Earlier this month, I spoke at the Gartner Security & Risk Management Summit about a blind spot most security programs are still not accounting for - how attackers are circumventing AI security programs by using legacy infrastructure to hijack AI agents.
AI adoption is moving faster than security programs can account for. Roughly 71% of organizations are piloting AI agents across their enterprise applications, and 31% have already moved them into production workflows.
For this reason, organizations are legitimately pouring resources into securing AI workloads against model poisoning, prompt injection, data leakage, and other emerging threats. Yet this focus misses everything underneath the AI layer. Because an unpatched server, a misconfigured Active Directory permission, or a cached credential on a developer's machine are exposures that give attackers a direct route to everything your AI agents depend on - knowledge bases, cloud storage, Lambda functions, SaaS integrations, and the credentials that connect them.
This means that threat actors don’t really need to attack your AI head-on - they just need to reach what it connects to. In this article, I'll walk through how legacy infrastructure becomes the attack path into AI agent environments and what security teams can do to block those paths.
