dlp

Detect and block outbound data exfiltration in agent tool calls.

Updated Apr 21, 2026
One-click install
npx skills add https://github.com/brucebanner010198-commits/DevSecOps-Agency --skill dlp
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: dlp
Source: https://github.com/brucebanner010198-commits/DevSecOps-Agency/tree/main/skills/dlp
Command: npx skills add https://github.com/brucebanner010198-commits/DevSecOps-Agency --skill dlp

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Outbound data leaks from agent tool calls pose a risk to credentials, secrets, and sensitive information. This Skill provides automated detection and protection to prevent data exfiltration.

Core Features & Use Cases

  • Static pattern matching and NLP-based scanning of outbound tool-call payloads, URLs, headers, and query strings to detect secrets and PII/PHI.
  • Per-call enforcement with redaction tokens and immediate blocking, plus optional waivers for sanctioned data flows.
  • Chain-correlation window to detect split-secret leakage across multiple calls, ensuring end-to-end coverage in dynamic agent workflows.
  • Easy integration with existing secrets vaults and runtime hooks, with a configurable custom term library per project.

Quick Start

Configure the DLP system to scan outbound tool calls for sensitive data and block or redact matches in real time.

Frequently Asked Questions about dlp

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I prevent outbound data leaks from agent tool calls?

Detecting split-secret leakage requires a chain-correlation window that tracks outbound data across multiple agent calls, correlating payloads to identify fragmented secrets distributed across separate requests before enforcing redaction.

Can I block sensitive data exfiltration from agent calls without breaking legitimate workflows?

You can balance security with legitimate workflows by configuring per-project waivers for sanctioned data flows, applying redaction tokens instead of hard blocking, and integrating with existing secrets vaults and runtime hooks.

How does NLP scanning detect PII and PHI in outbound agent payloads?

NLP-based scanning detects PII and PHI in outbound agent payloads by analyzing arguments, URL paths, headers, and query strings for sensitive language patterns, applying static matching alongside natural language processing to identify exposed personal data.

Does outbound DLP scanning support custom term libraries for project-specific sensitive data?

Outbound DLP scanning supports a configurable custom term library per project, allowing teams to define project-specific sensitive terms and patterns for detection across all outbound agent tool calls.

What is the best way to detect split-secret leakage across multiple agent calls?

The best way to detect split-secret leakage is using a chain-correlation window that monitors outbound tool calls over time, correlating fragmented data across multiple requests to catch secrets intentionally split across separate payloads.

When should I use per-project waivers for outbound data loss prevention?

Use per-project waivers for outbound data loss prevention when you have sanctioned data flows requiring legitimate transmission of sensitive information, allowing approved calls to bypass blocking while maintaining scanning on all other traffic.