The AI audit burden: Why ‘Explainable AI’ is the key

A recent article in FinTechGlobal posed the following question: ”Is transparency the final barrier to true AI compliance?” I thought this question hit on a key issue for every Chief Compliance Officer (CCO) and compliance professional who is turning to AI for their compliance program. It boils down to the issue of trust.

Not trust in the technology in some abstract sense, but trust in the specific decision that an algorithm makes about a customer, a transaction, or a potential regulatory breach. The bridge between automated efficiency and true regulator-ready trust is transparency, or, in compliance speak, auditability. In AI-speak, it is termed ”Explainable AI.”

The compliance profession has lived through many waves of automation, from early transaction monitoring engines to rule-based KYC workflows. Those innovations improved speed, but they did not replace the need to demonstrate judgment. Today’s AI tools have greatly accelerated that tension. They promise speed and accuracy, yet too many land in the market as black boxes. The model says “approved” or “flagged,” but the compliance officer is left with no defensible explanation for why. When a regulator comes calling, that opacity becomes a liability.

For nearly 20 years, I have said the three most important parts of every compliance program are: Document, document, document. But you can only document what you can explain. With AI, many compliance professionals are asking how they can still provide the documentation if a regulator, or even an internal audit, comes knocking.

In the article, Cardamon CEO Areg Nzsdejan framed the issue that automation has delivered efficiency, but accountability lags behind. Explainable AI, “transforms that accountability gap into something measurable and operational by showing the logic behind each result — what data was used, which thresholds were triggered, and what reasoning drove the final outcome.”

That shift turns AI from a mysterious engine into something much closer to a decision-support system that compliance can stand behind through transparency and auditing.

Explaining AI outputs to regulators, stakeholders

From a compliance perspective, this is not a philosophical matter. It is a regulatory one.

Under GDPR, individuals have a right to understand how automated decisions affect them. Firms must be able to articulate why an action occurred, not merely what action the system took. Explainable AI operationalizes this principle by generating human-readable explanations that support appeals, internal reviews, and regulator inquiries. In practice, this means clear audit trails, structured decision logs, and defensible rationale, which are all the raw materials of regulatory trust.

Auditability sits at the forefront of that trust equation. Without transparency, teams are pulled into retroactive rationalization, hunting through spreadsheets to recreate why a model fired an alert months earlier. With Explainable AI, every decision leaves a trail of evidence. That shift does more than streamline audits. It sets the foundation for a compliance culture where automated outcomes are inherently defensible and not products of guesswork.

Indeed, Explainable AI can be seen as the backbone of “human in the loop” workflows. Transparency lets users verify, collaborate on, and revise AI-generated results. It strengthens stakeholder confidence and reduces bias by grounding outputs in traceable logic rather than opaque judgment. GDPR’s requirements demand meaningful insight into the logic of an automated decision, not a symbolic gesture toward disclosure. That is not only a compliance obligation but a governance expectation for any firm deploying AI at scale.

Taken together, it is clear that transparency is no longer an enhancement for your compliance program. Rather, it should be seen as a program that is infrastructure. If an AI system cannot explain itself, then it should not be making regulatory decisions. And if firms cannot demonstrate how an automated decision was reached, then their compliance posture remains at risk, no matter how fast or sophisticated the tooling behind it.

Explainable AI will not eliminate human judgment. Nor should it. But it will define which firms can defend their automated decisions and which firms will find themselves on the wrong side of a regulator’s inquiry. In the era of AI-enabled compliance, transparency is not merely the final barrier. It is the gatekeeper of trust.