Responsible AI and Governance

Guide · Enterprise AI Solution EngineeringPage 10 of 18Overview → … → Governance

Executive summary

AI governance is an operating system: which use cases are allowed, who approves them, what evidence is required, which controls apply, how solutions are monitored and when they must stop. Classify risk, run a lifecycle, map principles to controls, and demand end-to-end traceability.

When to use this page

• Use for every production-bound AI use case.
• Use when classifying risk and assembling approval artefacts.
• Pair with Security and privacy and Evaluation.

Executive view

Name an accountable owner, accept residual risk explicitly, and require kill criteria—not principles on a poster.

Decision required: Approve, constrain or stop based on classification and evidence?

Technical view

Maintain use-case record, risk assessment, model card, evaluation report, DPIA, threat model and monitoring plan.

Design traces for user, model version, retrieval, tools, guardrails, human intervention, cost and latency.

Owners

Owner · Named product / use-case owner

Contributors · Responsible AI, legal, risk, security, AI Solution Engineer, audit, operations

Governance is an operating system

AI governance should determine which use cases are allowed, who may approve them, what evidence is required, which controls must be implemented, how solutions are monitored and when solutions must be stopped.

AI risk classification

Classify use cases according to impact, autonomy, data sensitivity, regulatory exposure, user population, reversibility and explainability needs.

Governance lifecycle

1. Intake
2. Classification
3. Risk assessment
4. Design review
5. Testing
6. Approval
7. Deployment
8. Monitoring
9. Reassessment
10. Retirement

Responsible AI principles

Principle	Expectation
Fairness	No unjustified differential outcomes
Reliability and safety	Behaviour within defined boundaries
Privacy	Personal data protected throughout
Transparency	Users understand when and how AI is used
Explainability	Organisation can explain significant outputs
Accountability	Named owner remains accountable
Human oversight	Humans retain meaningful control over high-impact decisions

AI governance artefacts

Maintain use-case record, risk assessment, model card, data sheet, architecture decision record, evaluation report, DPIA, threat model, approval record, monitoring plan, incident procedure and retirement plan.

End-to-end traceability

A traceable AI interaction should record user, time, application, model, model version, prompt version, retrieved sources, tool calls, policy decisions, guardrail results, output, human intervention, feedback, cost and latency.

Decision required

Do not approve production without risk classification, required artefacts for that class, a named owner, and kill / retirement criteria.

Case study: financial-services assistant

Classified as medium-high risk due to regulated product language. Artefacts required before production: use-case record, DPIA, threat model, evaluation report, model card, monitoring plan and named product owner. Every answer stores citations and policy decisions for audit.

Output

Risk classification, lifecycle stage with required artefacts, principles mapped to controls, trace-field schema, and incident plus retirement procedures.

Common failure modes

• Governance as paperwork after build
• No named owner after the pilot
• Principles without operational controls
• Missing retirement and reassessment paths
• Traceability that stops at “model called”

Solution Engineer checklist

Solution Engineer checklist

• Risk classification completed and accepted
• Lifecycle stage and required artefacts clear
• Principles mapped to concrete controls
• Trace fields defined in observability design
• Incident and retirement procedures drafted

Practical exercise

Complete a one-page use-case record for your initiative: purpose, data, autonomy, impact, residual risk, owner and kill criteria.

Key takeaways

• Governance decides allow, approve, control, monitor and stop.
• Classify risk before design freezes.
• Artefacts and named owners beat principle posters.
• Traceability must cover retrieval, tools, guardrails and humans.

What to do next

1. Continue with Evaluation and observability.
2. Align commercial risk with AI FinOps and commercial design.
3. Use 8D De-risk / Decide for stage gates.
4. Deep-dive the EU AI Act application playbook, NIST AI RMF and the EU AI Act, ISO/IEC 42001 and OWASP LLM Top 10 governance.