AI Governance

Governance is not a feature added at the end of an AI deployment — it is the operating layer that makes a deployment viable in an enterprise context. It determines who can query which knowledge sources, what topics or actions are off-limits, how responses are grounded in authoritative data, and what record is kept when the system acts.

In practice, AI governance involves four components working together: role-based access control (RBAC) that restricts what each user or group can see and do; policy guardrails that define what the AI is allowed to respond to; a full audit trail that records every input, retrieval, and output; and a human review layer for decisions that require oversight.

Consumer AI tools and self-serve AI platforms are built for individual use, not organisational accountability. They have no concept of who is asking, no access controls tied to organisational structure, no ability to restrict responses to approved knowledge sources, and no record-keeping that would satisfy a compliance audit.

Many enterprise AI pilots are built on these tools, which means governance is bolted on after the fact — or never addressed at all. The result is a system that IT and legal cannot sign off on, and that the CISO will block before it reaches production.

A governed AI system can be rolled out to hundreds of users across departments because each user's access is tied to their role. New knowledge sources can be added without risking information leakage across boundaries. Compliance and legal can review the audit trail after any flagged interaction. The CISO can point to a defined policy layer rather than a black box.

Without governance, AI stays in a single team's hands — useful for a pilot, unable to scale. With governance built into the foundation, production deployment and organisation-wide rollout become feasible decisions rather than compliance problems.