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Operationalise compliance through continuous monitoring, human oversight, and incident response. AI systems are dynamic — their behaviour evolves as data distributions shift, user patterns change, and operational contexts develop. Governance must therefore operate continuously, detecting deviations, enabling oversight, and responding to incidents in real time.
Core Activities
Five operational capabilities that ensure compliance requirements continue to be met during production operation — where the Architect phase designs for compliance, the Control phase sustains it.
Post-market monitoring is a legal requirement under EU AI Act Article 72 for providers of high-risk AI systems and a practical necessity for any system with ongoing governance obligations. The monitoring system continuously observes AI system behaviour against defined performance, fairness, and compliance thresholds.
The Architecture phase defines the oversight model; the Control phase makes it operational. EU AI Act Article 14(4) requires that oversight measures enable individuals to fully understand the system's capabilities, properly monitor operation, decide not to use or override outputs, and intervene or interrupt the system.
AI governance decisions should not rest with individual operators alone. Formal governance structures provide strategic direction, resolve cross-functional conflicts, and ensure accountability at appropriate seniority. The committee includes AI/ML engineering, data science, legal and compliance, DPO, CISO, risk management, and business units.
AI systems fail in ways that differ from conventional software failures. Model outputs can become biased, discriminatory, or harmful without any visible infrastructure error. The incident response framework must account for AI-specific failure modes: model failure, discriminatory output, data breaches, adversarial attacks, availability disruption, unintended autonomous action, and fundamental rights impact.
Beyond system-level monitoring, the Control phase encompasses ongoing monitoring of the compliance programme itself and the regulatory environment. The AI regulatory landscape is evolving at pace — delegated acts, implementing acts, harmonised standards, AI Office guidance, EDPB opinions, and sector-specific updates all create new obligations.
Incident Taxonomy
AI systems fail in ways that differ substantially from conventional software failures. Model outputs can become biased or harmful without any visible infrastructure error.
Artefacts
Six categories of operational deliverable that maintain governance effectiveness throughout the AI system lifecycle.
Continuous monitoring, human oversight, and incident response — governance that operates at the same cadence as your AI systems.