AI in GRC: Friend, Foe, or FOMO?

Everyone wants AI. No, scratch that; everyone needs AI. At least, that’s what leaders are concluding after seeing all the analyst reports, attending all the conferences, and reading all the industry news.

The FOMO is real, and it’s creating a kind of organizational whiplash. Top-down pressure is pushing AI adoption at breakneck speed while security teams scramble to understand what they’re even supposed to be protecting. Meanwhile, vendors are embedding AI capabilities into existing products faster than their customers can evaluate it. It’s no surprise that organizations are quickly losing control.

But there are ways to approach AI adoption with compliance and security best practices in mind. Today we tackle the fundamental question: How do you implement AI safely and effectively in a governance, risk, and compliance context?

 

You Can’t Automate Your Way Out of Bad Governance

Let's start with an uncomfortable truth: AI makes things fail faster. Much faster.

Unless you intentionally design your AI systems to shut down when something goes wrong, problems spread at the speed of light across your enterprise. And with more vendors baking AI into existing products (whether you asked for it or not), you’ve got a recipe for disaster.

Once AI gets into production, it’s nearly impossible to fix easily. Having intentional governance and segmentation in place is critical so you can weigh internal versus external AI, generative versus agentic, and vendor-embedded versus intentionally-built.

What’s more, AI has changed the old adage of “garbage in, garbage out.” Now it’s sometimes “garbage in, gospel out.” AI systems respond even when they’re uncertain, making up incorrect information that may sound like gospel truth. If your teams aren’t applying critical thinking to validate outputs, you’re setting yourself up for failure.

So what does good AI governance actually look like? It starts with something simple: knowing what you’re trying to create. Before you deploy anything, you need to define your business outcomes, understand if the risk is worth it, and determine if the cost to mitigate that risk makes sense.

 

An Important Caveat

Another question worth asking: Does using AI to monitor AI make sense?

The answer is yes and no. AI can help detect patterns and process high volumes of information, but the final evaluation can’t be left to systems that might lack the appropriate context. In general, human interpretation remains essential, especially for assurance processes.

That’s because AI is a tool for scale, not for replacing expert insights. It can provide faster analysis, better prioritization, and significantly reduced manual effort at high volumes than any human can. But that value only holds if human governance actually exists.

Ultimately, AI works best as decision support, not autonomous decision-making. It’s designed to optimize output and give you the summarized data you need to make good decisions.

 

AI-Augmented Compliance-as-Code

According to the cyber experts, AI is shifting the center of risk away from code and into data. Data integrity, data governance, and data authentication are now your real control points, which introduces new complexity: evolving model standards and data sprawl that expands both your compliance scope and attack surface.

The answer? Use Compliance-as-Code to keep up with the speed and complexity of modern development.

We write about Compliance-as-Code often (here and here, for example), but the gist is that it integrates automated compliance checks into your CI/CD pipeline. When you use Compliance-as-Code to evaluate security and compliance requirements at the earliest possible point in the development process, you can catch issues before they become expensive problems in production.

A mature Compliance-as-Code approach can leverage AI to keep pace with the speed and complexity of modern development. That looks like automatically ingesting code bases, using AI to evaluate them against your compliance requirements, and making faster risk-based decisions in near real-time. Your outputs become machine-readable, easily validated, and optimized for your team’s time, and you get compliance as a byproduct through automation and AI working together.

It’s all part of the broader shift to dynamic oversight, monitoring, and accountability. You’re not just testing before deployment anymore; you’re testing continuously, monitoring data pipelines, models, prompts, and outputs in real-time. All the while, you’re using AI to augment the work your human experts can do, not replace it.

 

The End Game: Cyber Resilience, Not Checkboxes

At the end of the day, the goal isn’t to be compliant; it’s to be cyber resilient.

That means understanding that not every organization needs AI right now (or at least not for everything). It means recognizing that just because a vendor added AI to your existing tools doesn’t mean it makes sense for your sector or use case. And it means taking the time to think through legitimate business cases before deploying systems.

ISO/IEC 42001:2023, the AI management standard, offers a framework worth considering. It forces organizations to think through whether they have legitimate business cases, what inherent risks they’re taking on, and what potential impacts could hit stakeholders if something fails.

Another important consideration is the regulatory environment you’re operating in, which will dictate where to focus your risk controls. (Consumer protection requirements differ from business protection requirements, for example.) But regardless of your industry, the fundamentals remain: understand your data, govern intentionally, monitor continuously, and never assume AI will do your thinking for you.

The truth is, AI exposes existing problems as much as it creates new problems. How you handle these problems determines whether you’re building on solid ground or setting yourself up for a spectacular failure. Our best advice is to move intentionally, with governance that matches the scale and speed of the technology you’re deploying, and to apply human wisdom about when and how to use AI.