# Sensitive Data Exposure Detector MCP for AI Agents AI Agent Connect

> Sensitive Data Exposure Detector intercepts and redacts secrets like AWS keys, GitHub tokens, and private keys from your AI agent's context. It uses pattern matching and entropy analysis to catch sensitive strings before they ever reach the model, ensuring your credentials and private data stay private.

## Overview
- **Category:** security
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_RLBK0WFvDSJHgpVyJooyxyZ2cdORMrpvMYciCMpx/ai-agent-connect
- **Tags:** security, redaction, privacy, regex, entropy

## Description

Working with AI agents often means feeding them logs, codebases, or configuration files. The danger is that these files frequently contain hardcoded secrets, API keys, or connection strings that you never intended to share with a third-party model. This MCP acts as a security buffer between your local files and your AI client. It scans everything you're about to send, looking for high-entropy strings and specific patterns that signal a security leak. If it finds an AWS key or a database password, it scrubs the sensitive parts immediately. You can set specific risk thresholds to decide how aggressive the redaction should be, or you can check the history of what was caught to keep an audit trail of your security posture. By connecting this to your workflow through the Vinkius catalog, you add a layer of automated privacy that works in the background while you code or analyze data.

## Tools

### get_redaction_audit
Retrieves a history of all intercepted redactions. Use this to review what sensitive data was caught during a session.

### scan_content
Analyzes a raw string for sensitive information. This is the primary way to clean text before it hits your agent.

### validate_safety_threshold
Checks if the risk score of a piece of content meets your safety requirements. It helps automate security enforcement.

## Prompt Examples

**Prompt:** 
```
I need to analyze this log file for errors, but I don't want to leak any keys.
```

**Response:** 
```
I've processed the log file. All sensitive credentials, including AWS access keys and database connection strings, have been redacted. 

**Processed Log Summary:**
- **Errors Found:** 3
- **Secrets Redacted:** 2
- **Status:** Safe to proceed.
```

**Prompt:** 
```
Is this block of code safe to send to my agent?
```

**Response:** 
```
I ran a check on that code block. It contains a high-entropy string that looks like a private key. 

| Risk Factor | Status |
| :--- | :--- |
| **Sensitive Patterns** | Detected (Private Key) |
| **Entropy Score** | High |
| **Action** | Redacted |

You can now safely use the cleaned version.
```

**Prompt:** 
```
What secrets were caught in my last session?
```

**Response:** 
```
In your recent session, the following redactions occurred:

* **GitHub Token:** Redacted from `config.yaml`
* **AWS Access Key:** Redacted from `env.local`
```

## Capabilities

### Scrub secrets from raw text
Automatically identifies and redacts sensitive strings like tokens and keys from any text you provide.

### Detect high-entropy leaks
Finds suspicious, random-looking strings that likely represent passwords or private keys using entropy math.

### Enforce security thresholds
Blocks or flags content if the calculated risk score exceeds a limit you define.

### Audit redaction history
Maintains a clear record of every piece of sensitive data that was intercepted and cleaned.

### Protect database credentials
Recognizes and hides connection strings to prevent leaking database access to your AI client.

## Use Cases

### Debugging production logs
An engineer needs to analyze a massive log file with an agent. The MCP catches the embedded API keys so the engineer can work safely.

### Refactoring legacy code
A developer asks an agent to rewrite a function. The MCP redacts the hardcoded database credentials found in the source file.

### Automated security auditing
A security tool uses validate_safety_threshold to ensure no sensitive user tokens are passed into a large language model context.

### Analyzing JWTs and tokens
A developer inspects authentication flows with an agent, while the MCP hides the actual JWT signatures to prevent exposure.

## Benefits

- Stop accidental leaks by using scan_content to clean text before it reaches your agent.
- Automate security checks with validate_safety_threshold to block high-risk data transfers.
- Maintain a clear paper trail of all intercepted secrets using get_redaction_audit.
- Catch non-obvious leaks using entropy analysis to find random strings that look like passwords.
- Protect your cloud infrastructure by automatically redacting AWS and GitHub tokens.
- Reduce manual oversight by letting the MCP handle the heavy lifting of pattern matching.

## How It Works

The bottom line is that it creates a one-way security filter that cleans your data before it leaves your machine.

1. Connect the MCP to your preferred AI client via Vinkius.
2. Point your agent toward the files or text you want to process.
3. The MCP intercepts the data, redacts the hits, and passes the safe version to your agent.

## Frequently Asked Questions

**How does the Sensitive Data Exposure Detector protect my secrets?**
It intercepts data before it reaches your AI client, using pattern matching and entropy calculations to identify and redact sensitive information like API keys and passwords.

**Can I use Sensitive Data Exposure Detector with Claude or Cursor?**
Yes, you can use this MCP with any MCP-compatible client, including Claude, Cursor, and Windsurf, once you connect it through Vinkius.

**What kind of sensitive data does the Sensitive Data Exposure Detector find?**
It is designed to catch common secrets like AWS keys, GitHub tokens, private keys, database connection strings, and JWTs.

**How do I know if a piece of data was actually cleaned?**
You can check the history of all intercepted redactions to see exactly what was caught and modified during your session.

**Can I set rules for what is considered too risky to share?**
Yes, you can use the built-in threshold tool to define a risk score, allowing you to automatically block content that is too sensitive.

**How does the redaction process work?**
The `scan_content` tool identifies sensitive patterns and replaces them with a placeholder in the format `[REDACTED_TYPE:prefix4...suffix4]`. This allows for debugging without exposing the actual secret.

**Can I audit the redactions that have occurred?**
Yes, you can use the `get_redaction_audit` tool to retrieve a history of all redaction events performed during the current session.

**What is the exposure risk score?**
The exposure risk score is a value from 0.0 to 1.0. You can use `validate_safety_threshold` to check if a detected risk exceeds your specific security requirements.