What is AI system lifecycle governance?
AI system lifecycle governance covers the full span of an AI system's existence: from initial registration, through business and technical intake, compliance screening, risk classification, obligation mapping, evidence management, supervisor approval, operational monitoring, re-screening when conditions change, and eventual retirement or replacement.
It treats governance not as a gate the system passes through once, but as a continuous state that accompanies the system throughout its entire operational life. The governance record grows with the system — each change, each review, each re-screening adds to the reconstructable history.
Key points
- The lifecycle begins at registration and does not end at approval. Post-approval governance — monitoring, evidence maintenance, re-screening — is equally important.
- Each lifecycle phase produces governance events that become part of the permanent record.
- System retirement or replacement is a governance event too. The governance record must show when and why a system was decommissioned.
- Lifecycle governance connects to post-market monitoring (Art. 72), continuous compliance, and regulatory change monitoring.
Why it matters
Most compliance approaches focus on the initial assessment: screen the system, classify the risk, document the obligations, get approval. Lifecycle governance recognises that this is the beginning, not the end. An AI system may operate for years after initial approval — and during that time, the system changes, the regulation evolves, and the evidence ages. Without lifecycle governance, the compliance state decays silently.
How EAB approaches this
EAB governs the full lifecycle. The Registry captures birth. Governance Flow manages the initial assessment. Continuous Compliance maintains the ongoing state. Re-Screening Queue handles change events. The audit trail preserves the complete lifecycle history as an unbroken chain of custody.