AI Governance and Assurance in Practice

Governance makes decision ownership and control evidence explicit. Define the business owner, technical owner, data owner, risk reviewer, and incident responsibilities for each use case. Distinguish a framework for risk management from a legal compliance determination.

• AI use cases and lifecycle boundaries
• Accountability and review forums
• Framework context and legal-review boundaries

Planned lab. Map ownership for three enterprise AI use cases.

Expected output. Accountability matrix and governance scope.

An inventory should capture what the system does, who uses it, what information it accesses, and which external services it depends on. A list of model names alone does not support an operational review.

• Use-case inventory and intended use
• Data and third-party dependencies
• Intake and change triggers

Planned lab. Create an inventory record for a support assistant, an HR drafting tool, and an invoice triage workflow.

Expected output. Inventory and intake form.

Risk depends on use, impact, access, and operating conditions. Select controls that change behaviour or provide decision evidence. Assign each control an owner and a method for checking whether it works.

• Context and impact assessment
• Privacy, security, quality, and oversight risks
• Control owner and evidence mapping

Planned lab. Build a risk register and control matrix for the synthetic support application.

Expected output. Risk assessment and control matrix.

Assurance reviews whether the evidence supports the proposed use. Check evaluation coverage, access tests, approval boundaries, source provenance, and operations readiness. A green aggregate metric should not hide a blocking failure.

• Evidence standards and review criteria
• Evaluation and security reports
• Exceptions and conditional approval

Planned lab. Review a deliberately incomplete evidence pack and issue an approval, conditional approval, or hold decision.

Expected output. Assurance checklist and reasoned review decision.

AI systems and their contexts change after release. Define review triggers for new sources, model changes, expanded users, and incidents. Keep the inventory and evidence aligned with the version actually in use.

• Monitoring owners and review triggers
• Model/source/use changes
• Retention and documentation responsibilities

Planned lab. Draft a change and periodic-review process with clear escalation responsibilities.

Expected output. Change-control and monitoring plan.

An incident response needs clear roles, a contained workflow, and evidence for restart. Exceptions should be time-bounded and owned rather than silently accepted. Document the decision and follow-up actions.

• Incident identification and containment
• Exception expiry and risk acceptance
• Restart criteria and learning actions

Planned lab. Run a tabletop for a restricted-source disclosure in a synthetic assistant.

Expected output. Incident record and corrective-action list.

Bring the inventory, risk assessment, control evidence, and operating responsibilities together. Present a justified decision for a specific use case and identify what remains outside the evidence available.

• Governance evidence index
• Decision and ownership presentation
• Follow-up plan

Planned lab. Present a complete governance review for one synthetic AI use case to a mock review panel.

Expected output. Governance pack and next-review plan.