RELEASE-AI: A Lifecycle Framework for Artificial Intelligence Development and Disclosure Control in Trusted Research Environments

TY - UNPB

T1 - RELEASE-AI

T2 - A Lifecycle Framework for Artificial Intelligence Development and Disclosure Control in Trusted Research Environments

AU - Crespi-Boixader, Alba

AU - Li, Simon

AU - Liley, James

AU - Ward, Laura

AU - Cole, Christian

AU - Smith, Jim

N1 - Copyright (c) 2025 The Authors.

PY - 2025/9/19

Y1 - 2025/9/19

N2 - 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 overall project risk assessment 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, this approach enables innovative AI research while maintaining rigorous data protection standards and public trust.

AB - 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 overall project risk assessment 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, this approach enables innovative AI research while maintaining rigorous data protection standards and public trust.

KW - RELEASE-AI

KW - TRE

KW - AI

KW - Artifical intelligence

KW - machine learning

KW - Disclosure control

KW - Sensitive data

KW - trusted research environment

U2 - 10.5281/ZENODO.17159193

DO - 10.5281/ZENODO.17159193

M3 - Preprint

BT - RELEASE-AI

PB - Zenodo

ER -