Topic

Lifecycle Management

Lifecycle management in AI governance refers to the systematic oversight of artificial intelligence systems from initial design and development through deployment, monitoring, and eventual retirement or replacement. This encompasses version control, model updates, data refresh cycles, and documentation of changes to ensure AI systems remain accurate, compliant, and aligned with organizational policies over time. Effective lifecycle management is critical for enterprises to maintain audit trails, manage technical debt, mitigate drift in model performance, and demonstrate accountability to regulators and stakeholders throughout an AI system's operational period.

2 items

ResearchUS2026-06-01

A Cancer Center's One-Year AI Governance Program Registered 26 Models and Offers a Replicable Blueprint for Healthcare Compliance Teams

A Comprehensive Cancer Center published a peer-reviewed account of a one-year Responsible AI governance program that registered and monitored 26 AI models, 2 ambient AI pilots, and 33 nomograms. The program established an AI Governance Committee, a formal model registry, a risk assessment tool, lifecycle management tooling, and an operating model called iLEAP with structured decision gates covering legal, ethics, adoption, and performance. The article, published in PMC, provides granular implementation detail that compliance teams at healthcare and other regulated organizations can adapt directly.

model registry healthcare AI lifecycle management AI governance committee risk assessment

ResearchGlobal2025-05-30

Federated AI Governance Models Demand New Central Controls, Atos Playbook Finds

Atos published a practitioner-oriented AI governance playbook on May 30, 2025, arguing that effective enterprise AI adoption requires a federated model combining local business-unit autonomy with centralized governance controls. The playbook identifies five critical control domains: centralized governance structures, identity and lifecycle management, data governance, agent deployment oversight, and executive review of AI use cases prior to scale-up.

AI governance agent governance enterprise controls federated governance lifecycle management