NIST AI Risk Management Framework¶
Operationalize the NIST AI RMF without a separate GRC tool. CRP continuously GOVERNs, MAPs, MEASures, and MANAGEs risk as your AI runs, capturing evidence automatically so your risk posture is always current.
Availability
The CRP SDK and CRP Comply are available for self-hosting today. The managed SaaS console is on the waitlist at comply.crprotocol.io.
Business value¶
- Always-on measurement - real-time quality scoring replaces periodic audits.
- Automatic evidence - every operation is logged in the HMAC audit chain.
- Faster risk response - degradations trigger re-grounding, echo detection, and redispatch.
- Shared vocabulary - map CRP outputs directly to NIST AI RMF categories and subcategories.
Framework Structure¶
The NIST AI RMF defines 4 core functions, 19 categories, and 72 subcategories:
graph TD
A[NIST AI RMF] --> B[GOVERN<br/>6 categories]
A --> C[MAP<br/>5 categories]
A --> D[MEASURE<br/>4 categories]
A --> E[MANAGE<br/>4 categories]GOVERN - Policies, Processes, and Accountability¶
The GOVERN function establishes organizational context for AI risk management.
| Category | Subcategory | CRP Mapping |
|---|---|---|
| GV-1 | Policies for AI risk management | Protocol axioms, Elastic License 2.0 |
| GV-1.1 | Legal and regulatory requirements | EU AI Act + GDPR compliance modules |
| GV-1.2 | Trustworthy AI characteristics | 10 design axioms |
| GV-2 | Accountability structures | RBAC (OBSERVER/OPERATOR/ADMIN) |
| GV-3 | Workforce diversity and expertise | Provider-agnostic design enables diverse teams |
| GV-4 | Organizational commitments | Security specification |
| GV-5 | Processes for ongoing engagement | RFC process, governance framework |
| GV-6 | Policies for third-party AI | Provider adapter interface |
MAP - Context and Risk Identification¶
The MAP function identifies risks in context.
| Category | Subcategory | CRP Mapping |
|---|---|---|
| MP-1 | Intended purpose is defined | Declarative task specification |
| MP-2 | Interdependencies mapped | Fact graph with typed relationships |
| MP-2.1 | Likelihood and magnitude of harm | client.compliance.classify(...) |
| MP-2.2 | Practices to identify risks | Quality tier degradation formulas |
| MP-3 | Benefits compared to risks | Quality reports with saturation metrics |
| MP-4 | Risks examined over lifecycle | Session-level quality monitoring |
| MP-5 | Impacts to individuals | PII scanning, processing records |
MEASURE - Analysis and Monitoring¶
The MEASURE function quantifies and monitors AI risks.
| Category | Subcategory | CRP Mapping |
|---|---|---|
| MS-1 | Appropriate methods and metrics | Real-time quality scoring per window |
| MS-1.1 | Approaches for measurement | Information density, coherence, novelty |
| MS-1.2 | Computational tests and evaluations | 1,500+ automated tests |
| MS-2 | AI systems evaluated for trustworthiness | Quality tiers S/A/B/C/D |
| MS-2.1 | Test sets representative | Live verification suite |
| MS-2.2 | Evaluations document AI limitations | Honest degradation reporting per tier |
| MS-2.3 | Relevant AI actors can access results | Compliance reports, quality reports |
| MS-3 | Mechanisms for tracking metrics | Telemetry in quality reports |
| MS-4 | Measurement feedback | Re-grounding on degradation threshold |
MANAGE - Risk Treatment¶
The MANAGE function addresses identified risks.
| Category | Subcategory | CRP Mapping |
|---|---|---|
| MG-1 | Risk treatment plans | Automatic mitigations per risk level |
| MG-2 | Risk responses | Re-grounding, echo detection, abort + redispatch |
| MG-2.1 | Response deployed quickly | Real-time quality monitoring, <1ms response |
| MG-2.2 | Mechanisms to supersede decisions | Human oversight via strict safety profile |
| MG-3 | Pre-deployment validation | Quality gates, envelope preview |
| MG-3.1 | Monitoring in deployment | Session status, telemetry, audit trail |
| MG-4 | Risks managed post-deployment | CKF cross-session learning |
Trustworthy AI Characteristics¶
NIST defines 7 characteristics of trustworthy AI. CRP's coverage:
| Characteristic | CRP Implementation | Evidence |
|---|---|---|
| Valid & Reliable | Quality tiers (S–D), degradation formulas | Benchmark reports |
| Safe | Compliance classification, human oversight | Risk assessment, oversight checkpoints |
| Secure & Resilient | 8 security layers, ~202μs overhead | OWASP 9/10 LLM, 8/10 ML |
| Accountable | HMAC audit trail, RBAC | Chain verification, 3-role hierarchy |
| Transparent | Quality reports, envelope preview | Saturation %, tier, facts included |
| Explainable | Fact provenance, window DAG | Full lineage from ingest to output |
| Fair | Multi-aspect decomposition | Balanced fact selection across topics |
| Privacy-Enhanced | PII scanning, consent, erasure, retention | GDPR coverage |
SDK Evidence Example¶
import crp
client = crp.SDKClient()
client.configure(safety_profile="strict")
# Map the system and intended purpose
risk = client.compliance.classify(
framework="nist-ai-rmf",
purpose="customer-support-routing",
personal_data=True,
automated_decisions=True,
)
# Generate evidence for all four functions
report = client.compliance.report(framework="nist-ai-rmf")
audit = client.audit.summary()
print(f"Risk level: {risk.risk_level}")
print(f"Mapped functions: {report.mapped_functions}")
print(f"Chain valid: {audit.chain_valid}")
Integration Approach¶
CRP enables NIST AI RMF compliance through:
- Continuous measurement - Real-time quality scoring, not periodic audits
- Automatic recording - Every operation logged in HMAC chain
- Evidence generation -
client.compliance.report()produces multi-framework compliance evidence - Built-in controls - Risk classification, human oversight, PII scanning are protocol features, not add-ons