uk:
English: Europe, Middle East, Africa
AI Security
Unify AI Security Across People, Agents, and MCP
Secure every layer of your enterprise AI, from the first employee prompt to the most complex agentic workflow.
Enterprise AI is expanding across three layers—each with distinct security challenges
Employees are adopting AI tools in every channel they work in, often without the knowledge of security teams. Applications are embedding AI into workflows where models process sensitive information and influence business decisions. Autonomous agents are connecting to enterprise systems through Model Context Protocol (MCP) and direct integrations. They reason and act on behalf of users, often without continuous oversight.
These layers are connected, and so are the risks they introduce. Legacy security tools were designed to monitor only human access to cloud applications. They cannot inspect what AI is doing inside a workflow, verify that an agent's actions are appropriate or reconstruct what happened when something goes wrong.
Secure every AI interaction across your organisation with unified discovery, runtime enforcement, and a defensible audit trail
Discover and inventory every AI application, agent, MCP server, toolchain and external service across your environment. Continuously assess AI risk at every layer.
Enforce context-aware policy at runtime across employee AI usage, application behaviour, agent actions and MCP connections. Evaluate every interaction. Block, redact, restrict or escalate as needed via a unified policy engine.
Produce a defensible audit trail for every AI interaction, from single user prompts to multistep agent workflows. Get full transaction reconstruction, user attribution, and security context for boards, regulators, and incident response.
Secure your enterprise at every layer
Secure AI usage by people
Users often adopt AI tools without the knowledge of security. Legacy tools can block access to unsanctioned AI services, but they cannot 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.
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 cannot validate whether an agent's actions match its assigned task.
Proofpoint Agentic AI Security governs agent behaviour with intent-based detection, runtime observability across multistep workflows and behavioural anomaly detection. It reconstructs every agent-based transaction in full, from user request through agent action to final outcome.
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.
FAQ
-
How can I discover shadow AI apps, agents and integrations in my company environment?
You can discover shadow AI activity by using a platform that analyses network signals, identity context and API behaviour to locate unauthorised apps, agents and MCP integrations.You can discover shadow AI activity by using a platform that analyses network signals, identity context and API behaviour to locate unauthorised apps, agents and MCP integrations. A solution set such as Proofpoint AI Security correlates traffic patterns, permission use and model interactions to identify AI services that bypass standard onboarding. It builds an inventory tied to risk, so you can see where AI connects, what data it uses and how it operates.
This approach delivers:
- Asset inventory: catalogues AI apps, agents, MCP servers and toolchains.
- Integration mapping: Shows API paths and MCP links across systems.
- Risk scoring: Rates sensitivity, scope, and exposure by context.
- Onboarding safeguards: Supports approval, labelling, and policy templates.
-
How can I create an audit trail of AI prompts and agent workflows for compliance and reporting?
You can produce a defensible AI audit trail by capturing each prompt, model output, tool call, and agent step, then linking them into a single event sequence.You can produce a defensible AI audit trail by capturing each prompt, model output, tool call and agent step, then linking them into a single event sequence. A solution set such as Proofpoint AI Security attaches user identity, agent identity and policy actions to each stage of the workflow. It reconstructs how a decision was made, validates the chain of actions and exports reliable evidence for audits or regulatory review.
Audit evidence typically includes:
- Event lineage: Prompt → decision → tool call → output.
- Attribution: User and agent identity with effective permissions.
- Control outcomes: Blocks, redactions, restrictions, escalations.
- Exportable records: Structured logs for audits and investigations.
-
How can I reduce the risk of data loss and sensitive data exposure through AI tools and LLMs?
You can reduce data loss risk by evaluating prompts and responses at runtime and enforcing policy based on data classification, user role and task intent.You can reduce data loss risk by evaluating prompts and responses at runtime and enforcing policy based on data classification, user role and task intent. A solution set such as Proofpoint AI Security analyses each interaction for sensitive content, over‑broad requests and unsafe outputs. It can redact or block activity before data leaves your control. It also generates a record of what was flagged and why enforcement occurred.
Effective controls include:
- Prompt/output scanning: Detects secrets, personally identifiable information (PII), intellectual property (IP), and regulated data.
- Granular enforcement: Redacts fields or blocks high‑risk requests.
- Task‑aware limits: Constrains retrieval and tool use to match scope of tasks.
- Posture‑aware policies: Tightens controls for higher‑risk apps or agents.
-
How is agentic AI security different from traditional access controls?
Agentic AI security evaluates an agent’s intent, workflow plan and executed actions, not merely the credentials attached to the request.Agentic AI security evaluates an agent’s intent, workflow plan and executed actions, not merely the credentials attached to the request. A solution set such as Proofpoint Agentic AI Security monitors each step in a multitool sequence and checks whether the agent’s behaviour matches the defined task. It inspects tool calls and identifies deviations from expected patterns. It also reconstructs the full sequence of actions so analysts can verify correctness and investigate anomalies.
Practical controls include:
- Intent evaluation: compares requested outcomes with the agent’s action path.
- Workflow tracing: tracks execution steps, tool calls and decision points.
- Anomaly detection: flags overreach, loops or unusual behaviour.
- Reconstruction: produces a defensible record for incident response.
