RELEASE-AI:Protecting Sensitive Data Across The AI Lifecycle Disclosure Risks and Mitigations in Trusted Research Environments
Year: 2025
Type: conference-paper
Abstract: Trusted Research Environments (TREs) provide secure access to personal and sensitive data, such as Electronic Healthcare Records (EHRs), for approved research. The increasing use of Artificial Intelligence (AI) and Machine Learning (ML) in health research introduces new challenges for managing privacy risks of individuals’ data. We present a comprehensive framework that embeds mitigation strategies throughout the entire AI project lifecycle, structured across six project phases: design, governance, development, evaluation, disclosure control, and release. This framework aims to empower all stakeholders - researchers, project teams, output checkers, and TRE staff - with clear, phase-specific recommendations on which measures and checks are necessary before model release to help identify potential disclosure risks to data. It promotes early identification of risks with corresponding mitigations and ensures responsibilities are clearly assigned to relevant actors at each stage, from initial planning through to deployment and monitoring. Mitigation strategies include good AI/ML practices, both in terms of code and documentation, privacy-enhancing techniques during training and evaluation, restricting model access via secure query systems, licencing agreements, and adversarial attack testing using tools like SACRO-ML. This process highlights the need to train everyone involved appropriately with relevant role-specific material. A novel tiering system for disclosure control is proposed, categorising AI projects based on the likelihood of attack and associated sensitive data leakage risks. By integrating a lifecycle-focused risk management process with a scalable disclosure control tiering system, this approach enables innovative AI research while maintaining rigorous data protection standards and public trust.
Authors: Crespi Boixader, Alba; id_orcid 0000-0002-1576-9723, Li, Simon; id_orcid 0000-0002-7929-2442, Liley, James, Ward, Laura; id_orcid 0000-0003-0880-0662, Cole, Christian; id_orcid 0000-0002-2560-2484, Smith, Jim
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: UK Research and Innovation
Award: G5235844062
OpenAlex ID: W7111521682
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