AI Governance for Enterprise Technology Leaders
AI governance should be implemented as operating controls, not only policy statements. This hub focuses on practical architecture and governance decisions.
Executive summary
AI governance advisory covering identity, access, control planes, runtime policy, and enterprise architecture readiness.
Practical explanation
- Create an AI identity model for human and non-human actors.
- Define runtime guardrails and auditability requirements.
- Map governance to delivery workflows, not standalone committees.
Common problems
- Agents with overprivileged access.
- No runtime policy enforcement.
- Compliance efforts disconnected from execution.
Advisory perspective
| Decision area | Advisory lens |
|---|---|
| Operating model | Effective AI governance integrates security, architecture, and operating model decisions into one accountable workflow. |
| Execution | Use dependency-aware sequencing and measurable checkpoints. |
| Governance | Assign decision ownership and close the gap between policy and implementation. |
Related articles
Relevant advisory services
Frequently asked questions
What is the first step in AI governance?
Inventory agent use cases and identities, then enforce least-privilege access and auditable runtime controls.
Can existing security governance handle AI?
Partially. Existing governance is foundational, but agentic systems require additional runtime authorization and monitoring controls.
Run an AI governance readiness review
Identify immediate governance gaps in identity, access, and runtime controls.