AI Security Platform
Intent-aware controls that govern how people and AI agents access, use, and act on enterprise data.
Platform Overview
AI security helps organizations govern how users and agents use AI across apps, models, tools, and enterprise data. Security teams can see AI interactions as they happen, enforce policy in real time, and keep forensic evidence for investigations. Pairing AI intent with data context lets teams distinguish productive use from unsafe behavior to proactively reduce risk.
Evaluate what AI is trying to do, not just whether access is allowed.
Apply data-aware controls to prompts, responses, tool calls, and workflows.
Extend consistent controls across workforce AI use, agents, MCP servers, and AI gateways.
AI Access Security
Users often adopt AI tools without security's knowledge. Legacy tools can block access to unsanctioned AI, 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.
Agentic AI Security
Autonomous agents reason, plan, and act independently across 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 not 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 outcome.
AI MCP Security
Model Context Protocol (MCP) is becoming the standard 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.
Why Proofpoint
| Capability | Siloed AI Controls | Proofpoint AI Security |
|---|---|---|
| Scope | Often protect one control point, such as endpoint, network, browser, model, or app traffic | Secures people, agents, MCP connections, AI gateways, and data in one suite |
| Intent context | May score individual events without understanding the full AI workflow | Connects AI behavior to the user request, data sensitivity, policy, and workflow context |
| Data security | May require separate DLP, identity, network, and app controls to approximate coverage | Uses data context to guide AI policy and enforcement decisions |
| Auditability | Evidence can be fragmented across tools and teams | Builds evidence across prompts, responses, tool calls, agent steps, and policy outcomes |
See why AI security needs runtime intent, data context, and governance across people and agents.
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Enterprises can manage shadow AI by continuously discovering AI apps, browser-based tools, agents, API connections, and MCP servers that employees or systems use without formal approval. Security teams should assess each tool’s risk, identify what data is being shared, and apply policies based on user, data, intent, and destination.
Proofpoint helps organizations uncover unsanctioned AI use, onboard approved tools, monitor risky activity, and enforce controls that reduce data exposure across human and agent workflows.
Enterprises can reduce sensitive data exposure by inspecting prompts, responses, file uploads, agent actions, and connected tool activity for regulated data, intellectual property, credentials, and other sensitive content. Policies should prevent risky sharing, redact sensitive data when appropriate, and create audit-ready records of AI activity.
Proofpoint brings together AI security and data security context so organizations can understand what data is at risk, who is using it, where it is going, and whether the activity should be allowed, coached, blocked, or investigated.
Organizations should govern AI usage with policies that work while activity is happening, not only after data has already been shared. Runtime controls can inspect prompts, outputs, files, user behavior, agent intent, and connected tools before allowing, coaching, redacting, or blocking risky actions.
With Proofpoint, security teams can apply context-aware AI policies, maintain audit trails, support compliance requirements, and give users safer ways to adopt AI across the business.
Traditional access controls determine whether a user or system has permission to access an application, file, or service. AI agent security goes further by evaluating what an agent is trying to do, whether its behavior matches the user’s intent, what tools it can call, and whether its actions could expose sensitive data or create business risk.
Because agents can reason, plan, and act across systems, enterprises need controls that monitor agent behavior continuously and enforce policy throughout the workflow.
Enterprises should secure MCP servers and AI tool connections by maintaining visibility into approved and unapproved servers, validating authentication, and authorization, inspecting data exchanged through tool calls, and enforcing policies based on user, agent, data, action, and destination. They should also monitor third-party connections and keep audit records of AI-to-tool activity.
Proofpoint helps organizations govern MCP servers and AI connections so employees and agents can use AI tools while reducing the risk of data exposure, unauthorized access, and uncontrolled agent behavior.