AI governance
A live inventory, named ownership and a lightweight approval route for AI systems, so useful work continues and shadow adoption stops.
Build AI governance- Standards
- ISO/IEC 42001 · NIST AI RMF
- Testing
- Adversarial, tool-aware
Why this matters now
AI is already in use inside most organisations, usually before governance catches up. AI governance gives you control without blocking work that is genuinely useful.
What the engagement covers
Each area below is a defined part of the engagement, with an owner, an output and a date.
Policy
Rules staff and suppliers can follow without stopping useful work.
Ownership
Named accountability for each AI system in use.
Inventory
A live register of AI systems, models, data and vendors.
Approval
A route through your governance that results in signed, dated documents.
Monitoring
Continuous observation with alerting tuned to your risk profile.
Reporting
Clear findings, evidence and owners, in a format your teams can act on.
How the work runs
The same sequence on every engagement, so you know what happens next.
Inventory
Establish what AI is actually in use, including tools adopted without approval.
Assess
Evaluate each use case for security, privacy, legal and model risk.
Control
Apply proportionate controls: policy, access, logging, approval points and monitoring.
Test
Adversarial testing against the deployed system, including indirect injection paths.
Govern
Reporting, review and change control so governance keeps up with adoption.
What you get out of it
Adoption without blind spots
Useful AI work continues; the uncontrolled parts get controlled.
Standards alignment
Mapped to ISO/IEC 42001 and ISO/IEC 27001 so one programme serves both.
Tested, not assumed
Controls verified by adversarial testing against the live system.
Before you enquire
How quickly can this start?
Who actually does the work?
How is this priced?
Where to go next
Build AI governance
One scoping call with the consultant who would run the work. No obligation, no charge.