Governance costs are the ongoing effort needed to review, validate, monitor, and oversee an AI system across its lifecycle. In practice, this includes policy checks, model evaluation, access reviews, logging, incident response, data lineage tracking, and periodic revalidation as models, data, and usage change.
In cyber security, these costs matter because control does not end at deployment. A model that is not monitored can drift, leak sensitive data, produce unsafe outputs, or be abused through prompt injection and other attacks. Defenders must budget for the people and tools that keep AI systems trustworthy: approval workflows, red-teaming, guardrails, content filtering, and audit-ready reporting. Attackers often benefit when organizations treat governance as optional overhead, because weak oversight creates blind spots. Good governance costs are not wasted spend; they are the price of operating AI safely at scale.