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.
Continuous Monitoring + Lifecycle Management is essential to ongoing trustworthy AI per Japan AI Guidelines for Business + integrates Safety + Accountability + Transparency Principles + addresses post-deployment risks.
JP-AIG-Continuous-Monitoring-Lifecycle-Model-Evaluation-Performance-Drift-Post-Deployment · Japan AI Guidelines Continuous Monitoring + AI System Lifecycle Management + Model Evaluation + Performance Drift + Concept Drift + Post-Deployment + Retraining Triggers + Safe Update + Decommissioning + Model Card Versioning →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 →V-Model Lifecycle - specification phases mirrored by verification phases. SPECIFICATION SIDE: (a) USER REQUIREMENTS SPECIFICATION (URS) - what the business needs the system to do + GxP requirements + regulatory + audit + user-defined functional + non-functiona...
GAMP5-Lifecycle-VModel-URS-FS-DS-IQOQPQ · V-Model Lifecycle - URS + FS + DS + IQ + OQ + PQ + Traceability →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 →Apply risk management across AI lifecycle stages: inception, design, data, build, verify, deploy, operate, decommission.
23894-A.3 · Lifecycle Risk Considerations →Questions people ask about ai lifecycle management
What is AI Lifecycle Management?
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