AI Governance in GRC: Building Features That Pass Policy

AI is changing how GRC teams work. But most oversight systems weren't designed for tools that learn, generate, and iterate, and that's starting to raise red flags. New regulations like the EU AI Act are forcing companies to rethink how they govern these tools. And boards are asking harder questions about where responsibility sits.

Even among mature GRC teams, I've seen how quickly these tools become embedded in workflows before formal governance catches up. Whether it's summarizing regulatory content or drafting early control language, these tools are being used to save time and move faster. But if your oversight model wasn't built for them, gaps show up, especially during policy review or audit.

Most AI governance still lives at the policy level. Principles like transparency and traceability exist on paper. But, in practice, features like automated risk scoring, policy drafting tools, or AI-generated workflows get built without oversight. Outputs get logged without review, and workflow automation often moves forward without defined checkpoints, ownership, and audit trails.

Governance works best when those checkpoints are built into processes, workflows, and solutions from the start. AI can accelerate compliance work. But without governance designed into workflows from the beginning, it creates audit and policy risks. GRC teams must embed oversight at the workflow level to make outputs policy-ready, accountable, and defensible under review. That bar too often gets missed in systems I've worked on when traceability isn't built in from the start.

This blog examines where governance breaks down, what works in practice, and how mature GRC teams are embedding AI into their solution building processes.

How AI gets built without governance

Most teams are using AI in some form: summarizing regulatory content, drafting procedures, or mapping obligations. These tools are saving time, but features are often built faster than governance can keep up.

I've seen this happen even in well-run programs. GRC teams may not develop these features, but they're still accountable for how they're reviewed. And when AI is used without structure, that responsibility becomes harder to carry.

Picture this: a regulatory change comes in, and someone drops an AI-generated summary into the compliance tracker. Weeks later, that same text shows up in a control testing plan. By the time the audit team reviews it, there's no version history, no reviewer on record, and no proof of accuracy. What started as a convenience now fails under scrutiny.

That's the reality of AI without governance. Where outputs drift from one system to another with no checkpoints, no ownership, and no audit trail. Teams can't explain how decisions were made or prove they followed policy.

And while those risks may start small, they surface at the worst time: during audits, regulatory exams, or board reviews, when the stakes are highest and the evidence is thin.

The risks of not modernizing your governance

AI often gets deployed quickly to meet real business needs. But speed often leads to a lack of checkpoints. Each unreviewed output, missing approval record, or shadow process may seem minor at first, but together they create systemic gaps that surface during reviews, when proof of oversight matters most.

Common gaps include:

• Lack of review and ownership: AI-generated outputs (like regulatory summaries) move forward with no formal review, no record, and no clear owner. Regulators and auditors don't ask who used the AI, they ask compliance to prove the organization followed policy.
• Missing traceability: First-line employees and operational teams may reuse AI-generated content without logging how it was created or who approved it. When questions arise, there's no decision trail, and GRC teams often weren't part of the process.
• Shadow processes: Outputs can quietly spread into production workflows without validation. Because review responsibility isn't defined, no one is accountable for catching them.

In regulated environments, these oversight gaps eventually surface in audits, regulatory exams, or board reviews. At that point, GRC teams are tasked with justifying outcomes they didn't oversee, and workflows stall or new solution features get blocked from release.

Why late oversight fails policy reviews

AI governance often fails because key decisions happen in isolation. In many organizations, technical teams move quickly to build or implement AI-enabled tools. Risk and compliance get involved too late to influence the design of the workflow or how those tools are used in compliance processes.

Most teams miss that oversight needs to shape design, not follow it. When policy interpretation and review are delayed, reviews become shallow, approvals inconsistent, and audit trails unreliable.

Common gaps include:

• No shared criteria for acceptable AI outputs
• No defined thresholds for when human review is required
• No record of how prompts, models, or sources were selected

Without structure from the start, gaps compound quickly. Once features are built without stakeholder input or controls, reviews can't make them policy-ready.

How GRC teams build policy-ready features

GRC programs that apply governance well tend to start small. For example, a GRC team might begin with one compliance workflow, like using AI to summarize new regulations, and define how that draft enters the policy development process. They focus on one high-impact use case, define the decision points, and map out where human review fits. From there, they help define how structure gets built into the workflow. Step-by-step, not all at once.

That staged approach is familiar to teams like mine who've worked on the systems behind it: Where a small, well-defined use case often becomes the blueprint for broader governance.

Clear ownership is set early, before AI-generated outputs enter production. Teams decide who approves the output, where that decision is recorded, and what triggers a second-level review. Defining these roles and checkpoints from the start keeps oversight consistent as tools evolve.

Those checkpoints don't appear by accident. They're often wired directly into the systems GRC teams already use, shaped to reflect how compliance work actually happens day to day.

Governance works best when review steps are built into the system. AI can generate a draft, but it can't advance until someone signs off with full context. Embedding approvals directly into workflows turns policy into action and makes governance repeatable.

Embedding oversight into designing features

Programs that function well don't rely on documentation alone. They make the review process easy to follow, and hard to skip. Every action including who reviewed, what was flagged, how it was approved, is traceable.

Strong oversight built into design includes:

• AI use cases scoped up front: Teams start by identifying where AI is being used and what decisions are involved. That context shapes the review process before anything is live.
• Review steps built into the workflow: The system won't move forward without an assigned reviewer signing off. GRC teams help define that checkpoint early. That way, it's part of how the task gets completed, not something bolted on later.
• Prompts and outputs documented automatically: When someone reviews the output, they can see what the AI was asked, how it responded, and what changed during editing.
• Defined responsibilities by role: Oversight isn't assigned to a team, it's assigned to a role. That keeps ownership clear, even when the workflow crosses departments.
• Structured feedback loops: When something gets flagged, that feedback is recorded and used to improve the next iteration, not buried in email or chats.

Teams that follow this approach don't rely on memory. They rely on structure. That's what makes the features they oversee consistent, auditable, and policy-ready.

Make oversight part of the build

By the time audit preparation begins, it's too late to ask whether your AI model adhered to policy.

From review checkpoints to embedded controls, AI-supported compliance workflows, such as policy drafting, regulatory mapping, and control updates, should be designed to reflect written policy. GRC oversight should shape those requirements for review approvals, recordkeeping, and traceability within those workflows. That only works if governance keeps pace with experimentation and checkpoints are defined early by the right stakeholders. When features are built without clear input from risk and compliance, they often stall at policy review.

In Resolver's work with GRC teams, we've seen the most success when oversight is embedded, not just documented. Our tools help teams trace policy to action without manual tracking. That way, GRC teams can easily prove how decisions were made, without relying on memory or scattered documentation.

Whether you're starting small or modernizing at scale, the key is the same: Make oversight part of the build. That's what turns policy into proof.