NIST AI RMF is presented as a four-part framework—Govern, Map, Measure, Manage—providing a high-level governance blueprint without dictating exact implementation details.

Quantitative risk assessment is identified as the hardest piece to pin down, with promises of a deeper dive in a future post.

Positioned as a market differentiator in the B2B AI space, with early adopters like Zendesk and Salesforce noted.

Recommended approaches include qualitative risk assessments that map context, accountability, documentation, legal/compliance, and security, supported by tools such as IBM FactSheet and AI risk repositories; third-party risk questionnaires (AI-CAIQ) are also discussed.

A seven-step outline starts with sponsorship, moves through integration with GRC and scoping, then risk mapping, measurement, ongoing management, and culminates in ISO 42001 certification as a potential milestone.

The piece opens by stressing trust in AI for business and presenting AI Governance as a structured response grounded in quality, safety, and regulatory compliance.

Implementation relies on executive sponsorship and cross-functional governance across Legal, Security, Engineering, and other domains, framed as a company-wide initiative with a clear charter.

The core framework favored is the NIST AI Risk Management Framework for its breadth and alignment with ISO 42001 and the EU AI Act.

A concise takeaway emphasizes that the best time to start AI Governance was a year ago; the next best time is today.

MAP and MEASURE are linked through qualitative mapping for practicality and measurable indicators, while acknowledging the challenges of quantification.

ISO 42001 is presented as a practical shortcut that complements NIST RMF and builds on ISO 27001 foundations to speed up adoption.