In this article
The new Workforce AI MCP is Check Point’s own Model Context Protocol server for Workforce AI Security. Connect a compatible AI client and you can query, analyze, and manage your employee AI usage policy in natural language instead of working through filters, screens, and individual rule checks.
- →ask plain questions such as which rule applies to a given user and application, or whether a rule is shadowed by a higher-priority one
- →work across users, managed assets, GenAI application usage, DLP data types, and agent and MCP activity in a single conversation
- →create and update rules by request once authorized administrators explicitly enable write capabilities
- →49 tools available today from any MCP-compatible AI client, with setup at mcp.checkpoint.com and documentation on GitHub
Check Point Workforce AI Security gives organizations granular control over how employees interact with AI, across applications, conversations, and agentic activity.
Through Manage Interactions, security teams can define which AI applications users can access, control how sensitive data is handled in prompts and file uploads, and govern the Model Context Protocol (MCP) servers, tools, and operations available to AI agents. Policies can account for users and groups, applications, data types, actions, and other contextual attributes.
This gives security teams considerable flexibility, but it also creates a rich set of policy and security data to work with.
The new Workforce AI MCP, Check Point’s own MCP server for Workforce AI Security, provides another way to access that depth.
By connecting an MCP-compatible AI client to Workforce AI Security, you can use natural language to retrieve policy information, investigate how rules are configured and applied, analyze your environment, and manage policy.
https://blog.checkpoint.com/wp-content/uploads/2026/09/WFAI-MCP-VIDEO-with-Audio.mp4
Complex analysis, simple questions
The most immediate benefit of the MCP is how directly you can interact with your security configuration.
Ask:
‣ Show me all Chats policies and summarize what each one allows or blocks.
The MCP retrieves the relevant configuration and presents it in a form that is easy to review. From there, you can investigate a specific rule, its scope, configured actions, or the sensitive data it protects.
The same approach can answer broader questions:
‣ Which AI services are my users allowed to access?
‣ Which rule applies to this user and application?
‣ Are any of my rules shadowed by a higher-priority rule?
These questions go beyond simply retrieving configuration. The MCP can use the context and tools exposed by Workforce AI Security to help analyze how policy actually applies in a given scenario.
That makes complex policy analysis significantly more accessible. Instead of translating a security question into a sequence of filters, screens, and individual policy checks, you can start with the question itself.
More than policy lookup
The Workforce AI MCP exposes a broad set of tools covering much more than policy retrieval.
You can query users and managed assets, investigate GenAI application usage, work with DLP data types and policy objects, analyze rule matching and shadowing, and explore activity across agents, MCP servers, and tools.
This becomes increasingly useful when the answer depends on several pieces of information.
For example, understanding an employee’s effective AI access may involve the user’s identity, the target application, existing policy rules, and rule priority. The MCP can work across those elements to help answer the question directly.
The result is a more efficient way to use the depth already available in Workforce AI Security, particularly for investigations and policy analysis that would otherwise require several steps.
From analysis to action
The same interface can also be used to manage policy.
For authorized administrators, write capabilities can be explicitly enabled to create and update rules through natural-language requests. For example:
‣ Create a Chats policy for unmanaged devices that blocks file uploads and monitors prompts.
The MCP translates the request into the corresponding Workforce AI Security policy configuration.
This creates a natural progression from understanding the current environment to making a change when needed. You can investigate a policy, identify what needs to change, and manage the relevant configuration from the same conversation.
49 tools available today
The Workforce AI MCP currently exposes 49 tools across policy analysis and management, users and assets, application discovery, DLP, and agent and MCP activity.
Because these capabilities are exposed through MCP, they can be used from compatible AI clients while remaining connected to the policy and security data in Workforce AI Security.
Get started
The Workforce AI MCP is available now
Workforce AI Security provides the granular controls needed to govern employee AI usage. The Workforce AI MCP makes those controls, and the information behind them, easier to access, analyze, and manage through natural language.
Configure
Connect the Workforce AI MCP to a compatible AI client and start asking questions of your policy.
Documentation
The complete list of available tools, configuration options, and technical documentation.
