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The Third-Party Agent Problem: Why Security Built for AI You Chose Misses the Agents You Didn't

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The Third-Party Agent Problem: Why Security Built for AI You Chose Misses the Agents You Didn't

In environments studied for the 2026 State of Agent Security Report, roughly 1,280 third-party products now embed AI. About 282 of them sit behind single sign-on. The other thousand are invisible to identity infrastructure by default, not because anyone hid them, but because an identity stack can only govern what authenticates through it, and most agents never do.

That gap is the clearest expression of a shift the security industry is only starting to name. For several years, "AI security" solved a first-party problem: the company decided to use AI, procured licenses, deployed a model behind a gateway, and security pointed controls at the thing the business had chosen. Agents do not arrive that way. They arrive inside software the enterprise already runs, and they arrive without a decision.

Every control in the first-party toolkit assumes a moment exists: model scanning assumes a model was selected, prompt inspection assumes a gateway was deployed, an acceptable-use policy assumes there was an adoption to accept. That moment gave security a review, a surface to instrument, and an owner to name.

Agents skip the moment. Salesforce's Slack Code, launched in August 2026, lets a user tag a coding agent into any conversation; the agent reads the shared context, writes the code, and opens the pull request. " Read by a security team, that sentence describes an autonomous actor with reach into GitHub and production infrastructure whose governance is a chat tool's channel membership. There was nothing to instrument, because nothing was adopted.



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