Governance and Compliance Considerations

You've got stakeholders aligned. Everyone's excited about the possibilities. Now for the conversation that dampens enthusiasm but saves careers.

Rules.

AI governance isn't optional. It's not something you figure out after launch. Get this wrong, and you're looking at regulatory fines, reputational damage, and a project that gets shut down by legal before it delivers any value.

The good news? If your organisation already has IT governance frameworks, you're not starting from scratch. AI governance extends what you have rather than replacing it.

Why AI Governance Is Different

Traditional IT governance focuses on access controls, change management, and audit trails. AI adds new dimensions.

Your systems are now making suggestions that influence decisions. Sometimes they generate content for customers. Sometimes they summarise sensitive employee data. Sometimes they get things wrong in ways that matter.

This isn't a database query returning records. It's a system producing outputs that look authoritative but might be completely fabricated. That changes the risk profile significantly.

The Design Authority Approach

Don't let AI skills proliferate without oversight. Establish a design authority to review and approve use cases before they go live.

This isn't bureaucracy for its own sake. It's a checkpoint that asks essential questions. Does this use case handle personal data appropriately? Has the prompt been tested for edge cases? Who's responsible when outputs are wrong? What happens if the AI produces something harmful?

A design authority also ensures consistency. Different teams building similar solutions waste effort. Worse, they might implement conflicting approaches to the exact compliance requirement. Central oversight prevents that mess.

Human in the Loop

Here's a principle that simplifies many compliance decisions. Don't let AI take consequential actions without human approval.

Summarising an incident for an agent to review? Low risk. The human makes the final call. Automatically closing tickets based on AI assessment? Higher risk. What if it's wrong?

The more autonomous the action, the more scrutiny it needs. Start with AI that augments human decisions rather than AI that replaces them. You can expand autonomy later once you've built confidence and controls.

Prompt Governance

Prompts aren't just technical artefacts. They're instructions that determine what your AI does. They deserve governance attention.

Who can create prompts? Who can modify them? How are changes tracked and approved? What testing is required before production deployment?

Treat prompts like code. Version control. Change management. Testing requirements. Rollback procedures. The same disciplines that keep your platform stable apply here.

Logging and Audit Trails

You need to know what happened. Every AI interaction should be logged. What input was provided? What output was generated? Who triggered it? When?

ServiceNow provides system tables for this. Use them. Configure retention policies appropriate to your compliance requirements. Some regulations require years of records. Plan for that.

Logs aren't just for compliance audits. They're how you debug problems, identify patterns, and prove that your governance controls actually work.

Ethical Standards

Beyond legal compliance, consider ethics. Is your AI treating people fairly? Could it produce biased outcomes? Are you transparent with users about when they're interacting with AI?

Most organisations have corporate values. AI should align with them. If your values include fairness and transparency, your AI implementations need to demonstrate both.

Some organisations establish AI ethics councils to review use cases. Others embed ethical review into existing governance processes. Either works. What matters is that someone asks the difficult questions before problems emerge.

Risk Classification

Not all AI use cases carry equal risk. A skill that helps agents draft internal notes is lower risk than one that generates customer communications.

Classify your use cases. High risk deployments need more controls, more testing, more oversight. Low risk deployments can move faster with lighter governance.

This lets you balance speed with safety. Not everything needs the full governance treatment. But high stakes use cases absolutely do.

Making Governance Practical

Governance fails when it becomes a barrier rather than an enabler. The goal isn't to slow everything down. It's to ensure you move fast without breaking things that matter.

Build governance into your workflows rather than adding it as an afterthought. Approval processes in ServiceNow. Checklists in your deployment procedures. Reviews as standard practice rather than exceptional hurdles.

When people see governance as helpful rather than obstructive, they actually follow it.

Right, governance framework established. Now let's get specific about the data protection rules you need to follow.