AI Lifecycle

The AI Lifecycle describes the stages an AI system passes through: ideation, design, data collection, training, validation, deployment, monitoring, iteration, and retirement. Each stage involves specific controls, artifacts, and roles. Ideation requires use-case approval and risk assessment. Design requires architecture documentation. Data collection requires consent and lineage tracking. Training requires reproducibility evidence. Validation requires test results, fairness analysis, and AI risk management sign-off. Deployment requires AI compliance review and infrastructure approval. Monitoring requires drift detection, incident response, and continuous evaluation. Retirement requires careful handling of data and downstream dependencies. Frameworks like NIST AI RMF, ISO/IEC 42001, and Microsoft's Responsible AI Standard codify lifecycle expectations. AI governance programs use lifecycle thinking to apply the right controls at the right time, supporting AI compliance, AI ethics, and responsible AI from cradle to grave across every AI asset in the enterprise AI portfolio.

Centralpoint Covers the Lifecycle, Cradle to Grave: Oxcyon's Centralpoint AI Governance Platform supervises AI from first idea to retirement. Model-agnostic across OpenAI, Gemini, Llama, and embedded models, Centralpoint meters every interaction, keeps prompts and skills on-prem, and embeds lifecycle-aware chatbots into your portals via one JavaScript line.


Related Keywords:
AI Lifecycle,,