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AI Security

Intent-Aware AI Security for the Enterprise

Only Proofpoint unifies AI runtime security with industry-leading data security to understand AI intent, govern every interaction, and build trust in enterprise AI.

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Overview and Benefits Why It Matters Secure Your Enterprise Request a Demo FAQ
Overview and Benefits

Secure AI adoption with intent-aware security and data context

A user or agent may be authorized to access data, but permission is not the same as intent, and access is not the same as trust. Without understanding what AI is trying to do, organizations can't know whether they're facing a security incident or a business risk: fraud, compliance exposure, or reputational harm. Building trust in enterprise AI requires more than access controls. It requires understanding intent.

Understand AI intent

Detect what AI is actually trying to do, not just what it's allowed to do. Proofpoint reasons over intent at runtime so you can stop risk while it's happening, not after. Because you can only trust AI you can understand in the moment.

Secure AI and data as one

Rely on the only AI security solution where data context informs AI intent detection, and AI behavior feeds back into data security decisions. No other platform gives you trust in what AI is doing with your most sensitive data.

Deploy AI openly

Activate Proofpoint AI Security on your existing endpoint footprint—no rip-and-replace, no portfolio lock-in. Govern AI intent and build trust across your environment without starting over. Stronger together, open by design.

Why It Matters

Building trust in AI requires clearer security context

AI tools and agents can access data, make decisions, and act across enterprise systems in real time. But authorized access is not the same as trustworthy intent. Without runtime visibility into AI intent, security teams can't distinguish appropriate behavior from risk—and they can't stop fraud, compliance violations, or reputational harm before it's too late. Legacy tools were built for human activity. They were never designed to understand AI intent. And without intent, there is no trust.

70%
of organizations lack optimized AI governance 
50%
of organizations expect data loss via AI tools in the next year 
49%
of organizations expect shadow AI incidents in the next year
Secure Your Enterprise
Secure your enterprise at every layer 

Secure AI usage by people

Users often adopt AI tools without security's knowledge. Legacy tools can block access to unsanctioned AI services, but they can't examine prompts, moderate outputs, or understand what AI is doing with enterprise data.

Proofpoint AI Access Security discovers every AI tool active in your environment. It inspects interactions at runtime, enforces context-aware policies, and produces audit-ready evidence of every employee interaction with AI.

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Secure AI usage by agents

Autonomous agents reason, plan, and act independently across enterprise systems on behalf of users via API connections, MCP, or custom integrations. Traditional access controls can confirm whether an agent has permission to act, but they can’t validate whether an agent's actions match its assigned task.

Proofpoint Agentic AI Security governs agent behavior with intent-based detection, runtime observability across multi-step workflows, and behavioral anomaly detection. It reconstructs every agent-based transaction in full—from user request through agent action to final outcome. 

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Secure MCP servers

Model Context Protocol (MCP) is becoming the standard interface for connecting AI to enterprise tools and data. However, it was designed for developer convenience, not enterprise governance. Developers can deploy MCP servers without security review, and agents can gain unexpected cross-system access.

Proofpoint AI MCP Security enforces authentication and content inspection at the MCP boundary. It maintains a registry of approved servers and checks the security posture of every service in the AI supply chain. 

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FAQ

FAQ

  • How can security teams discover shadow AI apps, agents, and MCP servers?

    Security teams can discover shadow AI by monitoring AI traffic, API activity, identity signals, and AI tool usage across the environment.

    Security teams can discover shadow AI by monitoring AI traffic, API activity, identity signals, and AI tool usage across the environment. AI security platforms help organizations find unsanctioned generative AI apps, AI agents, MCP servers, and connected services before they create unmanaged risk.

    Key capabilities include:

    • Discovering AI apps, agents, and MCP servers
    • Mapping AI integrations and connected tools
    • Monitoring AI usage across the AI lifecycle
    • Identifying high-risk AI activity
    • Applying policies to new AI tools

  • How can organizations govern AI usage and enforce AI security controls at runtime?

    Organizations can govern AI usage by applying AI-driven security controls at runtime across prompts, responses, agent actions, and connected tools.

    Organizations can govern AI usage by applying AI-driven security controls at runtime across prompts, responses, agent actions, and connected tools. Runtime visibility helps security teams determine whether AI activity aligns with business policies, user intent, and data protection requirements.

    Important capabilities include:

    • Inspecting prompts and outputs in real time
    • Automating responses to high-risk activity
    • Enforcing context-aware security policies
    • Continuously monitoring AI interactions
    • Creating audit trails for AI activity

  • How can enterprises reduce sensitive data exposure in generative AI tools and AI agents?

    Enterprises can reduce sensitive data exposure by inspecting prompts, outputs, and agent workflows in real time.

    Enterprises can reduce sensitive data exposure by inspecting prompts, outputs, and agent workflows in real time. AI security platforms help security teams identify regulated data, intellectual property, credentials, and other sensitive content before it is exposed through generative AI tools or AI-driven workflows.

    Key controls include:

    • Scanning prompts and responses
    • Classifying sensitive data
    • Redacting or blocking risky activity
    • Protecting AI agents and connected tools
    • Monitoring risky data sharing behavior

  • How is AI agent security different from traditional cybersecurity controls?

    Traditional cybersecurity controls focus on user access and permissions. AI agents can reason, make decisions, and act across multiple systems with little or no human involvement.

     

    Traditional cybersecurity controls focus on user access and permissions. AI agents can reason, make decisions, and act across multiple systems with little or no human involvement.

    AI agent security adds runtime visibility, behavioral monitoring, and intent-aware governance. These capabilities help organizations determine whether AI actions align with approved tasks and business policies.

    Core capabilities include: 

    • Monitoring multi-step AI workflows
    • Tracking tool usage and connected systems
    • Detecting unusual AI behavior
    • Governing agent actions in real time
    • Producing audit-ready records for investigations and compliance