AI in GRC: What's Actually Useful Right Now

Category: AI & Automation Tags: AI, automation, GRC, agentic workflows, risk operations, Claude, Copilot

Most of what gets said about AI transforming governance, risk, and compliance is vendor marketing, and most of the vendor marketing is about features that sound impressive in a demo and create real implementation headaches in production.

This is what I've actually found useful: the specific places where AI has changed how I work, and the places where the hype is still well ahead of reality.

What AI Already Does Well

Evidence collection and summarization. Control testing generates a lot of raw material: log exports, configuration screenshots, interview notes, audit responses. Summarizing that material, extracting key findings, and drafting initial evidence descriptions used to be tedious manual work. With the right AI tooling it goes much faster. The output still needs review, but the starting point is better.

Control mapping. Mapping controls across multiple frameworks (ISO 27001, SOC 2, NIST CSF, DORA requirements) is one of the most labor-intensive parts of building a unified control library. AI handles the initial mapping pass well. It's not perfect, and the edge cases require human judgment, but getting from zero to a first draft in hours instead of weeks is a real productivity gain.

Risk narrative drafting. When a risk needs to be escalated to executive leadership or the board, the write-up matters. The risk has to be described clearly, contextualized for the audience, and connected to business impact. That's a writing task, and AI is good at writing tasks, especially when you can give it the raw risk data and tell it who the audience is and what decisions they need to make.

Policy and procedure drafting. Same principle. Policy frameworks take a long time to write from scratch. Starting from a well-structured AI draft and editing toward your organization's specific requirements is a lot faster than starting from a blank document.

Meeting preparation and synthesis. Preparing for a risk committee meeting, synthesizing findings from a tabletop exercise, turning raw interview outputs into a coherent risk assessment: synthesis work like this is where AI is most consistently useful.

Where It Doesn't Replace Judgment

The limits are just as specific, and it pays to be clear-eyed about them before an AI output goes anywhere that matters.

It can't tell you what risk to accept. Risk appetite is a leadership decision. It takes organizational context and stakeholder alignment, and it has to be governed explicitly. AI can help you structure the conversation, but it can't make the call, and any tool that implies otherwise is overselling.

It can't validate that a control actually works. Control testing requires evidence of real-world operation. An AI-drafted control description is not a tested control. The testing is human work, or increasingly, automated technical testing, which is different from AI in the GRC sense.

It can't build relationships. Much of what makes a GRC program effective comes from the relationships between the GRC function and the business. The engineering lead who flags a risky architecture decision early because they trust the resilience team. The CFO who calls you before a major vendor contract because they know you'll catch something important. AI doesn't build those relationships. You do.

It hallucinates on regulatory specifics. AI models can produce plausible-sounding regulatory citations that are wrong. Any AI-assisted regulatory work needs careful human review. I've seen outputs that looked authoritative and were subtly incorrect in ways that would matter in an audit.

How I'm Actually Using It

In my day-to-day work, the tools I've found most useful are Claude Code for structured analysis and document work, GitHub Copilot for any scripting or automation I'm building to support program operations, and Cursor for more complex workflow development.

The pattern that's worked best is what I'd call agentic assist: instead of asking AI to run the program, I build workflows where it handles specific, well-defined tasks within a larger process that I'm directing. In practice that means automated risk intake summaries, draft escalation narratives, initial control gap analyses against a new framework, and evidence package formatting. That keeps the judgment and the governance where they belong (with the practitioner) while compressing the time spent on the administrative work that used to eat disproportionate hours.

Where This Is Heading

The GRC platforms that will win over the next few years will be the ones that embed AI into the workflow without requiring practitioners to prompt-engineer their way through every task: platforms that surface the right risk information at the right time, take the friction out of evidence collection and testing, and let GRC teams spend their time managing risk rather than managing documentation.

We're not there yet, but the distance between current AI capability and useful GRC tooling is shorter than most people in the industry think, and shorter than most legacy GRC platforms are willing to acknowledge.

The practitioners who learn to use these tools well now will have a meaningful advantage in the next two to three years. AI won't replace the judgment, but the administrative overhead that currently consumes a large fraction of GRC team capacity is going to shrink substantially, and the programs that adapt will do more with the same headcount.