Three cybersecurity professionals collaborating around a laptop in a modern office, analyzing data on screen—framed by a green curved graphic on a black background consistent with Proofpoint branding.
Three cybersecurity professionals collaborating around a laptop in a modern office, analyzing data on screen—framed by a green curved graphic on a black background consistent with Proofpoint branding.
Agentic AI Security

Secure AI Usage by Agents

Advance your enterprise AI adoption strategy with confidence. 

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Overview and Benefits Why It Matters Product Details Related Resources Request a Demo FAQ
Overview and Benefits

Confidently secure agent deployments across your enterprise

Roll out and use autonomous agents with complete agent integrity and full accountability.

Agent actions aligned to intent

Every agent action is evaluated against the user's intent, catching scope violations even when permissions are valid.

Complete agent workflow visibility

Transactions are fully reconstructed—from user request through agent reasoning and tool invocation—all linked to the originating user.

Governance that scales with adoption

Observability and policy enforcement scale across agent deployments from a single platform.

Why It Matters

Autonomous agents act on behalf of users, with or without your security team's knowledge 

Autonomous agents can act across channels such as email, cloud storage, code repositories, and databases—all via API, Model Context Protocol (MCP), or custom integrations. Agents deployed without security’s knowledge (shadow AI) carry persistent access to sensitive data across every connected app and downstream integration. Even when agents are known and sanctioned, traditional security can’t verify that each action aligns with the task the agent was asked to perform.

This is why agent integrity is essential. Without a way to ensure alignment between what agents can do, should do, and actually do, organizations face growing risks. These include: 

  • Long‑lived, unsupervised access paths created by shadow AI agents 
  • Actions that exceed user intent despite passing permission checks 
  • Opaque decision chains that limit oversight and trust 
  • Broader exposure driven by every tool, API, and MCP server the agent uses 
Security analysts working in a modern office environment, reviewing dashboards and data on multiple monitors—representing Proofpoint teams monitoring threats and protecting users.
Product Details

Scale autonomous agents you can audit, trust, and control

Two security engineers collaborating at a workstation, reviewing code and system activity on multiple monitors—reflecting Proofpoint teams deploying and managing secure enterprise solutions.

Confident agent deployments across the enterprise

Scale autonomous AI across business functions and geographies with full confidence that agent behavior is aligned, monitored, and governed. Make agentic AI adoption a strategic investment that security teams support—not another risk to contain.

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Agent integrity across every deployment

Ensure that an AI agent operates within its intended purpose, authorized permissions, and expected behavior. This applies across every interaction, tool call, and data access. Agent integrity validates what an agent can do, should do, and actually does—and whether those three dimensions align. That assurance scales with every new agent the organization deploys.

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Full accountability across autonomous AI

Know exactly what every agent does, on whose behalf, and whether those actions were appropriate to the task. This accountability gives security and leadership unprecedented confidence in autonomous AI. It also enables organizations to meet regulatory requirements around AI oversight as they emerge.

Agent and MCP Discovery

Discover autonomous agents—both custom and managed—their toolchains, MCP server connections, and external services. Trace execution from agent to tool to MCP server, extending inventory to the infrastructure agents use.

Runtime Observability

Capture behavior‑level telemetry across multi‑step workflows with correlated visibility across agent, tool, and MCP paths. Track how execution context evolves across handoffs, including multi‑agent systems. 

Intent-Based Access Control (IBAC)

Track what a user asks an agent to do and assess subsequent actions against that intent. Detect when agent actions exceed task scope, even when all permission checks pass.

MCP Governance

Enforce authentication and content inspection at the MCP boundary for all tool connections. Control data crossing MCP links and permitted actions. Help security teams govern the protocol layer, where agents access data and apps.

AI Supply Chain Visibility and Risk Assessment

Discover and maintain a registry of external tools, third‑party services, APIs, and MCP servers that agents use. Evaluate the security posture of every dependency node so the AI supply chain stays visible and governed.

Behavioral Anomaly Detection

Build a baseline of agent behavior and flag deviations—such as scope expansion, drift, and unusual access—that static policy or intent alignment might miss. Identify activity outside established norms.

Forensics and Defensible Audit

Reconstruct chains from user requests through agent reasoning, tool invocations, and outcomes. Link each step to the originating user with security context. Build complete, defensible audit trails for governance, compliance, and incident response.

 

 

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FAQ

FAQ

  • Why do enterprises need agentic AI security for autonomous agents?

    Enterprises need agentic AI security because autonomous agents can take independent actions across systems, and traditional security cannot verify whether those actions align with the agent’s intended purpose. These agents operate across email, cloud storage, CRMs, developer tools, and internal databases, often via direct APIs or MCP connections. This creates persistent access paths that security teams may not know exist.  Key reasons enterprises need agentic AI security include:

    Enterprises need agentic AI security because autonomous agents can take independent actions across systems, and traditional security cannot verify whether those actions align with the agent’s intended purpose. These agents operate across email, cloud storage, CRMs, developer tools, and internal databases, often via direct APIs or MCP connections. This creates persistent access paths that security teams may not know exist. 

    Key reasons enterprises need agentic AI security include:

    • Shadow AI deployments: Agents can be created or connected without security oversight, inheriting broad, long‑lived permissions. 
    • Unverifiable agent behavior: Even sanctioned agents can make decisions unrelated to their assigned tasks, bypassing intent boundaries. 
    • Expanded attack surface: Every tool, API, and MCP server connected to an agent becomes part of the enterprise’s AI supply chain. 
    • Regulatory and oversight requirements: Organizations must demonstrate controlled, auditable AI behavior as governance standards evolve. 

  • What risks do autonomous AI agents introduce across enterprise systems? 

    Autonomous AI agents introduce risks because they act independently across applications and can take actions that exceed user intent, authorized scope, or expected workflows. Because agentic AI models reason and operate through multi‑step plans, their decision paths can be difficult to monitor or constrain in real time. Primary risks include: 

    Autonomous AI agents introduce risks because they act independently across applications and can take actions that exceed user intent, authorized scope, or expected workflows. Because agentic AI models reason and operate through multi‑step plans, their decision paths can be difficult to monitor or constrain in real time.

    Primary risks include: 

    • Scope "creep": Agents might perform actions unrelated to the task they were asked to complete, even while passing all permission checks. 
    • Unmonitored access paths: Agents often connect to tools, APIs, and MCP servers that create unseen data flows and persistent privileges. 
    • AI supply chain vulnerabilities: External services and third‑party integrations can introduce new attack surfaces and dependency risks. 
    • Behavioral drift: Over time, agents can deviate from established behavioral norms in ways manual policy controls can’t detect. 
    • Lack of forensic visibility: Without specialized telemetry, organizations can’t reconstruct how an agent reached a decision or took an action.

  • What is agent integrity and why is it critical for AI governance? 

    Agent integrity is the assurance that an AI agent’s permissions, intended purpose, and actual behavior remain aligned across every tool call, interaction, and data access. It validates that an agent is doing what it should do, only what it is allowed to do, and exactly what the user requested.  Agent integrity is critical for AI governance because it: 

    Enterprises need agentic AI security because autonomous agents can take independent actions across systems, and traditional security cannot verify whether those actions align with the agent’s intended purpose. These agents operate across email, cloud storage, CRMs, developer tools, and internal databases, often via direct APIs or MCP connections. This creates persistent access paths that security teams may not know exist. 

    Key reasons enterprises need agentic AI security include:

    • Enforces intent alignment: Every agent action can be evaluated against the originating user request to prevent overreach. 
    • Establishes behavioral accountability: Security teams can verify whether agents acted within scope during multi‑step reasoning. 
    • Scales trust across deployments: Consistent integrity checks allow enterprises to adopt more agents without multiplying risk. 
    • Supports regulatory compliance: Governance frameworks require traceability, explainability, and auditability of autonomous systems.
    • Closes gaps traditional security cannot: Permissions alone can't guarantee that an agent’s chosen actions match the intended task.

  • How can organizations audit AI agent actions for compliance and forensics? 

    Organizations can audit AI agent actions by capturing end‑to‑end telemetry that reconstructs the full chain from user request to agent reasoning, tool invocation, and final outcome. Effective auditing requires visibility across agent workflows, tool paths, and MCP connections.  Core components of agent auditability include: 

    Organizations can audit AI agent actions by capturing end‑to‑end telemetry that reconstructs the full chain from user request to agent reasoning, tool invocation, and final outcome. Effective auditing requires visibility across agent workflows, tool paths, and MCP connections. 

    Core components of agent auditability include: 

    • Complete transaction reconstruction: Link every step of the agent’s reasoning and actions back to the originating user. 
    • Behavior‑level telemetry: Record decision branches, tool calls, and data accesses across multi‑step and multi‑agent workflows. 
    • Intent‑action comparison: Validate whether each action was appropriate for the task the user originally provided. 
    • Protocol‑level inspection: Monitor and govern data crossing MCP connections and external APIs. 
    • Defensible audit trails: Produce detailed records suitable for compliance reviews, incident response, and regulatory reporting.