How AI Anomaly Detection Selects Audit Targets

Audits are not random. In 2026 Microsoft uses AI anomaly detection to find the mismatches worth pursuing. Understanding the signals it watches is the first step to staying off the list.

Targeting is no longer guesswork

Audits cost Microsoft money to run, so it does not run them at random. It runs them where the expected recovery is highest. In 2026 that selection is driven by AI anomaly detection applied across licensing records and telemetry. The systems look for patterns that suggest unlicensed use, rank the candidates, and surface the ones worth pursuing. If you understand the signals those models weigh, you can see your own risk the way Microsoft sees it.

The signals that raise risk

Anomaly detection is built to find mismatches between what you appear to be entitled to and what you appear to be using. The strongest signals are the ones where deployment and entitlement diverge in a way that is hard to explain innocently.

• Usage spikes that outpace your licensed baseline, especially sudden growth in cloud consumption
• Entitlement mismatches where deployed products exceed what your agreement covers
• Telemetry from Azure Arc and management tooling revealing servers that no entitlement explains
• A true up history that consistently underreports relative to observed usage
• For hosters, monthly SPLA reports that do not move in line with infrastructure growth

Why both tracks are exposed

End customers and hosters are scored on different data, but the principle is the same. For an end customer, the signal is the divergence between deployment and entitlement across the estate, read against Azure, Microsoft 365, and management telemetry. For a hoster, the signal is reporting that does not track reality, where monthly SAL and processor counts stay flat while the platform clearly grows. In both cases the model is hunting for the same thing: a story that does not add up.

How the two tracks look to the model

How to lower your risk

You cannot opt out of the telemetry, but you can make your story add up. The defensive posture is the same one that wins an audit: know your real position before Microsoft models it. For end customers that means an accurate Effective License Position and a true up that reconciles with the data. For hosters it means reporting discipline, monthly SAL reports on time, sealed daily authentication counts, customer mapping, product version mapping, and documented multi tenant boundaries, so the reported numbers move with the platform.

Running your own internal assessment first, before any SAM engagement or demand, is a recognized defensive move precisely because it lets you find and close the gaps the model would otherwise flag.

The next step

Audit selection is data driven, which means it is also predictable. Start with our pillar on Microsoft Audit Triggers, then read how a Microsoft audit begins and why SPLA audits are different from normal audits. Close the gap the model is built to find, and you make yourself a poor target.

If you want a second set of eyes first, we take over the process through our Microsoft audit defense engagement.