Most organizations are deploying AI faster than they’re building security controls for it. The result is a growing gap between what AI can do in your environment and what your security team can actually see or defend.
AI security governance is about closing that gap — establishing a structured set of policies, controls, and oversight mechanisms before an incident forces the conversation.
**AI Asset Inventory**
You can’t secure what you don’t know exists. Build a comprehensive catalog of every AI model, AI-enabled tool, and AI-integrated system in your environment. This includes vendor-hosted models your users access through SaaS applications, internal models served via APIs, and anything connected to your data pipelines. Treat this inventory with the same rigor you’d apply to your critical asset list.
**Model Risk Tiering**
Not all AI systems carry the same risk. A model that summarizes internal documents sits in a different risk category than one that makes access control decisions or processes customer PII. Tier your models by consequence: what happens if this model is compromised, poisoned, or leaks data? Use that consequence level to drive controls — higher tier means stricter access controls, more logging, and more frequent evaluation.
**Input and Output Monitoring**
AI systems are an attack surface through their inputs and outputs. Monitor for adversarial inputs — prompt injection attempts, malformed requests designed to bypass safeguards, or data that signals reconnaissance against your AI infrastructure. Log AI outputs with enough context to support forensic investigation if something goes wrong. This is also where you catch model behavior drift that might indicate tampering.
**Incident Response for AI-Specific Breaches**
Your existing IR playbook probably doesn’t cover what happens when a threat actor manipulates a model’s behavior, steals training data, or uses your AI system as an attack vector against other targets. Build AI-specific scenarios into your tabletop exercises. Define escalation paths, containment steps, and communication protocols for AI incidents before they happen.
MITRE ATLAS — the Adversarial Threat Landscape for Artificial-Intelligence Systems — documents the specific techniques adversaries use against AI systems. Once you have your AI inventory and risk tiers, you can map your existing controls against ATLAS techniques most relevant to your environment. Gaps in coverage become your priority remediation list.
You don’t need to build all four pillars at once. Start with inventory and tiering — those two steps alone give your team enough visibility to have an honest conversation about AI risk. From there, add monitoring where the consequence of an incident is highest, and build the IR playbook as a distinct workstream.
The organizations that will be in the best position two years from now are the ones that started building governance structures today. Not perfect structures — just functional ones, with enough foundation to grow as the threat landscape evolves.
