispatch.v1` engines: `internal` constitution_refs: `constitution/38-COMMS-RAIL.md`
`nist-ai-rmf.MS.4.2`	Measure 4.2 — Measurement results regarding AI system trustworthiness in deployment context(s) and across the AI lifecycle are informed by input from domain experts and relevant AI actors to validate whether the system is performing consistently as intended	enforced	audit_events: `kye.assurance.audit_replay_report.v1`, `kye.approval_decision.v1` constitution_refs: `constitution/21-DELEGATED-AUDITABILITY.md`
`nist-ai-rmf.MG.1.4`	Manage 1.4 — Negative residual risks (defined as the sum of all unmitigated risks) to both downstream acquirers of AI systems and end users are documented	enforced	audit_events: `kye.risk.score.v1`, `kye.compliance.attestation.v1` engines: `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MG.2.2`	Manage 2.2 — Mechanisms are in place and applied to sustain the value of deployed AI systems	enforced	audit_events: `kye.change_calendar.v1`, `kye.signal.drift.detected.v1` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MG.2.3`	Manage 2.3 — Procedures are followed to respond to and recover from a previously unknown risk when it is identified	enforced	audit_events: `kye.signal.incident.opened.v1`, `kye.signal.incident.closed.v1` engines: `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MG.2.4`	Manage 2.4 — Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use	enforced	audit_events: `kye.purpose.grant.revoked.v1`, `kye.signal.revocation.cascaded.v1` engines: `internal`, `internal` constitution_refs: `constitution/12-PURPOSE-PERMISSION.md`
`nist-ai-rmf.MG.3.2`	Manage 3.2 — Pre-trained models which are used for development are monitored as part of AI system regular monitoring and maintenance	enforced	audit_events: `kye.entity.model.v1`, `kye.signal.drift.detected.v1` engines: `internal` constitution_refs: `constitution/14-AGENTS-AND-ENGINES.md`
`nist-ai-rmf.MG.4.2`	Manage 4.2 — Measurable activities for continual improvements are integrated into AI system updates and include regular engagement with interested parties, including relevant AI actors	enforced	audit_events: `kye.signal.drift.detected.v1`, `kye.compliance.attestation.v1` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MG.4.4`	Manage 4.4 — AI risks and benefits from third-party resources are regularly monitored, and risk controls are applied and documented	enforced	audit_events: `kye.subprocessor.v1`, `kye.spof_registry.v1` constitution_refs: `constitution/51-NO-SPOF.md`
`nist-ai-rmf.GV.OC-01.2`	Playbook GV.OC-1 — Establish AI organisational risk-tolerance statements (sub-action)	enforced	audit_events: `kye.risk.authority_register.v1` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.GV.OC-01.3`	Playbook GV.OC-1 — Document organisational risk-tolerance baseline and decay clock (sub-action)	enforced	audit_events: `kye.compliance.attestation.v1` constitution_refs: `constitution/DECAY-WINDOWS.md`
`nist-ai-rmf.MP.CT-01.2`	Playbook MP.CT-1 — Document intended purposes and beneficiaries of the AI system (sub-action)	enforced	audit_events: `kye.purpose_manifest.v1` constitution_refs: `constitution/12-PURPOSE-PERMISSION.md`
`nist-ai-rmf.MS.AI-01.2`	Playbook MS.AI-1 — Establish thresholds for trustworthiness measurements (sub-action)	enforced	audit_events: `kye.scenario.v1`, `kye.risk.score.v1` engines: `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MG.RR-01.2`	Playbook MG.RR-1 — Document residual risk and acceptance criteria per principal (sub-action)	enforced	audit_events: `kye.risk.authority_register.v1`, `kye.approval_decision.v1` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.GV.PO-02.1`	Playbook GV.PO-2 — AI-specific procurement policies (sub-action)	designed	audit_events: `kye.subprocessor.v1` constitution_refs: `constitution/26-COMMERCIAL.md`
`nist-ai-rmf.GV.AC-01.1`	Playbook GV.AC-1 — Roles and responsibilities for AI accountability (sub-action)	enforced	audit_events: `kye.purpose.permission.v1`, `kye.risk.authority_register.v1` engines: `internal` constitution_refs: `constitution/12-PURPOSE-PERMISSION.md`
`nist-ai-rmf.GV.AC-02.1`	Playbook GV.AC-2 — Periodic competency assessment of AI actors (sub-action)	designed	constitution_refs: `constitution/10-PARTNER.md`
`nist-ai-rmf.GV.TM-01.1`	Playbook GV.TM-1 — Multi-disciplinary team membership for AI risk management (sub-action)	advisory	audit_events: `kye.stakeholder.v1` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.GV.SK-01.1`	Playbook GV.SK-1 — Strategies for engaging stakeholders throughout AI lifecycle (sub-action)	enforced	audit_events: `kye.stakeholder.v1`, `kye.comms.dispatch.v1` constitution_refs: `constitution/38-COMMS-RAIL.md`
`nist-ai-rmf.MP.CR-01.1`	Playbook MP.CR-1 — Categorise AI system by capability, end users, and deployment context (sub-action)	enforced	audit_events: `kye.model.capability_profile.v1`, `kye.entity.model.v1` constitution_refs: `constitution/14-AGENTS-AND-ENGINES.md`
`nist-ai-rmf.MP.IM-01.1`	Playbook MP.IM-1 — Identify positive and negative impacts (sub-action)	enforced	audit_events: `kye.consequence_map.v1` engines: `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MP.RA-01.1`	Playbook MP.RA-1 — Likelihood and impact mapping per identified risk (sub-action)	enforced	audit_events: `kye.risk.score.v1` engines: `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MS.ME-01.1`	Playbook MS.ME-1 — Use approved methods and metrics for measurement (sub-action)	enforced	audit_events: `kye.scenario.v1` engines: `internal` constitution_refs: `constitution/21-DELEGATED-AUDITABILITY.md`
`nist-ai-rmf.MS.DC-01.1`	Playbook MS.DC-1 — Document measurement results and limitations (sub-action)	enforced	audit_events: `kye.assurance.audit_replay_report.v1`, `kye.evidence.pack.v1` constitution_refs: `constitution/21-DELEGATED-AUDITABILITY.md`
`nist-ai-rmf.MG.IM-01.1`	Playbook MG.IM-1 — Document risk-management decisions (sub-action)	enforced	audit_events: `kye.evidence.decision_map.v1`, `kye.approval_decision.v1` constitution_refs: `constitution/21-DELEGATED-AUDITABILITY.md`
`nist-ai-rmf.MG.RT-01.1`	Playbook MG.RT-1 — Risk treatment selection per identified risk (sub-action)	enforced	audit_events: `kye.risk.score.v1`, `kye.purpose.admissibility.v1` engines: `internal`, `internal` constitution_refs: `constitution/12-PURPOSE-PERMISSION.md`
`nist-ai-rmf.MG.CO-01.1`	Playbook MG.CO-1 — Communicate risk-management outcomes to relevant AI actors (sub-action)	enforced	audit_events: `kye.comms.dispatch.v1`, `kye.compliance.attestation.v1` constitution_refs: `constitution/38-COMMS-RAIL.md`
`nist-ai-rmf.GV.AC-03.1`	Playbook GV.AC-3 — Maintain audit-evidence trails for accountability (sub-action)	enforced	audit_events: `kye.audit_chain_entry.v1`, `kye.audit_retention_policy.v1` engines: `internal` constitution_refs: `constitution/30-AUDIT-WORM-RETENTION.md`
`nist-ai-rmf.GV.PO-03.1`	Playbook GV.PO-3 — Periodic policy review (sub-action)	enforced	audit_events: `kye.change_calendar.v1` constitution_refs: `constitution/DECAY-WINDOWS.md`
`nist-ai-rmf.MS.AI-02.1`	Playbook MS.AI-2 — Evaluate AI-system trustworthiness against the seven trustworthy-AI characteristics (sub-action)	enforced	audit_events: `kye.assurance.audit_replay_report.v1`, `kye.scenario_run.v1` constitution_refs: `constitution/21-DELEGATED-AUDITABILITY.md`
`nist-ai-rmf.MS.RD-01.1`	Playbook MS.RD-1 — Recurrent measurement cadence (sub-action)	enforced	audit_events: `kye.change_calendar.v1`, `kye.compliance.attestation.v1` constitution_refs: `constitution/DECAY-WINDOWS.md`
`nist-ai-rmf.MG.MR-01.1`	Playbook MG.MR-1 — Monitor risk after deployment (sub-action)	enforced	audit_events: `kye.signal.drift.detected.v1`, `kye.audit_chain_entry.v1` engines: `internal` constitution_refs: `constitution/35-STREAMING-LOGS.md`
`nist-ai-rmf.MG.4.1`	Manage 4.1 — Post-deployment AI system monitoring plans are implemented, including mechanisms for capturing and evaluating input from users and other relevant AI actors	enforced	audit_events: `kye.signal.drift.detected.v1`, `kye.audit_chain_entry.v1` engines: `internal`, `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`, `constitution/35-STREAMING-LOGS.md`
`nist-ai-rmf.GV.6.3`	Govern 6.3 — Third-party data sources, models, and APIs used by the AI system are subject to acquisition and supplier risk-management processes	enforced	audit_events: `kye.subprocessor.v1`, `kye.connector.evidence_import.v1` constitution_refs: `constitution/51-NO-SPOF.md`, `constitution/26-COMMERCIAL.md`
`nist-ai-rmf.GV.1.1`	Legal and regulatory requirements involving AI are understood, managed, and documented	enforced	audit_events: `kye.compliance.attestation.v1` engines: `internal` constitution_refs: `constitution/40-IMPLEMENTATION-CANONICAL.md`
`nist-ai-rmf.GV.1.2`	The characteristics of trustworthy AI are integrated into organisational policies and processes	enforced	audit_events: `kye.purpose.permission.v1`, `kye.purpose.grant.v1` engines: `internal`, `internal` constitution_refs: `constitution/12-PURPOSE-PERMISSION.md`
`nist-ai-rmf.GV.1.3`	Processes, procedures, and practices are in place to determine the needed level of risk management	enforced	audit_events: `kye.risk.score.v1`, `kye.model.capability_profile.v1` engines: `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.GV.2.1`	Roles, responsibilities, and lines of communication related to mapping, measuring, managing AI risks are documented	enforced	audit_events: `kye.purpose.grant.v1`, `kye.federation.cross_org_delegation.v1` engines: `internal` constitution_refs: `constitution/12-PURPOSE-PERMISSION.md`, `constitution/21-DELEGATED-AUDITABILITY.md`
`nist-ai-rmf.GV.3.1`	Decision-making related to mapping, measuring, managing AI risks throughout the lifecycle is informed by a diverse team	advisory	constitution_refs: `constitution/36-GOVERNEDUI.md`
`nist-ai-rmf.GV.4.1`	Organisational policies and practices are in place to foster a critical thinking and safety-first mindset	enforced	audit_events: `kye.agent.governance.v1`, `kye.agent.refusal.v1` engines: `internal` constitution_refs: `constitution/52-DELEGATED-AGENT-BINDING.md`
`nist-ai-rmf.GV.5.1`	Organisational policies and practices are in place to collect, consider, prioritise, and integrate feedback from external sources	designed	audit_events: `kye.resilience.improvement_record.v1` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.GV.6.1`	Policies and procedures are in place to address AI risks arising from third-party software, data, and other supply-chain issues	enforced	audit_events: `kye.evidence.tool_call_pin.v1`, `kye.federation.cross_org_delegation.v1`, `kye.agent.mcp_allow_list.v1` engines: `internal` constitution_refs: `constitution/52-DELEGATED-AGENT-BINDING.md`
`nist-ai-rmf.GV.OV-1`	AI system performance and trustworthiness is regularly evaluated against agreed-upon metrics	enforced	audit_events: `kye.compliance.attestation.v1`, `kye.evidence.trace_replay_spec.v1` engines: `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.GV.IM-1`	Continual improvement of AI risk management is integrated into organisational decision-making	enforced	audit_events: `kye.resilience.loop_iteration.v1`, `kye.resilience.improvement_record.v1` engines: `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MG.1.1`	A determination is made as to whether the AI system achieves its intended purposes and stated objectives	enforced	audit_events: `kye.assurance.adoption_stage.v1`, `kye.compliance.attestation.v1` engines: `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MG.1.2`	Treatment of documented AI risks is prioritised based on impact, likelihood, available resources or methods	enforced	audit_events: `kye.risk.authority_register.v1`, `kye.risk.score.v1` engines: `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MG.1.3`	Responses to identified AI risks include plans, follow up, response time, communication, decisions	enforced	audit_events: `kye.resilience.improvement_record.v1`, `kye.purpose.grant.revoked.v1`, `kye.evidence.decision_map.v1` engines: `internal`, `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MG.2.1`	Resources required to manage AI risks are taken into account along with viable non-AI alternatives	advisory	constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MG.3.1`	AI risks and benefits from third-party resources are regularly monitored and risk controls are applied and documented	enforced	audit_events: `kye.evidence.tool_call_pin.v1`, `kye.agent.mcp_allow_list.v1`, `kye.federation.cross_org_delegation.v1` engines: `internal` constitution_refs: `constitution/52-DELEGATED-AGENT-BINDING.md`
`nist-ai-rmf.MG.4.1`	Post-deployment AI system monitoring plans are implemented	enforced	audit_events: `kye.signal.drift.detected.v1`, `kye.signal.stress_test.high_risk_detected.v1`, `kye.evidence.observed_action.v1` engines: `internal`, `internal` constitution_refs: `constitution/35-STREAMING-LOGS.md`
`nist-ai-rmf.MG.4.3`	Incidents and errors are communicated to relevant AI actors including affected communities	enforced	audit_events: `kye.signal.stress_test.high_risk_detected.v1`, `kye.evidence.pack.v1` engines: `internal` constitution_refs: `constitution/38-COMMS-RAIL.md`
`nist-ai-rmf.MP.1.1`	Intended purposes, potentially beneficial uses, context-specific laws, norms, and expectations are understood and documented	enforced	audit_events: `kye.purpose.permission.v1`, `kye.model.capability_profile.v1` engines: `internal` constitution_refs: `constitution/12-PURPOSE-PERMISSION.md`
`nist-ai-rmf.MP.2.1`	The specific tasks and methods used to implement the tasks that the AI system will support are defined	enforced	audit_events: `kye.model.capability_profile.v1`, `kye.model.influence_envelope.v1` engines: `internal` constitution_refs: `constitution/14-AGENTS-AND-ENGINES.md`
`nist-ai-rmf.MP.3.1`	Categorisation of the AI system is performed	enforced	audit_events: `kye.model.capability_profile.v1`, `kye.risk.score.v1` engines: `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MP.4.1`	Approaches for mapping AI technology and legal risks are followed	enforced	audit_events: `kye.risk.score.v1`, `kye.compliance.attestation.v1` engines: `internal` constitution_refs: `constitution/40-IMPLEMENTATION-CANONICAL.md`
`nist-ai-rmf.MP.5.1`	Likelihood and magnitude of each identified impact based on expected use are identified and documented	enforced	audit_events: `kye.risk.score.v1`, `kye.evidence.decision_map.v1` engines: `internal`, `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MP.6.1`	Practices and personnel for supporting regular engagement with relevant AI actors and integrating feedback are documented	designed	audit_events: `kye.resilience.improvement_record.v1` governedui_modules: `kye.governedui.module.consultants.v1`, `kye.governedui.module.auditors.v1` constitution_refs: `constitution/36-GOVERNEDUI.md`
`nist-ai-rmf.MS.1.1`	Approaches and metrics for measurement of AI risks enumerated during Map function are selected for implementation	enforced	audit_events: `kye.risk.score.v1`, `kye.compliance.attestation.v1` engines: `internal`, `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MS.2.1`	Test sets, metrics, and details about the tools used during TEVV are documented	enforced	audit_events: `kye.evidence.trace_replay_spec.v1`, `kye.evidence.tool_call_pin.v1`, `kye.assurance.audit_replay_report.v1` engines: `internal`, `internal` constitution_refs: `constitution/21-DELEGATED-AUDITABILITY.md`
`nist-ai-rmf.MS.2.5`	AI system performance or assurance criteria are measured qualitatively or quantitatively	enforced	audit_events: `kye.assurance.audit_replay_report.v1`, `kye.compliance.attestation.v1` engines: `internal` constitution_refs: `constitution/21-DELEGATED-AUDITABILITY.md`
`nist-ai-rmf.MS.2.7`	AI system security and resilience are evaluated and documented	enforced	audit_events: `kye.signal.stress_test.high_risk_detected.v1`, `kye.resilience.drift_event.v1`, `kye.compliance.attestation.v1` engines: `internal`, `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MS.3.1`	Approaches, personnel, and documentation to detect and track existing, unanticipated, and emergent AI risks based on factors such as intended use are in place	enforced	audit_events: `kye.signal.drift.detected.v1`, `kye.signal.stable_drift.detected.v1`, `kye.resilience.drift_event.v1` engines: `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`
`nist-ai-rmf.MS.4.1`	Measurement approaches for identifying AI risks are connected to deployment context(s) and informed through consultation with domain experts	enforced	audit_events: `kye.risk.score.v1` engines: `internal` governedui_modules: `kye.governedui.module.consultants.v1` constitution_refs: `constitution/36-GOVERNEDUI.md`
`nist-ai-rmf.MS.4.3`	Measurable performance improvements or declines based on consultations with relevant AI actors are identified and documented	enforced	audit_events: `kye.resilience.improvement_record.v1`, `kye.compliance.attestation.v1` engines: `internal` constitution_refs: `constitution/13-RESILIENCE-LOOP.md`