Governed capacity is AI infrastructure that is monitored, policy-managed, and operated like a production service. It includes the serving layer for inference, where models are loaded, requests are routed, and usage is measured across pools or tenants. In practice, governed capacity means teams can see utilization, latency, throughput, cost, and health telemetry, then apply rules for scaling, isolation, and access.
This matters in cyber security because an unmanaged AI service can become a target for abuse even without a classic breach. Attackers or insiders may spam inference endpoints, drive up token or compute costs, exhaust shared capacity, or use weak routing and permissions to reach models they should not access. Defenders use governance to enforce authentication, quotas, workload isolation, audit logs, and policy-based routing. When capacity is governed, organizations can keep AI services reliable, accountable, and harder to misuse.