NIST AI RMF compliance,manage AI risk across the lifecycle
The NIST AI Risk Management Framework is a voluntary framework for managing the risks of AI systems across their lifecycle, organized around four functions: Govern, Map, Measure and Manage.
What NIST AI RMF asks of you
The requirements below define NIST AI RMF. ComplyEasyAI maps your environment to each one.
Govern
Cultivate a culture and structures for managing AI risk.
Map
Establish the context and identify risks of each AI system.
Measure
Assess, analyze and track identified AI risks.
Manage
Prioritize and act on risks based on their impact.
Trustworthiness
Address validity, safety, fairness, transparency and privacy.
From scoping to a clean report
Scope
Define the systems, boundaries and requirements your program will cover.
Connect your stack
Link cloud, identity, code and ticketing with read-only access; controls are discovered and mapped.
Collect evidence
AI agents gather configuration and activity evidence on a schedule, building a versioned trail.
Monitor & remediate
Continuous monitoring flags drift the moment it happens and routes it to an owner.
Track effectiveness
Operating effectiveness is recorded over time, ready for your observation window.
Report & hand off
Organized, current evidence is packaged for auditors and stakeholders.
Automate the work that doesn’t need a human
Control mapping
Your environment is mapped to each requirement, so you see exactly which control satisfies what.
Automated evidence
Read-only integrations build a versioned, timestamped trail instead of manual screenshots.
Continuous monitoring
Drift surfaces as soon as it happens, not during fieldwork.
Readiness dashboards
Real-time views highlight failing or unmapped controls with owners attached.
Multi-framework reuse
Shared controls are mapped once and reused across every framework you run.
Audit-ready reporting
Export organized, current evidence packages on demand.
Map once, reuse across programs
NIST AI RMF questions, answered
Is the AI RMF mandatory?
No — it is voluntary, but widely adopted as a baseline for responsible AI and often referenced in policy.
How does it relate to the EU AI Act?
They are complementary; RMF practices support demonstrating the governance the Act expects.
What are the trustworthiness characteristics?
Validity and reliability, safety, security, accountability, explainability, privacy and fairness.
How do you help?
The platform operationalizes the four functions with evidence and tracking rather than static documents.
Start your NIST AI RMF program.
Map the requirements, automate the evidence, stay audit-ready.