Why AI governance matters

Generative AI has moved faster than most internal policies. Employees are using ChatGPT, Claude, Gemini and others to draft reports, analyse data, and even process client information. That productivity gain is real—but so are the risks. Every prompt that leaves your environment could expose confidential data, intellectual property, or regulated information.

Governance isn’t about slowing people down. It’s about creating visibility, accountability, and safe boundaries for experimentation. The organisations that succeed won’t be the ones that ban AI—they’ll be the ones that use it responsibly.

The five most common governance gaps

• Uncontrolled data sharing: staff paste sensitive data into public tools that retain prompts or use them for model training.
• No visibility or logs: there’s no record of what was asked, what was generated, or who did it—leaving no audit trail.
• Policy disconnect: AI usage isn’t covered by existing data classification or acceptable-use policies, creating grey areas.
• Unverified outputs: AI-generated advice or code can make its way into client deliverables or systems without human review.
• Retention blind spots: prompts and responses fall outside your records-retention and eDiscovery frameworks.

Building a framework for responsible AI

Start with the same pillars you apply to any critical system—people, process, and technology—and adapt them for generative AI:

• People: train staff on what’s safe to share. Define roles—who can experiment, who can approve AI use, who owns the risk.
• Process: update data-handling and records-retention policies to include AI inputs and outputs. Add human review steps for business-critical content.
• Technology: use governed AI environments where prompts, responses, and files stay within your control—logged, attributed, and retained.

Implementing AI safely in your organisation

Most organisations start by mapping where AI is already in use—both officially and unofficially. From there:

• Identify high-risk use cases, such as customer data or confidential reports.
• Introduce a central “AI workspace” for staff to experiment safely.
• Capture prompts and responses automatically for review and retention.
• Apply access controls aligned with your data classification scheme.
• Establish a short governance policy (1–2 pages) that people actually read.

Start small: one team, one controlled tool, one clear success metric. Then scale as trust grows.