The Four Pillars of AI Governance
Pillar 1: Deployment standards
What must be true of an AI system before it can be deployed to production? This includes: minimum accuracy thresholds tested on representative data, security review (data handling, access controls, input validation), human review design for high-stakes outputs, rollback plan, and named operational owner. Deployment standards make the go/no-go decision systematic rather than ad hoc.
Pillar 2: Ongoing monitoring requirements
What must be monitored after deployment, and how often? This includes: quality metrics reviewed on a defined schedule, alerts for metric degradation, evaluation against ground truth test sets run periodically, and an escalation process when metrics breach thresholds. Monitoring requirements ensure that degradation is detected programmatically rather than by a user complaint.
Pillar 3: Incident and error management
What happens when an AI system produces harmful, incorrect, or biased outputs at scale? This includes: incident classification (severity levels for AI output failures), response process (who reviews, who approves remediation), communication protocol (who is notified, how quickly, what is disclosed), root cause analysis, and post-incident improvement requirements.
Pillar 4: Model and data lifecycle
How are AI systems updated, retrained, or retired? This includes: model update review process (before deploying a model update, regression testing against evaluation sets), data retention and deletion policies (ensuring training data meets regulatory requirements), and deprecation planning (how systems are retired safely when they are no longer fit for purpose).