The AI Governance Mistake Many Companies Don’t Realize They’re Making

The sheer pace of AI adoption across companies has left many executives and boards struggling to lay out an effective governance response. Not surprisingly, many have resorted to implementing AI audits, which are designed to test model reliability, detect bias and meet compliance requirements. However, the traditional audit paradigms simply cannot hold a candle to AI, which operates in a rapidly changing environment.

By the time the audit report makes its way to management, the underlying factors might have changed. The model can continuously evolve, and along with changing user behavior, output too can breach any guardrails that the audit might have recommended. Add to that, AI vendors frequently share updates and may introduce features or integrations that operational teams may rapidly absorb.

This may make the report worthless, not because the auditors failed to do their job, but because the production version is now materially different from the one that was audited in the last quarter of even a month before.

The photograph problem: Traditional audit models come up short

Traditional audit mechanisms relied on taking a snapshot of data and elements at a given point in time. Just like a photograph, they showed the status of a system at a specific time, and the entire analysis was built around it. It worked well and still works for IT infrastructure, ERP systems or internal databases that are bound by planned release and update cycles. The picture does not change overnight, and audits played a critical role in generating guidelines and best practices.

In contrast, AI systems are altogether a different beast. They are closer to living organisms than a photograph, and changes can be frequent and substantive. Take the example of an AI customer service agent that can suddenly respond differently to sensitive user inputs. Or an AI-powered fraud detection system, which starts flagging false positive patterns due to changes in user behavior.

Key blind spots undermining your audit

When it comes to undermining your AI audit, there are three key forces at play. You can also consider them as blind spots that governance typically misses out on.

Your vendor keeps updating the model:

It is common knowledge that most companies do not build their AI models from scratch. Instead, they rely on foundational models from major players like OpenAI or Anthropic to build their specific use cases. While the use cases may be highly customized, updates to the fundamental models can change how the AI system responds.

Add to that, the updates can be quite frequent, even in the span of a few weeks. Moreover, the vendor does not really take your permission before releasing an update and barely ever shares a changelog with most companies. How the update impacts your use case and risks associated with it are entirely yours to handle.

Data drift reduces AI reliability:

As AI systems interact with your stakeholders, including your customers, it keeps evolving based on the data it interacts with. User behavior and market conditions also change, and the kind of questions that AI gets asked a year after its initial different maybe quite different from the test data it was trained on.

Essentially, this can lead to a drift where the world the AI was trained on no longer exists, where it currently operates. Data drift is also extremely difficult to detect, as on the surface, everything seems to be working.

Changes in AI usage in your organization:

How your organization evolves using AI can also create blind spots over a period of time. Say, at the time of deployment and initial audit, only a few departments were using AI, and some of them only in pilot form. Over time, adoption may have increased, and new workflows built on older ones. This may have exposed the organization to risks that were not there at the time of the audit.

The false confidence trap of AI audits

AI audits have their value, but they should not be treated as a destination. Relying on periodic AI audits as gospel would trap your teams in false confidence that everything is working just fine until suddenly, they come across deviations that are too great to ignore.

The false confidence trap may expose the organization to both reputation and regulatory risks.

Implementing continuous oversight for effective governance

Continuous oversight does not necessarily mean frequent audits. Instead, an ideal oversight mechanism would focus on establishing triggers that would initiate an immediate review. Such triggers can be a vendor update or a drastic change in usage pattern. In addition, unexpected output or a change in usage context, too, can be valid triggers.

Another crucial element of continuous oversight involves asking questions. This could involve asking the vendor about the updates they are planning to release and how they can impact your specific case. It may also involve having active interactions with the operation teams about how AI output is changing and how users are reacting to the results.

Last but not least, one needs to assign clear responsibility to a team or individual for owning the AI risk. They would also be responsible for implementing corrective measures whenever they notice AI deviating from its expected performance.

Companies should look at AI governance as a part of operational discipline and avoid treating it like a checklist for meeting compliance requirements. With continuous oversight, organizations would be way better placed to handle issues when something unexpected happens and take immediate corrective measures. Such an approach will go a long way in making AI systems reliable and improving regulatory readiness.