Agentic AI security

Agentic AI security

WAI Docs Wed Aug 19 13:22:37 EDT 2026
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Quick Start
Welcome
Supported Applications & LLMs
Release Notes
August 18, 2026 WitnessAI Release
August 4, 2026 WitnessAI Release
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November 18, 2025 WitnessAI Update
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October 28, 2025 WitnessAI Update
October 23, 2025 WitnessAI Update
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October 2, 2025 WitnessAI Update
September 30, 2025: WitnessAI Update
September 23, 2025: WitnessAI Update
August 12, 2025: WitnessAI Update
July 31, 2025: WitnessAI Update
July 18, 2025: WitnessAI Update
April 11, 2025: WitnessAI Release v2.0
June 9, 2025: WitnessAI Update
June 23, 2025: WitnessAI Update
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User Guide
Policies - GuardRails
Witness Anywhere: Remote Device Security
Witness Attack
Administrator Guide
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Agentic AI security

AI agents don't just answer questions — they take actions: calling tools, querying data, and connecting to services through protocols such as MCP (Model Context Protocol). This page explains how WitnessAI helps you discover the agents in your environment, attribute their activity to a real identity, and control what they're permitted to do.

The problem

Autonomous and semi-autonomous agents introduce risks that prompt-only controls don't cover:
  • Invisible actors. Agents and the MCP servers they connect to can proliferate without anyone tracking them.
  • No identity attribution. When an agent acts, it's hard to tie that action back to the human or service it acts on behalf of.
  • Unbounded tool access. An agent with broad tool or MCP access can reach data and systems far beyond what its task requires.

How WitnessAI solves the problem

WitnessAI extends the same observe-control-protect model to agentic AI. It discovers the agents and MCP servers operating in your environment, brings their activity into the same classified, auditable record as the rest of your AI traffic, and lets you govern which tools and MCP servers an agent can use through policy — so agent actions stay scoped to what you intend.

What capabilities WitnessAI provides

  • Discover agents and MCP servers. Agents — part of Discovery — surfaces the AI agents and the MCP servers they connect to, so you can see what's operating and assess its risk.
  • Govern tool and MCP access. Policies can control agent tool use and MCP access, restricting an agent to an approved set of capabilities. See Policy types for the policy controls available and Policy creation for how to build them.
  • Apply behavioral guardrails. The Behavioral Activity GuardRail lets you define and enforce the behaviors an agent is — and isn't — allowed to perform, including rules generated from a stated purpose.
  • Audit agent activity. Agent interactions flow into Conversations and Alerts alongside the rest of your AI traffic, giving you one record to investigate and one place to catch violations.

How to get started

  1. Find your agents. Open Agents under Discovery to see the agents and MCP servers in your environment.
  2. Decide what's allowed. Review Policy types to understand how to scope agent tool and MCP access.
  3. Build the guardrail. Use the Behavioral Activity GuardRail to define permitted behaviors, then attach it to a policy via Policy creation.
  4. Monitor. Track agent activity in Conversations and Alerts, and refine as your agent footprint grows.