uk:
English: Europe, Middle East, Africa
Agentic AI Security
Secure AI Usage by Agents
Advance your enterprise AI adoption strategy with confidence.
Confidently secure agent deployments across your enterprise
Roll out and use autonomous agents with complete agent integrity and full accountability.
Every agent action is evaluated against the user's intent, catching scope violations even when permissions are valid.
Transactions are fully reconstructed, from user request through agent reasoning and tool invocation, and they are all linked to the originating user.
Observability and policy enforcement scale across agent deployments from a single platform.
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 cannot verify that each action aligns with the task the agent was asked to perform.
That’s why agent integrity is essential. Without a means to ensure alignment between what agents can do, should do and actually do, organisations 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
Scale autonomous agents you can audit, trust, and control
Confident agent deployments across the enterprise
Scale autonomous AI across business functions and geographies with full confidence that agent behaviour is aligned, monitored, and governed. Make agentic AI adoption a strategic investment that security teams support—not another risk to contain.
Agent integrity across every deployment
Ensure that an AI agent operates within its intended purpose, authorised permissions, and expected behaviour. 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 organisation deploys.
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 organisations 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 behaviour‑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.
Behavioural Anomaly Detection
Build a baseline of agent behaviour 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.
Explore Related Resources
Solving the Unsolvable
Inside Proofpoint’s Prompt Injection and Jailbreak Detection Models
FAQ
-
Why do enterprises require 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 be aware of.
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 behaviour: 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: organisations must demonstrate controlled, auditable AI behaviour as governance standards evolve.
-
Which 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, authorised scope or expected workflows. Because agentic AI models reason and operate through multistep plans, their decision paths can be hard to monitor or constrain in real time.Autonomous AI agents introduce risks because they act independently across applications and can take actions that exceed user intent, authorised scope or expected workflows. Because agentic AI models reason and operate through multistep plans, their decision paths can be hard 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.
- Behavioural drift: over time, agents can deviate from established behavioural norms in ways manual policy controls cannot detect.
- Lack of forensic visibility: without specialised telemetry, organisations cannot 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 behaviour 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 be aware of.
Key reasons why enterprises need agentic AI security include:
- Enforces intent alignment: every agent action can be evaluated against the originating user request to prevent overreach.
- Establishes behavioural accountability: security teams can verify whether agents acted within scope during multistep 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 that traditional security cannot: permissions alone cannot guarantee that an agent’s chosen actions match the intended task.
-
How can organisations audit AI agent actions for compliance and forensics?
Organisations 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:Organisations 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.
- Behaviour-level telemetry: record decision branches, tool calls and data accesses across multistep 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.
