Preparing for an AI-Driven Audit: Five Things You Need to Know

I recently attended the HLTH ViVE conference in LA, where I had the opportunity to speak with healthcare tech executives from across the country. One constant theme of my conversations with attendees was the impact of AI on the infosec audit process, and the main questions were often along the lines of “sounds great, but what does that actually mean for me?” and “so how is this different to what I do now?”

Having worked both as an auditor and now on the advisory side, I’ve had a front-row seat to how AI is transforming audits. What used to be manual, time-intensive work is now faster, more scalable, and increasingly automated. But from my experience, the real story isn’t about replacement, it’s about balance and trust. Infosec audits need to be unimpeachable, or they risk causing huge damage to the reputation of the company audited, the auditor, and the industry as a whole.

Here are five key issues I believe every team should carefully consider as they bring AI into their audit programs:

1. AI accelerates audits, but also raises the bar

One of the biggest changes I’ve seen is the sheer volume and complexity of data we’re expected to handle. AI is incredibly effective at ingesting, normalizing, and analyzing large datasets, and it enables more continuous monitoring instead of point-in-time reviews. However, with that capability comes increased expectations from regulators, customers, and the auditors themselves. Faster audits aren’t enough; they also need to be deeper and more consistent. Teams need to be ready for that shift.

2. Automation without context is risky

AI is powerful, but it lacks the business context that experienced auditors rely on. It can identify anomalies or flag missing elements, but it doesn’t understand why a control exists or how a business actually operates. I’ve seen situations where AI-generated outputs look complete on the surface but miss critical nuance that only a human can deliver. Audits are rarely black and white, and relying too heavily on automation can oversimplify complex realities.

3. Audit-ready evidence still requires human storytelling

To expand on the previous point, there’s a growing misconception that simply connecting systems and automating evidence collection will produce audit-ready artifacts. In my experience, that’s rarely true. Evidence without context is incomplete. Auditors need to understand how controls map to real processes, why decisions were made, and how exceptions are handled. That “why” narrative still has to come from humans who understand the environment.

4. Strong processes matter more than shiny tools

I see a lot of organizations investing heavily in AI tools but overlooking process maturity. Automation can streamline tasks, but it can’t fix weak governance or unclear ownership. High-performing teams pair AI with strong oversight, clear control ownership, and well-defined review processes. They also train their teams to critically evaluate AI outputs instead of blindly trusting them. In my experience, that’s what separates effective programs from fragile ones.

5. Human judgment and trust remain irreplaceable

Audits are ultimately about trust. Auditors need to interpret gray areas, evaluate compensating controls, and communicate findings in a way that stakeholders understand. They also represent your organization to customers and regulators. That requires judgment, empathy, and credibility, which are all things that AI simply can’t replicate. I’ve found that the best outcomes happen when AI handles the repetitive groundwork, freeing humans to focus on interpretation and communication.

If there’s one lesson I keep coming back to, it’s this: AI is an accelerator, not a replacement. When we combine its efficiency with human insight, we don’t just make audits faster. We make them better.