Enterprise AI Governance Guide
A practical guide to governing AI across an organisation — the policies, controls and operating model that keep AI secure and accountable without slowing every team down.
What you'll take away.
- The core elements of an AI governance framework
- Security and data-protection controls for AI systems
- How to assess and tier AI use cases by risk
- Operational guardrails: monitoring, review and incident response
- An operating model that does not become a bottleneck
The guide in brief.
Why governance comes first
Most AI risk comes from use that nobody planned for: sensitive data pasted into public tools, outputs trusted without review, models changed without notice. Governance gives teams a safe default so they can move faster, not slower.
Policy and acceptable use
Start with a short, plain-language policy covering which tools are approved, what data can be used with them and when human review is required. Keep it short enough that people actually read it.
Risk tiering
Not every use case needs the same scrutiny. Classify use cases by the impact of a wrong answer and the sensitivity of the data involved, and match the level of review and testing to the tier.
Security and data controls
Treat AI systems like any other application handling sensitive data: least-privilege access, encryption, logging and vendor review. Pay particular attention to prompt injection and to what data leaves your environment.
Operational guardrails
Once in production, AI needs monitoring for quality, cost and misuse, a way for users to flag problems and a defined incident process. Record what the system did so decisions can be reviewed later.
Operating model
A small central group should own standards and shared tooling while product teams own their use cases. Clear ownership avoids both an uncontrolled free-for-all and a committee that approves nothing.