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Can We Trust AI to Govern AI? Benchmarking LLM Performance on Privacy and AI Governance Exams

Authors: Zane Witherspoon, Thet Mon Aye, YingYing Hao

Published: 2025-08-12

arXiv ID: 2508.09036v1

Added to Library: 2025-08-14 23:08 UTC

Risk & Governance

📄 Abstract

The rapid emergence of large language models (LLMs) has raised urgent questions across the modern workforce about this new technology's strengths, weaknesses, and capabilities. For privacy professionals, the question is whether these AI systems can provide reliable support on regulatory compliance, privacy program management, and AI governance. In this study, we evaluate ten leading open and closed LLMs, including models from OpenAI, Anthropic, Google DeepMind, Meta, and DeepSeek, by benchmarking their performance on industry-standard certification exams: CIPP/US, CIPM, CIPT, and AIGP from the International Association of Privacy Professionals (IAPP). Each model was tested using official sample exams in a closed-book setting and compared to IAPP's passing thresholds. Our findings show that several frontier models such as Gemini 2.5 Pro and OpenAI's GPT-5 consistently achieve scores exceeding the standards for professional human certification - demonstrating substantial expertise in privacy law, technical controls, and AI governance. The results highlight both the strengths and domain-specific gaps of current LLMs and offer practical insights for privacy officers, compliance leads, and technologists assessing the readiness of AI tools for high-stakes data governance roles. This paper provides an overview for professionals navigating the intersection of AI advancement and regulatory risk and establishes a machine benchmark based on human-centric evaluations.

🔍 Key Points

  • Evaluation of ten leading LLMs against industry-standard privacy and governance certification exams, revealing their performance vis-à-vis human benchmarks.
  • Identified strengths in legal and technical knowledge among top-performing models, notably Gemini 2.5 Pro and OpenAI's GPT-5, which consistently exceed passing thresholds.
  • Highlighted domain-specific gaps in smaller models, particularly in the area of Privacy Program Management (CIPM), indicating that size and training focus significantly impact performance.
  • Correlation analysis among exam results indicates areas where model competencies overlap, suggesting that advances in one domain can enhance understanding in others.
  • Provides practical insights for privacy professionals on leveraging LLMs for regulatory compliance and governance tasks, underscoring the potential of AI in high-stakes privacy domains.

💡 Why This Paper Matters

This paper is significant in demonstrating that leading large language models can achieve human-level competency in privacy law and AI governance, thus providing a foundational understanding for organizations considering AI tools for compliance purposes. The insights gained from the evaluation not only inform the readiness of these AI systems for operational roles but also pose questions about their technical development and optimization to bridge existing knowledge gaps.

🎯 Why It's Interesting for AI Security Researchers

AI security researchers may find this paper particularly relevant as it explores the capabilities of LLMs in regulatory compliance—a critical aspect of AI governance. The benchmarks and findings offer clues on how LLMs handle sensitive data, comply with privacy laws, and their potential implications for security frameworks in AI applications. Understanding model performance on governance exams can aid researchers in assessing AI risks and developing more robust security measures.

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