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Product cover image for Governing Innovation: Google’s SOX Controls for AI/ML in Financial Systems

Governing Innovation: Google’s SOX Controls for AI/ML in Financial Systems

Glorin Sebastian, Eshan Bhatt

Product #:W44898
Supplier:Ivey
Discipline:Accounting, Finance
Setting:United States, 2024
Details:Case (PDF) • 8 pages • 2025-08-20

Subjects:

Corporate governanceFinance and investingAccounting

Industries:

Information technology and telecom sector

Geography:

United StatesNorth America
Your Price:$9.71

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In early 2023, Google LLC (Google) faced a pivotal decision: how to scale its use of artificial intelligence (AI) and machine learning (ML) models in financial forecasting without compromising regulatory compliance and internal control integrity. While AI/ML tools (such as predictive analytics, document parsing, and chatbot-based automation) improved efficiency, they also introduced risks such as data completeness and accuracy issues, model drift due to continuous learning, lack of transparency in decision-making, and challenges in reproducing results for audit purposes. Google had to design a risk and control strategy that balanced innovation and compliance with the Sarbanes-Oxley Act of 2002 (SOX). How could Google integrate AI/ML models into its SOX-regulated financial systems? How could it use cross-functional collaboration to overcome implementation challenges and drive both innovation and effective governance? And how could it meet future regulatory standards while reinforcing investor trust?

Learning Objectives

This case can be used in the following courses: finance or accounting information systems, or corporate governance (upper-year bachelor of business administration), internal controls and compliance (graduate level), information systems audit (graduate and executive MBA level), and corporate governance for technology-driven firms (graduate or PhD level). After working through the case and assignment questions, students will be able to do the following:
  • Understand the unique risks introduced by AI/ML models in financial reporting.
  • Analyze how traditional control frameworks must evolve to address probabilistic models.
  • Evaluate how cross-functional collaboration can support scalable governance.
  • Identify best practices for AI/ML model lifecycle controls.
    • Design risk-based control strategies aligned with regulatory requirements.
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Case Study: Governing Innovation: Google’s SOX Controls for AI/ML in Financial Systems (English version) | Ivey Publishing