Keep your AI coding assistants secure

AI coding security is the set of controls that keep AI coding assistants such as Claude Code, Codex, Cursor and GitHub Copilot from becoming a risk to your organization. It covers what the assistant reads, which tools and MCP servers it can call, what data leaves your environment in prompts and responses, and keeping a record of everything the agent did so security teams can review it.

The main risks are prompt injection, where hidden instructions inside a web page, document, tool result or MCP server description steer the agent; shadow AI, where developers connect unapproved tools or MCP servers; and data leakage, where secrets, API keys or proprietary code end up in a prompt or response. Because these assistants can read your codebase and act on internal systems, each of these can have real impact.

Place a gateway between your coding assistants and the models and tools they use. With TrustGate in that position, you can scan every input for hidden instructions before it reaches the model, approve or block specific MCP servers and tools per team, mask secrets and credentials before they leave your environment, and apply the same policies to every assistant without changing how developers work.

Yes. When traffic flows through the gateway, every prompt, tool call and response is logged with the user, the assistant and the time it happened. Security and compliance teams can search that audit trail to answer exactly what an agent did and when, instead of piecing it together from developer machines after an incident.

Route Claude Code through TrustGate so its model calls and MCP tool calls pass through a single control point. From there you can block prompt injection hidden in files or tool results, restrict which MCP servers it can reach, mask secrets and source code in outgoing prompts, and keep a full log of its activity. The same setup works for Codex, Cursor and GitHub Copilot.