How to Write Custom Regex Rules to Mask Internal Corporate Data for AI Workflows

Why Standard PII Rules Miss Proprietary Information

Most out-of-the-box data loss prevention (DLP) tools only look for common identifiers like emails, phone numbers, or credit card numbers. If an engineer is using AI to review code containing an internal project name (e.g., "Project Titan") or a database server path, generic filters will ignore it. This represents a significant risk of intellectual property leakage, especially when using tools like Claude Code or Cursor.

Building Custom RegEx Rules for AI Prompt Security

To achieve total security, developers and security managers need to define custom expressions tailored to their organization. By implementing custom rules (such as PROJ-\d{4} or api_[a-zA-Z0-9]{32}), you can prevent these proprietary strings from being uploaded. PrivacyScrubber processes these custom rules locally, ensuring the patterns run inside a Zero-Trust Data Protection container.

Integrating Custom Presets Across the Organization

Once you build your custom regex rule set, you can share it with your team. This ensures that every developer debugging logs or using AI assistants is bound by the same data classification guidelines. This is ideal when cleaning database dumps or analyzing production logs during outages, preventing sensitive identifiers from leaking. For automated pipelines, custom rules easily integrate into a Model Context Protocol (MCP) workflow.