AI passes industry privacy and governance exams at expert level

The capabilities of leading artificial intelligence systems to meet professional human standards in regulatory compliance, privacy program management, and AI governance have been put to the test, and the results suggest that top-tier models are already operating at, or above, expert levels. A new study provides an in-dept analysis of large language model (LLM) performance against established certification benchmarks.

Published under the title Can We Trust AI to Govern AI? Benchmarking LLM Performance on Privacy and AI Governance Exams, the research assessed ten prominent open and closed-source models from OpenAI, Anthropic, Meta, DeepSeek, and Google DeepMind. The models were evaluated on four official sample exams from the International Association of Privacy Professionals (IAPP): the Certified Information Privacy Professional/United States (CIPP/US), Certified Information Privacy Manager (CIPM), Certified Information Privacy Technologist (CIPT), and Artificial Intelligence Governance Professional (AIGP). These exams are widely recognized as industry standards for legal, managerial, technical, and ethical expertise in the field.

Can AI models consistently meet human certification standards?

The study tested the models in a closed-book, zero-shot setting with identical prompts and no access to external resources. Each exam was scored according to the IAPP’s pass threshold, which typically equates to correctly answering around 85% of the questions. The findings show that several frontier models not only cleared the passing bar but did so with scores comfortably above the levels achieved by certified human professionals.

Google DeepMind’s Gemini 2.5 Pro emerged as the overall top performer, achieving an average score of 92.1% across all four exams. OpenAI’s GPT-5 followed closely at 91.3%, while DeepSeek’s R1 secured 90.2%. Gemini 1.5 Pro, GPT-5-Mini, and Google’s open-weight Gemma-3-27B-IT all scored in the high 80s, indicating consistent competence across legal, managerial, and technical domains. At the lower end, Meta’s LLaMA-3-8B-Inst trailed significantly with an aggregate score of just 65.3%, underscoring the gap in capabilities between large-scale frontier models and smaller, resource-constrained systems.

Exam-by-exam analysis revealed notable strengths and weaknesses. On the CIPP/US legal certification, GPT-5 led with 93.4%, closely followed by Claude 3.7 Sonnet, Gemini 2.5, and other large models. State privacy laws proved the most challenging subdomain, with only the best-performing models maintaining strong scores. The CIPM exam, which focuses on privacy program governance and operational management, showed the widest performance gap. Gemini 2.5 topped the list here as well, while LLaMA-3-8B recorded just 57.8%.

On the technically oriented CIPT exam, DeepSeek-R1 and Gemini 2.5 shared the lead with 92.2%, whereas Anthropic’s models, typically strong in reasoning tasks, posted comparatively weaker results. The AIGP exam saw Gemini 2.5 score 93.9%, with most other top-tier models clustered above 90% and even GPT-5-Mini outperforming its flagship counterpart.

How do domain strengths influence exam outcomes?

The researchers found a clear link between a model’s training focus and its exam performance. Models that excelled in areas aligned with their core training, such as legal reasoning, technical privacy engineering, or AI ethics, tended to perform strongly across multiple assessments. Gemini 2.5 maintained high marks in every domain, topping both the AIGP and CIPT exams while also performing well on the CIPM and CIPP/US. DeepSeek-R1 displayed similar versatility, combining strong technical and governance scores with solid results in legal and managerial areas.

Subdomain analysis showed that advanced models consistently achieved perfect or near-perfect results in areas like government and court access to private-sector data, workplace privacy, and privacy-by-design principles. However, gaps remained in more specialized areas. For example, on the CIPT exam, no model exceeded two-thirds accuracy on emerging privacy technologies and privacy-enhancing strategies, highlighting the need for further domain-specific fine-tuning.

The study also measured correlation between exam domains. AI governance knowledge (AIGP) was strongly linked to both legal privacy expertise (CIPP/US) and technical privacy skills (CIPT), with correlation coefficients above 0.9. In contrast, managerial privacy content (CIPM) correlated weakly with other exams, suggesting that privacy program management requires a distinct skill set that is less developed in current LLM training.

What do these results mean for the future of AI in high-stakes governance?

The conclusion is clear: leading LLMs are now capable of passing, and in many cases excelling at, professional certification benchmarks in complex, regulated domains. This has significant implications for how AI might be deployed in high-stakes governance roles. The research indicates that well-trained models could already provide valuable assistance to privacy professionals by drafting compliance documents, answering regulatory queries, and conducting automated risk assessments.

At the same time, the authors stress that performance is not solely determined by model size. The open-weight Gemma-3-27B-IT, fine-tuned on governance-specific data, matched or outperformed some much larger proprietary systems. This points to the potential of targeted fine-tuning to close capability gaps without requiring extreme computational scale. However, the performance disparities on CIPM highlight a need for more focused training on organizational governance frameworks such as ISO 27701, as well as scenario-based managerial content.

The researchers caution that while these exams measure core competencies, they do not encompass the full scope of judgment, contextual understanding, and real-world decision-making needed for effective privacy and AI governance. Nevertheless, the fact that top-tier LLMs can match or surpass human certification performance suggests a readiness for AI to augment, if not yet replace, certain professional functions in compliance and governance.

The authors further recommend extending this benchmarking to other jurisdictions and related certifications, such as the CIPP/E for European privacy law or technical domains like ethical hacking. They also note that prompt construction can significantly impact results, suggesting that more context-rich and scenario-based evaluations could offer deeper insights into model readiness.