AI Lifecycle Management
What is AI Lifecycle Management?
The governance of AI systems throughout their entire lifecycle from conception and development through deployment, monitoring, and retirement.
Frameworks that govern ai lifecycle management
What the standards actually require on ai lifecycle management
Requirements naming ai lifecycle management across 6 standards, quoted from the control text.
Measurement results regarding AI system trustworthiness in deployment context(s) and across AI lifecycle are informed by input from domain experts and other relevant AI actors to validate whether the system is performing consistently as intended.
AIRMF-MS-4.2 · Measurement results regarding AI system trustworthiness in deployment contexts and across the AI lifecycle are informed by input from domain experts and other relevant AI actors to validate whether the system is performing consistently as intended, and results are documented →Understand the enterprise's role in the AI lifecycle and keep an up-to-date registry of linked AI systems; understand potential adverse impacts from internal sources (incident monitoring, oversight bodies) and external sources (human rights institutions, AI ob...
oecd-due-diligence-guidance-for-responsible-ai-2026::2.1.G2 · 2.1.G2 Initial scoping of risks: enterprises in the AI system lifecycle →Operate cross-cutting requirements per NIST SP 800-63-3 / 63A / 63B / 63C. Threat Model per AAL: (a) per Section 8 of SP 800-63B + Section 4.4 of SP 800-63-3 (cover impersonation + verifier compromise + session hijacking + replay + phishing + denial of service...
NISTSP63-8 · Threat Model, Lifecycle Management, Privacy, Equity, Records, and Subscriber Communication →Ensure AI systems do not produce unjust bias or discriminatory outcomes and that similar individuals are treated similarly across the AI lifecycle.
AIGE-P2 · Fairness and Equity →At data preprocessing: through privacy by design and similar approaches, collect training data properly and, where it includes third parties' personal data or material needing attention to intellectual property rights, ensure it is handled lawfully throughout...
japan-ai-guidelines::D-2.i · D-2) i. Proper data training (also D-4) i.) →Register all AI use cases in the AI Assessment Framework Platform and keep accurate and complete records across the AI system lifecycle; agencies may use their own systems but must synchronise records to the AIAF Platform.
nsw-ai-operational-policy-2026::4.5 · 4.5 Register all AI use cases in the AIAF Platform →Questions people ask about ai lifecycle management
What is AI Lifecycle Management?
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See how AI Lifecycle Management applies across compliance frameworks
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