Why is AI security suddenly a budget line?
Because the attack surface arrived with the deployments. Gartner forecasts the market for securing AI will reach 4.8 billion dollars in 2027, a spend category that barely existed three years earlier. Every system wired to company data through a language model inherits a new class of attack, and the first one most companies meet is prompt injection: instructions hidden in the content a model reads that redirect what it does.
How is securing AI different from normal application security?
The failure modes are statistical, not binary. A firewall rule either holds or it does not; a model can be talked into something. That is why the discipline leans on AI red teaming, adversarial testing of the model’s behavior rather than the network around it, and on runtime guardrails instead of one pre-launch penetration test. You are defending behavior, so the controls have to watch behavior, continuously, not annually.
What does AI security look like for a mid-sized company?
Not a platform purchase first. The highest-return controls are organizational: an inventory that surfaces shadow AI before it surfaces itself in an incident, least-privilege access for every agent, contracts that say what a vendor may do with your data, and human approval on consequential actions. Most AI incidents at this scale are permission problems, not exotic attacks, which is why frameworks such as AI TRiSM put access and governance ahead of model-level defenses. Scope what each system can reach before you buy anything that inspects what it does.
Who owns AI security: the CISO or the AI team?
Both, under one accountability line. The CISO owns the threat model and the controls; whoever runs AI governance owns the inventory, the policy, and the approval paths. Splitting the two is how systems end up secured on paper and over-permissioned in production.