Building Practical AI Governance
Build practical AI governance into ownership, risk tiers, delivery gates, evaluation, monitoring, incident response, and portfolio review.
Tier by consequence
Classify uses according to data sensitivity, autonomy, affected stakeholders, reversibility, legal exposure, and business impact. Apply stronger evidence and approval requirements as consequences increase.
Govern the lifecycle
Require a named owner, documented purpose, approved data and tools, pre-release evaluation, change control, monitoring, and retirement plan. Vendor capabilities should enter the same inventory as internally built systems.
Connect policy to operations
Translate principles into access controls, release checks, logging, escalation paths, and response times. Review incidents and near misses to improve both technical safeguards and management decisions.
Common Mistakes
- Treating governance as a legal document
- Banning low-risk learning through excessive review
- Approving a system only once
Market Signals
- The system inventory omits embedded vendor AI
- All use cases face identical approval
- Policies lack an operational owner