Which aspect of EPP are you submitting for?
Endpoint Protector Client
What is a one sentence summary of your feature request?
Request for GenAI Target App Expansion, MCP Protocol Detection, and IDE Policy Guidance
Please describe your idea in detail. What is your problem, why do you feel this idea is the best solution, etc.
As enterprise AI adoption transitions from basic user-driven prompts (e.g., pasting text into a ChatGPT browser window) to automated, agentic workflows and IDE-integrated development environments (e.g., VS Code, Cursor, Claude Desktop), traditional Endpoint Protection (EPP) and Data Loss Prevention (DLP) solutions like Netwrix Endpoint Protector are facing a critical architectural coverage gap.
Traditional DLP was built around human-driven actions: web forms, clipboard copy-paste events, and manual file uploads. Modern GenAI workflows, however, rely on Model Context Protocol (MCP), background process-to-process communication (STDIO, local HTTP/SSE), and third-party service connectors (GitHub, Canva, Slack plugins).
Because these agentic tools pull local files, database records, and proprietary source code via background processes without triggering browser events or standard clipboard hooks, existing EPP tools fail to inspect, audit, or block this data exfiltration.
To address this gap, we request that Netwrix expand its GenAI coverage beyond standard web UIs by introducing native protocol-aware inspection for MCP communications, local inter-process channels (STDIO/HTTP), and granular controls over third-party connectors inside AI services. Implementing process-level and protocol-aware inspection within Netwrix EPP is the best solution because it extends endpoint protection directly to developer tools and agentic AI without creating security blind spots or requiring complex third-party network proxies for basic compliance.
How do you currently solve the challenges you have by not having this feature?
Without native MCP and agentic protocol inspection in our DLP, we currently manage these security and operational risks through high-friction workarounds:
Nuclear Process and Port Blocking:
We use local firewalls and application control policies to hard-block unapproved local executable binaries and restrict IDE AI extensions.
Drawback: This severely impairs developer productivity and stops modern AI-assisted software engineering workflows.
All-or-Nothing GenAI Network Restrictions:
Because we cannot inspect or control third-party plugins/connectors (e.g., GitHub or Canva connectors inside ChatGPT) at a granular level, we are forced to either block the entire GenAI platform or allow it completely with unmonitored risk.
Drawback: This creates heavy friction for end users and increases the risk of Shadow IT on personal devices.
Reliance on Human Oversight and Policy Manuals:
To prevent background MCP tools from pulling sensitive credentials, API keys, or PII into AI prompts, we rely heavily on written corporate policy compliance and manual code reviews.
Drawback: Human error remains extremely high, and background processes can still automatically extract data without explicit developer intervention.