One Thing Everyone Gets Wrong About AI RMF in M365

my reaction when you're back to read another one of my blogs:

Mapping the NIST AI Risk Management Framework onto compliance dashboards, DLP rules, policies, etc. is great; don't get me wrong. But, configuring controls isn't always enough. In this case, we need to actually close the loop between policy and proof.

Most orgs are equating "Govern" with assigning data owners, classifying files to satisfy "Map", tackling "Measure" by taking a cursory glance at a couple analytics dashboards, and trying to "Manage" via DLP rules or Insider Risk Management policies.

Again, I applaud the effort. This puts you strides ahead of most other orgs just getting started with Purview. Unfortunately, true risk management requires you to validate. Continuously. Most of my Purview Data Security engagements come about because Purview is daunting. You might not trust 100% that your DLP policies are working. Throw AI in the mix, and that trust is now non-existent.

Sooo...what do we do?

1. Run simulated leaks and test flows: Try pulling sensitive data through Copilot, Teams, and SharePoint, and then review whether DLP and labeling policies actually stop you.
2. Monitor and adapt: Try using Activity Explorer and incident analytics to spot gaps or drift over time. Don’t just blindly assume that today’s settings are going to hold up as AI features evolve.
3. Document and escalate your findings: The RMF is a cycle, not a one-off; share your test results with stakeholders and feed those lessons back into your control tuning.

Implementing the AI RMF with Microsoft Purview by matching features to pillars is a great way to get started, but you need to also build a living feedback loop between those compliance controls and real-world outcomes.

Make a routine of challenge and proof. Then you’re actually managing AI risk.