The five horsemen of audit prep: Key challenges that derail audit readiness

Audits are among the most stressful periods for GRC professionals. A lot of this stress is born from looming uncertainty, with compliance folk often asking, ‘Do we have everything in place?’, ‘Are controls designed and working as they’re supposed to be?’, and ‘Do we have the right kind of evidence?’

Add to that the nuances of AI, and you have genuine panic. AI has single-handedly changed the way professionals view audits. Not only has it penetrated every facet of audit prep, but it has challenged leadership on traditional notions of how compliance as a function operates. 

But we’re getting a little ahead of ourselves. Let’s get back to audits for a moment. 

The old guard approach to audits 

Traditionally, audits were cyclical processes that involved creating documentation, chasing owners, surviving fieldwork, and passing the certification process. This cycle would repeat every year (or at set intervals, depending on the framework of choice).  

This was acceptable. Until now. 

With maturity and the evolution of tech, this approach introduces a single point of failure—scalability. 

As companies grow, so do their goals—they look for bigger logos, expand into newer regions, and build products that scale with their customers’ needs. And with these new objectives, the scope of compliance grows. The loftier the goals, the larger the data surface; the larger the data surface, the greater the scrutiny and the debt of trust you build with your customers.

Taking the cyclical approach to audits isn’t just ineffective; it’s problematic because it’s inherently broad-spectrum and reactive. And with attack vectors increasing by the day, this approach is a mistake waiting to happen.  

Now, let’s add a little chaos into the mix. 

AI and its far-reaching implications

Organizations are adopting AI faster than governance is maturing. According to Sprinto’s AI Pulse Check Report, over 30% of U.S. organizations faced a major AI-related security incident in the past year, but only 25% have the governance maturity to deal with them.

This changes audit preparation in two ways. 

First, customers are skeptical of how you use AI. They want to know where you use AI, what data it touches, who approved it, how you review output, and the ways in which you monitor drift. And these requirements are mentioned in contracts. 

Second, regulation is arriving thick and fast. The EU AI Act rolled out the first set of rules, such as AI literacy and prohibited-use provisions, as of February last year. This was followed by rules for general-purpose AI in August 2025. A bigger set of rules is expected to be enforced by August this year, with a full roll-out slated for the latter half of next year. 

This means that audit preparation is no longer about how you satisfy an external assessor. It is about proving, on demand, that your controls still work in an environment shaped by faster product change, more third-party dependencies, and growing scrutiny around AI governance. 

Against that backdrop, here are five challenges that organizations face during audit preparation.

Five key challenges of audit prep

Accounting for risk

Many organizations still take a reactive approach to audits. Audit prep is often seen as a checkbox activity that starts when the audit is penciled in and ends with the audit. 

But risk does not operate this way. It’s dynamic.

Every subtle change to your system introduces new risk—access changes daily, new employees join your organization, and new vendors are onboarded. AI-enabled workflows can change even faster than regulation can keep up. 

These changes accumulate over time, leading to long periods of drift. Your controls may look fine during the audit, but proving their effectiveness over the observation period is a whole other matter. 

This is especially true in AI-heavy environments, where teams must reconstruct control effectiveness rather than demonstrate continuous compliance.

Keeping up with changes in requirements

With every new risk comes a large-scale change in regulation. It’s not just internal processes that are impacted. Framework requirements evolve, auditors interpret requirements differently over time, and new regulations can introduce new areas of scrutiny. And what passed an audit previously may no longer be enough because requirements have changed. 

Organizations adopting AI have as big a challenge as any. Most audit frameworks were not built with AI use cases in mind, so organizations have to translate these broad requirements into practical governance for AI tools. Moreover, the challenge isn’t just about decoding requirements but understanding how they apply across the organization. 

Compliance teams have to monitor updates, keep track of AI regulations, satisfy customer requirements, and, at the end of it all, ensure these changes reflect across policies and controls. And so, with most organizations, audit prep becomes a game of catch-up. Eventually, despite a few high-stress weeks, they end up entering audits with the uncertainty of whether their controls still align with the latest requirements.

Scattered evidence

Evidence is rarely missing because the work never happened. It is missing because the work happened in different tools, with different owners, and under different naming conventions. A single control may require screenshots from an HRMS, logs from an identity provider, and policy acknowledgements and training completion from a Learning Management System. 

The result is a scramble for evidence. Teams spend hours collecting evidence from multiple platforms. And satisfying auditor requests becomes an uphill task because proof is spread across silos, with nobody being able to connect artifacts to the right controls.

AI only compounds this problem because the scope is simply broader—policy coverage, human oversight, vendor diligence, monitoring, and approval records all matter.    

The degradation of evidence quality

Evidence degrades over time and between audits. A snapshot that was taken a month ago might not accurately reflect the current configuration of controls. 

Evidence that’s incomplete, outdated, pulled from the wrong environment, or manually altered may pose different issues down the line. Generic evidence is equally problematic since it cannot sufficiently prove that controls operate as intended. 

If a company says it reviews AI use cases for risk, the evidence must show what was reviewed, when, by whom, against what criteria, and what decision followed. If it says humans oversee AI outputs, the evidence should demonstrate how that oversight happens in practice.  

Many organizations discover that quality is what gets them over the line. Just like with risk, small changes have a profound impact on evidence quality. As new tech, people, processes, and AI tools are introduced, compliance teams need to ensure that the strength of evidence holds up. 

To sum up, high-quality evidence reduces debate. Weak evidence creates more requests, more sampling, and more doubt. 

Closing gaps

Closing gaps is one of the most common pitfalls of audit management. An exception was spotted in the previous year, remediation was documented, and the team moved on to other things. But when the next audit comes around, the auditor flags the same issue in a different form. 

So what got fixed was the symptom and not the root cause. 

Remediation isn’t meant to be a task; it must address design. In practice, files are patched, approvals granted, and policies updated, but findings persist because deeper issues like ownership, workflow, monitoring, or escalation aren’t fixed. 

Most organizations struggle to prove that findings remediated post-audit stay resolved in the months that follow. Remediation must not end with addressing the issue and documenting what was done. It requires a deeper, more dedicated approach that involves testing and monitoring controls over time to ensure they function as intended. 

Closing thoughts

Compliance teams today are quickly realizing what audit prep is—and more importantly, what it isn’t. The challenges that exist today are unprecedented, and compliance teams have had to change the way they think about audit management and unlearn some lessons to keep up. 

And among these lessons, one stands out. Audit prep isn’t an endeavor in documentation. It is about building a living, breathing system that ensures control ownership, adapts to change, remediates gaps to closure, and collects evidence that proves compliance in real-time. 

In the age of AI, this philosophy is no longer optional. With the rapid rate of adoption and regulatory shift, the real challenge is not preparing harder—it’s being mindful and preparing continuously.