GoKawiilBy Maril Vernon, GRC Engineering Evangelist, Anecdotes.
Every vendor on every panel right now is saying the word "agentic." But most of them can't explain what actually changes when you stop treating GRC like a filing cabinet and start treating it like a fluid system.
I spent years on the offensive side, red and purple teaming, breaking the controls that GRC teams swore were working. Same findings, same gaps, different quarters. So when I tell you agentic AI is about to reshape how GRC operates, I'm not selling you a buzzword. I'm telling you what I'd be paying attention to if I were still trying to get past your controls.
Here is the honest version of where this goes, and what one of these agents actually looks like when you build it.
What "Agentic" Actually Means Here
Automation is not new to GRC. We have been scripting evidence collection and bolting RPA onto workflows for years. The problem is that most of it just moved the busywork around faster. It still produced static artifacts, still ran on a schedule, still answered the only question legacy GRC knows how to ask: "Did this control pass?"
An agent is different in three specific ways. It has autonomy, so it acts when a condition is met instead of waiting for a human to kick off a task. It has context, so it works against the actual state of your program rather than a screenshot from last quarter. And it executes multiple steps, so it can analyze, decide, and act in sequence rather than dumping a row into a report for you to deal with later.
The systems we are governing have already gone agentic. Cloud is elastic, identity is fluid, infrastructure is ephemeral, AI is non-deterministic, and CI/CD never stops. Attackers figured that out a long time ago but too many compliance programs are still trying to govern real-time systems with point-in-time assumptions.
Now, agentic does not mean handing judgment to a stochastic parrot, in fact most of the work should remain deterministic. The model provides reasoning, summarization, and orchestration. Your controls, thresholds, and policy decisions should still come from humans.
Frankly, this is one of the best use cases for AI in cybersecurity. GRC is full of high-volume, repeatable work performed against known baselines. That's exactly the kind of problem machines excel at. We already trust AI to help us detect anomalies, prioritize alerts, and sift through mountains of telemetry.
... continue reading