⚡Quick Summary
This 214-page Controls Checklist offers a granular, control-by-control mapping of the NIST AI RMF 1.0 into a structured format for compliance audits, implementation planning, and policy alignment. Produced by the “Helena” AI assistant from GRC Library, it’s a technical draft intended to help GRC teams operationalize the AI RMF in practice. Each control includes enhanced guidance, indicators, frequency definitions, and templates for evaluating effectiveness.
🧩 What’s Covered
- 58 Control Areas: Ranging from “Emergent AI Risk Identification” to “Proportional Transparency for Severe Consequences.”
- Control Elements: Actor, Type (e.g. preventive, technical), Frequency, Monitoring, Key Control Indicators, and Evaluation Methods.
- Integration Guidance: Frequent cross-references to NIST RMF concepts (GOVERN, MAP, MEASURE, MANAGE).
- Specific Guidance for GRC Teams: Including Version Control, Independent Audit Triggers, Third-Party Risk, and IP Compliance.
💡 Why it matters?
Most organizations struggle to translate high-level risk principles into operational safeguards. This checklist does the hard part—breaking down the NIST AI RMF into controls you can assess, assign, and audit. For legal, security, or compliance professionals implementing AI risk governance, it’s a practical companion—even if unofficial.
❓What’s Missing
- Official Endorsement: This isn’t published by NIST, despite the name.
- Implementation Status Indicators: Included, but not pre-filled or tailored to risk categories (e.g., based on system criticality).
- Maturity Mapping: There’s no roadmap linking these controls to levels of AI governance maturity.
👥 Best For
- GRC professionals aligning their internal AI controls to NIST RMF.
- CISOs and Risk Officers building internal assurance checklists.
- Teams preparing for AI assurance audits or impact assessments.
📄 Source Details
Title: Artificial Intelligence Risk Management Framework (AI RMF 1.0) – Controls Checklist
Version: 0.1 (Technical Preview Draft)
Publisher: GRC Library (via AI-generated consultant “Helena”)
Last updated: 09 June 2025
Warning: AI-generated draft, not reviewed by NIST.
🙏 Thanks to the GRC Library team and their AI assistant Helena for putting together this early-stage but ambitious implementation toolkit.
