Governance Failure Radar: AI failures and what would have stopped them.
For each public failure, we explain what happened, why the controls failed, and where runtime enforcement could have blocked or escalated the action.
We document every public AI governance failure.
We analyze root cause.
We show how deterministic runtime controls would have prevented it.
Incident Structure
→
What Happened: Public incident documentation
→
Root Cause: Why existing controls failed
→
Prevention Model: How deterministic runtime control would have prevented it
What the Radar Measures
- Missing authority boundary
- Many incidents occur because generated intent can reach tools before a policy gate evaluates whether the action is authorized.
- Unverifiable governance
- Logs may describe a failure, but they rarely prove the policy state, decision path, and authorization result for each action.
- Escalation failure
- Human oversight often becomes a dashboard review after the fact instead of a runtime escalation requirement before high-risk execution.
- Evidence gap
- Without signed receipts, organizations cannot show that a specific AI decision passed the required governance checks.
From Failure Analysis to Measurement
The Radar is the incident side of the same thesis behind the Agentic Governance Benchmark: governance has to be measured at runtime, not described in policy language after the system has already acted.
Incident Analysis
Written failure analysis
Longer write-ups of the incidents and failure patterns the Radar tracks.
- 5 AI Governance FailuresPublic incidents, the control that was missing, and what would have blocked them.
- AI Agent Incident Response PlaybookWhat to do once an agent has already taken an unauthorized action.
- Why AI Governance Platforms FailWhy observation-first tooling keeps missing the execution boundary.
- Agentic AI Risks Every Enterprise Must KnowThe failure modes that appear once agents can call real systems.