DeepKeep Adds Runtime Guardrails for AI Coding Agents With AI Lens for Developers

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DeepKeep has introduced AI Lens for Developers, a security layer designed to give CISOs visibility and policy control over AI coding agents operating on developer machines. The product extends DeepKeep’s existing AI usage-control and runtime-protection capabilities to tools such as Cursor and Claude Code, with support for GitHub Copilot, OpenAI Codex, Lovable, Windsurf and additional coding agents planned.

The launch addresses a security problem created by the growing autonomy of developer tools. Modern coding agents can inspect and modify local files, execute shell commands and invoke external services through the Model Context Protocol (MCP). Those permissions make the tools useful, but they also allow an agent to expose credentials, change sensitive code or interact with systems outside the controls security teams traditionally apply to cloud applications.

The result is a widening visibility gap. A company may approve a coding assistant as a product while having little evidence of what an individual agent did during a particular session. As Unite.AI recently explored, security teams increasingly need to trace agent tool calls, command execution and system-changing events, not simply maintain a list of approved AI services.

AI Lens for Developers is built as a lightweight plug-in rather than a full endpoint agent. DeepKeep says it hooks into the coding-agent workflow to inspect prompts, file reads, shell commands and MCP tool calls before and after they run. Each activity can be routed through the company’s policy system for an allow, block or audit decision.

The distinction between pre-execution and post-execution monitoring is important. An audit trail can explain why an incident occurred, but it cannot prevent a destructive command or stop a secret from leaving a developer’s environment. DeepKeep is positioning AI Lens as both a preventive control and a source of evidence