The unit of AI security is the interaction
Security disciplines form around units of work. Files got scanning, networks got firewalls, endpoints got detection and response. AI introduces a new unit, the interaction, and it changes where security has to stand.
An enterprise adopting AI is not adding a new kind of file or a new kind of traffic. It is adding a new kind of event: a person or a piece of software assembles context, sends it to a model, and acts on what comes back. That event is the interaction. It is where sensitive data meets a model and where intent becomes action. Securing AI means securing that event.
Three shapes, one event
Interactions come in three shapes: human to model, agent to tool, agent to agent. The first is familiar. An employee prompts an assistant, pastes a contract, asks for a draft. The second appears the moment a model can act: an agent queries a CRM, writes to a repository, sends a message on someone's behalf. The third is the newest, one agent delegating work to another, with context moving between them and no person in the loop.
| Interaction | What moves |
|---|---|
| HUMAN TO MODEL | A person prompts a model: chat, copilots, uploads, pasted context |
| AGENT TO TOOL | A model acts: API calls, queries, file writes, messages sent on its behalf |
| AGENT TO AGENT | Delegation between agents: context and instructions passing machine to machine |
All three carry the same anatomy: an identity acting, data in motion, a destination, and an intent that can be judged. Security that reads that anatomy governs all three shapes with one policy. Security that cannot read it treats each new agent framework as a fresh perimeter problem.
Why the perimeter does not see it
Endpoint and network controls answer questions about containers. Which file moved, which host connected, which bytes crossed the wire. An interaction is not a container. Its sensitive content is assembled at the moment of use, a prompt plus pasted records plus retrieved context, and its risk lives in meaning: what is being asked, by whom, with what data, of which model.
Two properties make the difference concrete. First, an interaction can be perfectly well-formed and still be a violation; nothing about the bytes is malformed when a salary table goes to a personal AI site. Second, an interaction can originate from software. An agent holds credentials, calls tools, and never opens a login page, so controls built around human sessions have no subject to attach it to.
Security as a runtime
If the unit of work is a live event, the control has to run while the event runs. That is the argument for a runtime: a security layer standing on the path of every interaction, executing four functions inline. It observes who is acting, with what data, toward which destination. It detects what matters, judged by meaning and intent, not pattern matching alone. It enforces a verdict, allow, redact, hold, or block, before the prompt reaches the model's API. And it traces the decision as audit-ready evidence.
Position is the whole point. A verdict rendered after the model has answered is a report. The same verdict rendered before the model sees the prompt is a control. A single control point watches traffic; the runtime governs the whole path.
What follows
Once interactions are the unit, familiar problems get simpler statements. Shadow AI is unobserved interactions. Data leakage is an interaction that should have been redacted or blocked. Agent risk is an interaction chain with no human in it. Audit readiness is the record of every interaction's verdict. One layer, applied at the right unit, covers what otherwise takes several disconnected controls.
That is the bet First Recon AI is built on. The runtime understands, secures, and proves every AI interaction, from device to model. The unit of AI security is the interaction; the rest follows from taking that seriously.