Half one: security operated by an AI agent
Through the Senserva MCP, Claude or the AI of your choice becomes a security operator with guardrails. You ask in plain language; the agent works from your real scan data and Senserva validates everything it proposes.
Half two: securing the agents in your tenant
Copilot, custom agents, and the service principals behind them are identities with permissions, and most tenants cannot list them. Treat them like the privileged accounts they are:
Why one model has to cover both
The agent that runs your security and the agents you need to secure are the same kind of thing: identities that act. If your security model can validate, gate, and verify an action from Claude, it can inventory and gate the actions of every other agent in the tenant. That is why Senserva treats agentic AI security as one connected problem, on the same data model as your users, apps, and devices.
Frequently asked
It covers two things that are converging: using agentic AI (AI that plans and acts, not just answers) to operate your security, and securing the agentic AI that is appearing inside your environment: Copilot, custom agents, and the service principals behind them. A real strategy handles both.
Ground it and gate it. Through the Senserva MCP, Claude or the AI of your choice works only from your real scan data, every proposed change is validated by Senserva first, and nothing applies without your approval. The agent does the work; you keep control.
Scan for them like any other identity and permission surface. Senserva inventories agents, their service principals, and the permissions behind them, and flags over-scoped or unapproved ones, the same way it audits users and apps.
Yes. Copilot is the most widely deployed agentic surface in Microsoft 365, and its risk is mostly data governance: what it can reach is what it can leak. Senserva audits the oversharing and labeling gaps that Copilot inherits.