Opsin gives security teams visibility, context, and control over the enterprise AI already running in production: Microsoft Copilot, ChatGPT Enterprise, Claude Enterprise, and Google Gemini, along with the agents employees are building on top of them. We connect identity, data, and model behavior into a single contextual layer, so security teams can see what an agent was built to do, what it can actually reach, and where the two have drifted apart. Deployment is API-based and takes minutes, and our proactive risk assessment simulates real user queries to surface exposure within 24 hours.

Most agentic AI security conversations at Black Hat will be about prompt injection and runtime attacks. Worth asking us about the other side of the risk: the agents your business users are standing up on Copilot Studio, ChatGPT Enterprise, and Agentforce with no SDLC, no AppSec review, and no clear owner. That's the category with the most direct line to your identities, permissions, and data, and it's where we spend all our time.

It's also the reason we built Agent Intent, which just went live. Most tools can tell you what an agent is allowed to do. Very few can tell you whether that agent's configuration actually matches what it was built for, before it ever runs a task. We classify every agent in your inventory against five dimensions (who it was built to serve, what it should touch, what actions are proportionate, what outcome it's working toward, and what constraints it declared for itself) using nothing but what was true at the moment it was provisioned. A public-facing HR bot with a delete-resources tool attached is a finding the day it's built, not the day something goes wrong. If you've got an agent population you can't currently answer that question for, that's worth twenty minutes at the booth.

Bring your Copilot or ChatGPT Enterprise rollout plans and we'll talk specifics, not slideware.

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Agent Intent evaluates an agent's declared purpose across five dimensions (audience, topic, action, goal, and guardrail) at the moment it's provisioned, using its name, system prompt, connected tools, and provisioner identity as evidence. That matters because most misconfigured agents behave normally most of the time. A public-facing HR bot with a delete-resources tool attached is a problem the day it's built, not the day it acts on it, and runtime monitoring alone won't catch it until it's already in motion. If you want to see how your own environment scores, ask us for a live walkthrough in a 1:1 Meeting at Black Hat.

Most vendors in this space secure one of three very different problems: engineering-built AI (prompt injection, runtime firewalls), IT-managed AI (endpoint and device controls), or business-built AI, the agents non-developers stand up on enterprise AI platforms with no security review in the loop. That third category is where the data says the real exposure lives, and it's the one Opsin was built for. We'd rather show you where we fit and where we don't than compete on which vendor uses the word "governance" more convincingly.