AI coding assistants have redrawn the IDE’s security boundary. Cycode AI Guardrails enforces controls at the moment secrets would escape—before they ever reach an AI model or external service.
The IDE Is Now a Data Gateway
AI coding assistants have changed the IDE’s security boundary. Prompts, file context, and tool invocations are no longer local operations—they’re outbound data flows to model providers, plugins, and external services.
Traditional secret detection happens in CI pipelines or during PR reviews—after code is written. That’s insufficient for AI-assisted development, where sensitive data can leak in real-time through channels that never touch your repository.
Three Attack Surfaces Inside the IDE
AI assistants do more than generate code. They read files, build context, and invoke tools. That creates three distinct attack surfaces—each representing a different way secrets can escape your development environment:
| Attack Surface | How It Happens | Risk Level |
| Prompt Submission | Developers paste credentials while debugging authentication issues | High frequency |
| File Reads | AI agents automatically read .env, config files, and keys to build context | Silent & automatic |
| MCP Tool Execution | Secrets embedded in payloads sent to Jira, GitHub, Slack, or other services | Highest risk |
None of these show up in git history. None of them trigger your CI scanners. But all of them represent real credential exposure to external services.
Real-Time Interception at the IDE Boundary
Cycode AI Guardrails uses native hooks exposed by AI coding assistants to enforce security controls at the IDE boundary—before prompts are sent, before files are added to agent context, and before tool calls are executed.
Prompt Protection: Catch Credentials Before They’re Sent
Prompt submission is the most common AI-related leakage path. A developer debugging an OAuth issue pastes a token. Someone troubleshooting a database connection includes the connection string. It happens constantly.
How Guardrails stops it:
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The
beforeSubmitPrompthook intercepts every message -
Cycode’s detection engine scans for credential patterns
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Secrets are blocked before reaching the AI model
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The prompt never leaves the IDE
The secret value is never exposed to the model provider or logged in any external service.
File Read Protection: Block Silent Context Leakage
File reads are a silent way secrets leak into AI context. When an agent helps debug an issue, it automatically reads files to understand the problem—including sensitive configuration files, environment variables, and credential stores.
How Guardrails stops it:
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The
beforeReadFilehook intercepts file access requests -
Path-based rules immediately block known sensitive patterns (
.env,.ssh/*,*kubeconfig*) -
Content scanning catches secrets in files that pass initial checks
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Protected files are never added to the AI’s context
You can configure policies to protect specific directories—like blocking all reads under /deploy or /secrets—ensuring sensitive infrastructure files stay out of AI conversations entirely.
MCP Tool Protection: Prevent Secrets from Reaching External Services
MCP tool calls represent the highest-risk leakage path. In a typical scenario, an AI agent debugging an issue gathers relevant context—including environment variables and configuration values—then attempts to call an external tool like Jira to create a ticket with the collected data.
How Guardrails stops it:
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The
beforeMCPExecutionhook intercepts tool invocations -
Cycode scans the full MCP payload for embedded secrets
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Tool execution is blocked before anything leaves the IDE
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No secret is sent to Jira, GitHub, Slack, or any other external service
This protection is critical as AI agents become more autonomous and integrate with more external services.
Complete Visibility for Security Teams
Blocking secrets is essential—but security teams also need visibility. Cycode AI Guardrails logs every AI interaction in a centralized dashboard:
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Every prompt, file read, and MCP tool call—scanned and logged in one place
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Clear status for each interaction: blocked, warned, or passed after validation
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User attribution: see which developers triggered which events
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Finding breakdown: understand whether secrets were in prompts, files, or tool arguments
Even “Passed” interactions were checked and cleared. This gives security teams confidence that every AI interaction has been validated—without creating friction that slows developers down.
The result: proof that secrets are protected across the entire AI workflow, before anything leaves the IDE.
Enterprise-Ready Deployment
Cycode AI Guardrails is built for security teams managing AI adoption at scale—from individual developers to organization-wide rollouts.
Developer Installation: One Command
Developers can install Guardrails themselves with a single command:
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Repository-level: Protect a specific project. Run
./install.sh --scope repoin any repository, and every developer working in that codebase is automatically covered. -
User-level: Protect everything. Run
./install.sh --scope userto enable Guardrails globally across all projects on that machine.
No complex setup. No configuration files to edit manually. Just run the command and Guardrails is active.
Enterprise Deployment: MDM Distribution
For organizations that need centralized control, Cycode AI Guardrails supports deployment via Mobile Device Management (MDM) solutions. Security teams can push Guardrails across the entire organization—ensuring every developer is protected from day one, without relying on individual installation.
Gradual Rollout with Block or Report Modes
Security teams have full control over enforcement behavior:
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Block mode (default): Secrets are stopped immediately. The operation is denied before any data leaves the IDE.
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Report mode: The operation proceeds, but the event is logged to Cycode for security team review. Ideal for gradual rollouts—gain visibility into AI interactions, identify risky patterns, and transition to full blocking when ready.
This flexibility lets enterprises start with visibility, build developer awareness, and move to full enforcement on their own timeline.
Shift Left—All the Way to the IDE
Traditional secret detection happens in CI or during pull request reviews. By then, secrets have already been written to disk, committed to history, and potentially exposed through AI interactions.
Cycode AI Guardrails shifts secret protection from post-commit detection to real-time prevention within the IDE.
| Traditional Approach | Cycode AI Guardrails |
| Detects secrets in CI pipeline | Intercepts secrets before AI submission |
| Scans committed code | Scans prompts, file reads, and tool calls |
| Alerts after exposure | Blocks before exposure |
| Requires remediation | Prevents the incident entirely |
Three interception points. Real-time scanning. Blocked before reaching any AI model or external service.
Your secrets never leave the IDE.
Get Started
Cycode AI Guardrails works with Cursor and Claude Code, with support for additional AI coding assistants on the roadmap. If you’re already using Cycode for secret scanning, SAST, or SCA, setup takes minutes:
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Install and authenticate the Cycode CLI
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Run the installation script
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Guardrails activate automatically for AI coding sessions
No changes to developer workflows. No new tools to learn. Just real-time protection running at the IDE boundary.
The Bottom Line
AI coding assistants have redrawn the security boundary of the IDE. Prompts, file context, and tool invocations are now outbound data flows—and traditional security controls don’t cover them.
Cycode AI Guardrails intercepts secrets at all three attack surfaces: prompt submission, file reads, and MCP tool execution. Every interaction is scanned, logged, and—when necessary—blocked before anything leaves the developer’s machine.
Real-time prevention. Complete visibility. Zero friction.
That’s how you secure AI-assisted development.
