AI lifecycle management
AI lifecycle management is the process of governing, monitoring, and optimizing artificial intelligence systems from development to decommissioning to ensure transparency, compliance, and ethical use.
What is AI lifecycle management?
AI lifecycle management provides a structured framework for overseeing artificial intelligence systems at every stage—design, data collection, model training, deployment, monitoring, and retirement. It ensures that AI remains transparent, reliable, and aligned with regulatory and ethical standards throughout its use.
This approach supports compliance with evolving frameworks such as the EU Artificial Intelligence Act (EU AI Act) and the General Data Protection Regulation (GDPR), which emphasize documentation, risk management, and human oversight.
AI lifecycle management also integrates principles of AI governance, enabling organizations to manage accountability and ensure continuous model improvement.
Why AI lifecycle management matters
AI systems evolve over time, creating potential risks related to accuracy, bias, and compliance. AI lifecycle management ensures these systems are consistently reviewed, audited, and updated to maintain ethical and legal standards.
For organizations, it provides visibility into how AI systems make decisions, enabling traceability and accountability across departments.
Effective lifecycle management reduces operational risks, supports regulatory compliance, and builds trust among customers, regulators, and stakeholders.
How AI lifecycle management is used in practice
- Defining governance processes across data, model, and deployment stages
- Conducting AI impact assessments (AIIAs) to evaluate risks before deployment
- Monitoring performance metrics and retraining models to address drift or bias
- Maintaining AI audit trails for transparency and accountability
- Integrating Responsible AI and AI accountability principles into development workflows
- Decommissioning outdated or non-compliant models to prevent unintended consequences
Related laws & standards
- EU Artificial Intelligence Act (EU AI Act)
- General Data Protection Regulation (GDPR)
- Digital Operational Resilience Act (DORA)
- ISO/IEC 27001 (Information Security Management)
- ISO/IEC 42001 (AI Management System Standard)
How OneTrust helps with AI lifecycle management
OneTrust enables organizations to operationalize AI lifecycle management by automating governance workflows, maintaining transparency records, and ensuring compliance with global AI regulations. The platform helps monitor model risk, document decisions, and streamline audits across the AI lifecycle.
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FAQs about AI lifecycle management
The AI lifecycle includes design, data preparation, model training, validation, deployment, monitoring, and decommissioning—each governed by defined accountability and risk management controls.
The EU Artificial Intelligence Act (EU AI Act) requires ongoing oversight, traceability, and documentation, all of which are core components of lifecycle management.
While AI governance defines the policies and accountability structure for managing AI risk, AI lifecycle management operationalizes those principles across each stage of system development and deployment.
