Get in touchDeep session inspection of human and AI work.
Every session on the network, judged by intent, tied to an owner.
Every session on the network, judged by intent, tied to an owner.
The AI security challenge is a network security challenge. Bind gives you one place to see and control what humans and AI agents do.
Six to start with. One control point, whether a human or an AI agent is doing the work.
Customer records, source code, and secrets stop before they reach a model, whether a human pasted them or an agent fetched them.
When an agent is about to change something that matters, the owner approves inside the tool before it happens.
Agents reach only the tools, data, and destinations you approve. The work continues. The detour does not.
Content an agent reads can try to redirect it. Bind sees the attempt in the session, and the agent never acts on it.
Every action an agent takes traces back to the human who started the work, across every tool it touched.
What happened, who did it, what the policy said, and what Bind did. One record for humans and AI agents.
Decisions show up where the work happens: the terminal, the app, or the agent's browser.
One policy, one enforcement point, and one record for humans and AI agents.
Bind is network security for the AI era: one policy for humans and AI agents, enforced in the session, with every agent action tied to the owner.
Your SASE decides by user and destination. Bind decides by what the actor is trying to do in session, and runs beside your SASE rather than replacing it.
Every person and every agent, on any device or in any cloud, crosses the network to reach a model, a tool, or your data. Securing AI at the network covers agents that run in the cloud with no device to install on, sees both the request and the response across every hop, and works across apps without integrating each one.
Identity travels with every session from the first request, including which human started the work. That link is kept across every hop, so each action in the session record traces back to the owner.
The reason, inside the tool they are working in. If the rule allows it, they continue with approval. If not, the block and the policy that fired are on the record.
Deterministic rules where you need certainty. Model-based risk scoring where you need coverage. Together they let you write rules in plain language without giving up accuracy.
Bind can allow, detect, block, modify, redirect, challenge, and mask. Actions apply to both the request and the response.
What happened, who did it, what the policy said, and what Bind did, for humans and AI agents alike.
No. One policy and one record cover all of the tools used for work. New apps get coverage without dedicated integrations.
LLMs and AI gateways, SaaS applications, public APIs, MCP servers, and internal apps. Coverage follows the work to managed and unmanaged devices, embedded agent browsers, SaaS with AI inside, and agents running in the cloud.
Bind is designed to run alongside your current stack. You can point one app or one group at Bind and leave everything else where it is.
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