⚡ Quick Summary
This December 2025 paper from the Centre for Information Policy Leadership (CIPL) tackles one of the most persistent tensions in AI governance: how to reconcile data-hungry AI systems with the data minimization principle embedded in global privacy laws. Rather than treating minimization as a blunt obligation to “use less data,” the paper reframes it as a contextual, risk-based assessment of necessity, proportionality, and purpose across the AI lifecycle. Drawing on GDPR doctrine, comparative global privacy regimes, and emerging regulatory guidance, CIPL makes a strong case that large and diverse datasets can be compatible with data minimization where they are demonstrably required to ensure accuracy, fairness, robustness, or bias mitigation. The paper is both normative and practical, offering organizations a structured necessity test and concrete technical and organizational safeguards to operationalize minimization without undermining innovation.
🧩 What’s Covered
The document is structured around a progressive reinterpretation of data minimization in light of modern AI development. It begins by mapping how the principle is defined across jurisdictions, showing broad global convergence around necessity and proportionality, alongside meaningful differences in strictness (notably between the EU and some US state laws). It then explains why AI, especially foundation models, generative AI, and agentic systems, puts pressure on traditional minimization logic due to their reliance on scale, diversity, and iterative learning.
A core contribution is the clarification that data minimization does not impose quantitative limits but qualitative ones: each data point must be justified by a legitimate purpose that cannot reasonably be achieved with less intrusive means. This is reinforced through discussion of fairness and bias mitigation, where the processing of sensitive or large-scale data may be not only permissible but required.
The paper walks through the AI lifecycle phase by phase, outlining tailored minimization measures for data sourcing, preprocessing, training, fine-tuning, deployment, and monitoring. It highlights the role of privacy-enhancing technologies such as federated learning, differential privacy, synthetic data, and secure computation, while acknowledging their trade-offs.
A particularly practical element is the proposed “necessity test” framework, which guides organizations through defining purpose, evaluating alternatives, justifying data scope, limiting retention, and embedding accountability. The analysis is rounded out with a detailed overview of current regulatory positions from bodies such as the EDPB, EDPS, ICO, CNIL, and German DPAs, illustrating a clear regulatory shift toward contextual and risk-based interpretations of minimization in AI.
💡 Why it matters?
This paper directly addresses a real-world governance dilemma faced by AI developers, deployers, and regulators: how to comply with data protection law without crippling AI performance or safety. Its reframing of data minimization as a principle of justified use, rather than data scarcity, aligns closely with the EU AI Act’s emphasis on representative and statistically sound datasets. For organizations, it provides defensible language, frameworks, and safeguards that can be operationalized in DPIAs, AI risk assessments, and governance programs. For regulators and policymakers, it offers a coherent narrative that supports innovation while preserving core privacy values, helping avoid enforcement approaches that inadvertently increase bias, inaccuracy, or harm.
❓ What’s Missing
The paper largely focuses on model developers and large-scale AI systems, with less attention to downstream deployers integrating third-party models into specific business processes. It also stops short of providing concrete, sector-specific examples (e.g. health, finance, employment) showing how the necessity test would be applied in practice. Finally, while accountability is emphasized, the interaction between data minimization decisions and emerging AI auditing or conformity assessment regimes is only implicitly addressed rather than explored in depth.
👥 Best For
AI governance leads, privacy and data protection officers, legal counsel, policymakers, and regulators working at the intersection of GDPR, global privacy laws, and AI regulation. It is particularly valuable for organizations developing or training foundation models, high-risk AI systems, or large-scale generative AI tools.
📄 Source Details
Centre for Information Policy Leadership (CIPL), Hunton Andrews Kurth LLP, December 2025.
📝 Thanks to
The Centre for Information Policy Leadership for consistently producing pragmatic, regulator-aware guidance that bridges legal doctrine and real-world AI development.
