Creating Explainable AI Models for Sarbanes-Oxley Audits

AI-driven decision-making is revolutionizing corporate finance, but regulatory frameworks like the Sarbanes-Oxley Act (SOX) demand transparency and accountability.

Finance teams using machine learning for forecasting, fraud detection, or internal control monitoring must ensure their models are explainable—auditable by both humans and regulators.

This post explores how to create explainable AI (XAI) systems that align with SOX audit requirements while preserving model performance.

📌 Table of Contents

• Why SOX Audits Demand Explainable AI
• What Counts as “Explainable” Under SOX
• Top XAI Frameworks and Tools
• Mapping AI to Internal Control Objectives
• Audit Readiness Tips for AI Teams

Why SOX Audits Demand Explainable AI

SOX Section 404 requires public companies to document and test internal controls over financial reporting (ICFR).

When AI systems influence or automate financial decisions, companies must prove how those decisions were made—and by whom.

Opaque “black box” models present unacceptable risk under audit standards.

What Counts as “Explainable” Under SOX

SOX-aligned AI models should provide:

✅ Traceability of inputs, outputs, and weights

✅ Human-readable explanations of decisions

✅ Version control and audit logs

✅ Controls against model drift and bias

These criteria help satisfy external auditors and internal risk committees.

Top XAI Frameworks and Tools

🧰 SHAP (SHapley Additive Explanations): Breaks down prediction impact by feature

🧰 LIME (Local Interpretable Model-Agnostic Explanations): Explains individual predictions for any black-box model

🧰 IBM AI Explainability 360: Enterprise-grade toolkit for regulators and auditors

🧰 What-If Tool (Google): Visual exploration of model behavior and fairness

Mapping AI to Internal Control Objectives

Explainable AI should directly support ICFR pillars:

📊 Accuracy – AI outputs should match GAAP-based reporting standards

🔐 Authorization – Ensure only validated inputs influence reporting forecasts

🕒 Timeliness – Document update cycles and model retraining frequencies

🔁 Reviewability – Allow CFOs and audit committees to override or annotate AI outcomes

Audit Readiness Tips for AI Teams

✔️ Maintain model documentation as part of SOX control narratives

✔️ Validate all AI transformations and preprocessing logic

✔️ Store training and testing datasets with timestamps and lineage

✔️ Conduct internal mock audits before external reviews

Explore AI & Compliance Readiness Platforms

Keywords: explainable AI SOX, audit-ready machine learning, XAI for finance, SOX AI compliance, interpretable models regulation