How the GRC Analyst Role Is Evolving in the Age of AI

The GRC analyst role isn’t disappearing. It’s shifting from evidence collection to judgment. I’ve spent the last few months demoing GRC platforms with AI built into the workflows. In most demos, a tool is configured to connect to an environment, evidence is pulled, a control is tested, and a result is presented without analytical input from anyone on the compliance team. It’s impressive, and it can be a bit unsettling if your job is the thing being demoed away. But “the analyst gets automated” is not the outcome. The role is changing shape, not disappearing, and the analysts who learn to work alongside these emerging technologies will be the ones who stay relevant in the job market. What follows is what’s actually happening and what to do about it.

What AI and automation supplement versus what they replace

For years, the bulk of GRC work was collection. It meant gathering screenshots, chasing attestations, reconciling risk registers, and reformatting the same evidence for different auditors. That work is exactly what current tooling absorbs:

• Continuous control monitoring — runs configuration checks against your cloud and identity systems around the clock, so drift surfaces in hours instead of at audit time.

• Automated evidence collection — pulls logs and reports on a schedule and attaches them to the right control.

• Cross-framework mapping — lets one piece of evidence satisfy SOC 2, ISO 27001, and NIST at once instead of being collected three times.

Industry analysts have put numbers to this. Gartner predicts that by 2028, 65% of organizations will have integrated compliance automation into their DevOps workflows, “reducing compliance risk and improving lead time by at least 25%,” and that 75% of continuous compliance automation processes will use AI to drive efficiencies in auditing, reporting, validating, and remediating regulatory compliance “Gartner, 2026 Market Guide for DevOps Continuous Compliance Automation Tools”. On the labor side, a Forrester Consulting study titled “The Total Economic Impact” (a GRC and reporting platform study of Workiva, 2026), found that streamlined control testing, faster audit prep, and reduced time with auditors saved a composite organization (Forrester’s modeled composite based on interviewed customers) roughly 2,000 hours a year.

None of this is hypothetical. Leading GRC platforms have expanded their AI agents to draft policies and answer questionnaires from a company’s own evidence, run vendor-risk assessments that collect evidence and score posture without manual input, and map a single control across a hundred-plus frameworks at once. Some now test a control the moment evidence lands and return a pass/fail/incomplete result with a rationale attached.

The collection layer is being commoditized quickly, but human input is still required. The output is built for human validation, and a person still needs to sign off on the results before they reach an auditor. The pass/fail result is an initial review a person is expected to confirm. The more honest evaluations note that most “agentic” GRC tools are still advanced automation rather than autonomous reasoning, and the real test is not the polished demo. It is how the tool behaves when an integration breaks or the evidence comes back incomplete, which is precisely the moment the work stops being collection and starts being judgment.

Where judgment still matters

The part that matters for your career comes next. Automatically collected evidence is not automatically audit-ready. It can be incomplete, mapped to the wrong control, or missing the context an auditor needs. So the work doesn’t vanish, it moves. It shifts from gathering evidence to reviewing whether the evidence is sufficient, handling exceptions, and deciding whether a control that passes a config check actually works. A control that exists and a control that actually works are two different claims, and only a person can make the second one. I’ve seen this first-hand where collected evidence via a platform does not cleanly align with a control objective. In fact, most platforms that use automation to review various file formats like screenshots end up being poorly analyzed and mistakenly tagged as evidence that can support a compliance check.

That’s the durable core of the role. The analyst who can review an AI agent’s output, determine that the evidence will not withstand auditor scrutiny, and document exactly why is doing something these tools are designed to assist, not replace. Determining whether evidence is sufficient remains human judgment.

This is where the role gets more demanding, not less. Analysts have always exercised judgment, deciding whether evidence was adequate, whether a control was designed well, whether an exception was acceptable. What changes with AI and automation is how much that judgment carries. When collection was manual, an analyst saw every artifact along the way and caught problems by handling them. When a tool collects and tests at machine speed, nobody sees the intermediate steps, so the only remaining check on a bad result is the person reviewing the output. The volume goes up, the visibility goes down, and the judgment call becomes the entire control.

What a GRC analyst can do about it

If I were setting my priorities today, this is where I’d start.

1. Learn to question the AI results and the automation behind them instead of trusting them by default. You don’t need to become an engineer, but you should understand a tool well enough to check its work, meaning what evidence it pulled, what it actually tested, and why it returned the result it did. If a tool says a control passed, you want to be able to explain how it reached that conclusion, not just repeat it.
2. Get comfortable reading the native formats of modern evidence, like an IAM policy, a pipeline definition, or an infrastructure-as-code file. This isn’t the baseline for the role today, and it doesn’t mean becoming an engineer. Plenty of strong analysts have built successful careers on process and analysis without it. But as more evidence arrives as configuration rather than documentation, the ability to read a technical artifact directly is turning into a differentiator, and hiring managers are starting to screen for it. Half the old bottleneck was waiting for an engineer to translate. The analyst who reads it directly and uses AI to help explain what the evidence means, removes that wait.
3. Sit through the vendor demos yourself and ask hard questions. Watch what the automation does versus what the slide claims. An hour of that teaches you more about where the role is heading than any thought leadership piece.

The pattern underneath all of this is that the role is reorganizing around judgment and technical fluency. The clerical work is moving into software and automation. What remains is interpretive work: deciding what evidence means and whether a control truly works. And it requires enough technical grounding to evaluate what the automation places in front of you.

Together, judgment and the technical fluency to use AI features effectively will define the role now in the AI era.