agent-security-dlp

What This Skill Does

The Agent Security DLP (Data Loss Prevention) skill is an enterprise-grade security framework designed to protect AI agents from data leakage and malicious manipulation. It acts as an intelligent intermediary, analyzing data flows across five critical layers: input (preventing prompt injection), internal memory, tool execution, output filtering (sensitive data masking), and comprehensive auditing. With a robust library of 170 regex-based rules spanning over 25 industries, this skill ensures that your sensitive business data—from API keys and medical records to financial credentials—is never accidentally exposed or compromised. It provides real-time monitoring and configurable enforcement modes to balance security with developer productivity.

Installation

To integrate this security layer into your OpenClaw environment, use the command-line interface to pull the module directly from the official repository. Execute the following command in your terminal: clawhub install openclaw/skills/skills/caidongyun/agent-security-dlp Once installed, you can verify the integrity of the setup by running the status check utility: python3 skills/agent-security-dlp/bin/agent-dlp status. Ensure your configuration files located in config/config.json are properly mapped to match your security requirements, choosing between 'normal', 'strict', or 'personal' modes based on your environment's needs.

Use Cases

This skill is ideal for teams deploying AI agents in high-compliance environments. For example, in a medical setting, the DLP agent automatically masks patient records before they are transmitted to a third-party LLM. In financial services, it detects and prevents the accidental leakage of bank card numbers or private cryptographic keys in outbound communications. Development teams use it to automatically intercept 'jailbreak' prompts that attempt to override system instructions. By deploying this, companies can audit every interaction, ensuring that internal communications comply with corporate privacy policies and regulatory standards such as PII and data privacy laws.

Example Prompts

1. "Check if the recent output to the user contained any exposed API keys or cleartext credentials from the last tool execution."
2. "Update the DLP configuration to strict mode and monitor for any attempts to exfiltrate company salary data in the logs."
3. "Audit the latest conversation flow to identify potential prompt injection attacks directed at the agent's core system prompt."

Tips & Limitations

To maximize the effectiveness of the DLP agent, perform regular updates to the rule sets to stay protected against new patterns of data exfiltration. While the regex-based engine is highly performant and suitable for production, be aware that static rule matching may have limitations compared to transformer-based semantic scanning. We recommend using 'strict' mode in production environments to maximize protection. Avoid disabling the audit log feature, as it provides the only forensic trail for identifying attempted security breaches. Finally, always validate your custom regex additions in a staging environment before pushing them to your primary agent instance to prevent over-blocking legitimate business communications.