Knowledge · Enterprise

Enterprise AI Governance

How organisations govern AI at scale: workflow, accountability, lifecycle management, board oversight, shadow AI, vendor governance, and why spreadsheets and AI agents are not enough.

Workflow & Accountability
Enterprise governance workflow?
The enforced sequence from registration through screening, classification, evidence, approval, and re-screening.
Management accountability?
Boards and executives are personally responsible — not just for delegating, but for demonstrating active oversight.
How should boards read status?
Verifiable portfolio visibility — not traffic lights without drill-down to governance records.
Lifecycle governance?
From registration through approval, monitoring, re-screening, and retirement — continuous, not one-time.
What is compliance maintenance?
The ongoing work after the initial assessment that keeps compliance alive.
Continuous monitoring?
Active tracking of evidence currency, legal changes, and system state across the portfolio.
Why Not Simpler Approaches
Why do spreadsheets fail?
No workflow, no approval gates, no audit trail, no evidence linkage. Documentation, not governance.
Why is Excel not enough?
Cannot enforce process, cannot create immutable records, cannot scale across hundreds of systems.
Why not just an AI agent?
Agents accelerate analysis. They cannot enforce governance, attribution, or human accountability.
Can compliance be automated?
Processes yes, decisions no. Human accountability remains the regulatory requirement.
What is shadow AI governance?
Discovering and governing AI used without formal oversight. You cannot govern what you don't know exists.
What is AI vendor governance?
Structured assessment of third-party AI providers — deployers cannot outsource governance responsibility.

Back to all knowledge.

Knowledge Base → Governance Concepts
Get in Touch
Request More Information

Tell us about your organization and what you’re looking to address. We’ll follow up with the relevant information.