Why Good AI Projects Still Fail Governance Reviews
By Jora Gill · · Updated · 5 min read
A capable team builds an AI system that works. Then it reaches risk, legal or compliance review and stops. The system is not necessarily unsafe. The team simply cannot show that it is safe.
The common reasons
- Nobody can say how accurate the system is on real cases.
- It has not been tested against manipulation or data leakage.
- Outcomes have not been compared across groups of users.
- Ownership and oversight are unclear.
- There is no plan for monitoring and re-testing after launch.
Assurances versus evidence
Reviewers are asked to accept risk on behalf of the organisation. “We tested it and it looked fine” gives them nothing to rely on. Test results, ranked findings and a way to repeat the tests do.
Arriving at review prepared
Test the system independently across security, reliability and fairness before review, not after it. Address the serious findings, re-test to confirm the fixes, and bring the evidence. Review then becomes a decision, not an investigation.
Find out what your AI will get wrong, before anyone else does.
FireBreak independently tests AI for security, reliability, fairness and governance. We never assess systems we built, and we never sell fixes for what we find.