We’re moving from quarterly sample-based SOX testing to continuous monitoring using AI-driven anomaly detection across our ERP transactions. The system flags exceptions in near real-time and routes them to control owners, which has already reduced our testing cycle time noticeably.
The challenge we’re running into now is explainability for audit purposes. Our external auditors are asking how the AI determines what constitutes an anomaly, what thresholds are used, and how decisions are documented. The vendor provides recommendations but doesn’t surface the decision logic in a way that satisfies audit requirements. We’ve been manually reconstructing rationales after the fact, which defeats the time savings we gained.
Has anyone successfully implemented explainable AI for SOX or other compliance workflows? What approach did you take to generate audit-ready evidence automatically, and how did you position AI capabilities with external auditors to build their confidence in the system?
One thing we’ve learned is to document drift monitoring explicitly. Auditors want to know that the model’s performance characteristics haven’t changed over time without review. We track precision metrics monthly and log any significant shifts along with the remediation steps. That ongoing monitoring documentation became part of our control evidence and showed auditors we weren’t just deploying AI and walking away.
What helped us was building evidence capture directly into the workflow. When the AI flags an exception and the control owner investigates it, the system automatically logs their notes, the data they reviewed, and their final decision. That creates an immutable audit trail without anyone having to go back and reconstruct what happened. We also added a feature where the system suggests a rationale based on the data, and the reviewer can edit or accept it. That way the explanation is generated in real time, not after the fact.
Position it as a copilot, not autopilot. That’s what got our auditors comfortable. We explicitly told them that AI handles pattern recognition and scale but humans retain accountability for control design, exception triage, and final sign-off. We documented that framing in our control narratives and walked them through a few live examples during interim testing. Once they saw people were still making the judgment calls, their concerns dropped significantly.