AI model lifecycle management: Staying compliant from start to finish

AI models don’t stay static, and neither do the regulations around them. As companies build out their AI governance programs, understanding the lifecycle of each AI model—from creation to decommissioning—has become essential. Here’s what you need to know about each stage and how to ensure compliance along the way. 

What is AI model lifecycle management? 

AI lifecycle management refers to overseeing each phase of an AI model's "life" within an organization. It includes initial design, testing, deployment, and ongoing updates, all the way to retirement. Lifecycle management helps organizations keep their models ethical, compliant, and efficient.   

Why does it matter for your organization? 

Regulations like the EU AI Act and emerging US state laws increasingly require AI systems to meet transparency, fairness, and accountability standards. Effective lifecycle management ensures your organization meets these regulatory needs at every stage, reducing risks associated with out-of-date models, unmonitored data use, or undocumented changes.   

How do you implement it effectively? 

To get started, establish a clear process for each stage: 

• Design and development: Build in accountability and transparency checks as your models are developed. Ensure data sources adequately represent the populations they will impact. 

• Deployment: Document every deployment detail, including the training methods, level of human intervention, data inputs, and performance metrics. 

• Monitoring and updating: Use regular evaluations to catch potential issues. Leverage model observability tools to monitor model performance, quality, and compliance with regulatory standards; configure alerts for critical system components, performance thresholds, and compliance violations. 

• Decommissioning: When a model reaches the end of its useful life, decommission it responsibly by documenting the reasons, archiving relevant data, and updating your AI inventory. 

How can OneTrust help?

Solutions like OneTrust Data & AI Governance can simplify lifecycle management, from initial risk assessments to ongoing monitoring. For a deeper dive, download the eBook Establishing a Scalable AI Governance Framework, coauthored by OneTrust and Protiviti, to explore essential steps for building AI inventories, managing shadow AI, and operationalizing governance to meet evolving compliance needs.

Timeline: AI's emerging trends and journey  

• The G7 Data Protection Authorities (DPAs) endorsed an AI governance plan that emphasizes privacy, trust, and child safety. This endorsement underscores the need for ethical, privacy-conscious AI frameworks that build trust and comply with global standards.   

• The New York State Department of Financial Services (NYDFS) released guidance addressing cybersecurity risks arising from AI and recommending controls to mitigate those risks. Check out what the guidance recommends here.
• Google has launched SynthID, an open-source tool that watermarks and identifies AI-generated content. You can learn more about AI content watermarking in this Nature article. How will this affect organizations? It will be a requirement in the California's AI Transparency Act, effective January 1, 2026.    

• The iconic Brenda Lee’s “Rockin’ Around the Christmas Tree” has an authorized Spanish rework powered by responsibly trained AI. This project underscores the importance of managing intellectual property rights, transparency, and ethical AI use by prioritizing artist approval and ethical training methods. Listen to it here and get into the Holidays mood. 
• The United Nations Economic Commission for Europe (UNECE) published a declaration on AI-embedded products setting standards for trust and risk assessment. Read on here. 

Your AI 101: What are...?  

Large Language Models (LLMs) are AI systems that can perform a wide range of distinct tasks, such as recognizing and generating text. They're not limited to text and can also process and output other media like videos, images, or audio files. With billions of parameters, LLMs recognize patterns and generate quick responses to a range of prompts or questions. Despite their benefits, LLMs raise privacy and security concerns, making ethical oversight essential to ensure responsible use and trustworthiness in applications. 

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Charles Kerrigan , a partner at CMS UK , is an expert in finance, specializing in emerging technologies including AI. He consults on AI and digital assets for public bodies, policy makers, standards institutions, and corporations. Charles also serves on advisory boards, including the UK All Party Parliamentary Group on Artificial Intelligence (APPG AI).