Published: Apr 12th, 2025 by Dr. Norman Mooradian

Artificial intelligence (AI) is transforming how organizations handle data, but its dependence on vast datasets raises pressing privacy concerns. AI systems, particularly those using machine learning and deep learning, not only process personal information but also generate new insights about individuals, sometimes from seemingly unrelated data. This ability to infer sensitive details—such as political beliefs or health risks—makes managing privacy more complex than ever. A recent study I coauthored with Patricia C. Franks, and Amitabh Srivastav explores these risks from an information governance perspective, highlighting how organizations can mitigate privacy concerns while leveraging AI’s benefits.

Organizations must navigate two key challenges: ensuring compliance when using personal data to train AI models and addressing the privacy risks posed by AI-generated inferences. Traditional governance frameworks, which focus on direct data collection, struggle to keep pace with AI’s ability to uncover hidden patterns. As a result, new risk management strategies are needed.

One fundamental approach to managing personal information is data minimization. It requires that organizations collect and retain only the data necessary for specific purposes. Synthetic data—artificially generated datasets that mimic real ones—offers a promising alternative, allowing AI models to train without exposing personal information. Encryption techniques, such as homomorphic encryption, allow data to be analyzed without being decrypted, maintaining privacy even during processing. Other methods, like differential privacy, introduce controlled randomness to prevent AI from identifying individuals while still allowing useful insights.

Governance frameworks are evolving to address these risks. The concept of Privacy by Design (PbD) emphasizes integrating privacy safeguards into AI systems from the outset, rather than as an afterthought. The National Institute of Standards and Technology (NIST) AI Risk Management Framework provides a structured approach to balancing AI innovation with ethical responsibility. Additionally, Data Protection Impact Assessments (DPIAs) help organizations evaluate potential privacy risks before deploying AI-driven systems.

Despite these advances, AI’s rapid development presents ongoing challenges. The opacity of deep learning models makes it difficult to explain how decisions are made, complicating accountability. Moreover, privacy regulations, such as the GDPR, impose strict rules on data collection and retention, requiring companies to be more vigilant in managing AI-driven processes.

To responsibly harness AI’s potential, organizations must adopt a proactive approach to data governance, integrating privacy safeguards into every stage of AI development. By balancing innovation with ethical responsibility, businesses can protect personal information while leveraging AI’s transformative capabilities, ensuring trust and compliance in an increasingly data-driven world.

Reference:

Mooradian, N., Franks, P. C., & Srivastav, A. (2024). Artificial Intelligence on Data Privacy: A Risk Management Perspective. Records Management Journal. https://doi.org/10.1108/RMJ-06-2024-0013