PII detection rules

[ralyodio]

📋 Product Requirements Document

PRD: PII detection rules

Issue: #148
Milestone: Phase 4: Security
Labels: pii-redaction, hacktoberfest

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PRD: Implementation of PII Detection Rules

Overview

The implementation of Personally Identifiable Information (PII) detection rules within MeshHook is a critical enhancement aimed at bolstering the security and privacy of the data processed through the system. This feature is designed to automatically identify and manage PII within workflow data, either by redacting or encrypting sensitive information before it is stored or logged. This initiative aligns with MeshHook's overarching goals of providing a secure, reliable, and privacy-compliant workflow automation platform.

Purpose

The purpose of this feature is to ensure that MeshHook can automatically detect and appropriately handle PII, thereby protecting sensitive information and ensuring compliance with global data protection regulations. This is critical for maintaining the trust of users and customers, and for upholding MeshHook's reputation as a secure and privacy-respecting platform.

Alignment with Project Goals

• Security Enhancement: By detecting and redacting or encrypting PII, we add an essential layer of security, further protecting user data.
• Compliance with Data Protection Regulations: Helps ensure MeshHook workflows are compliant with GDPR, CCPA, and other privacy laws.
• Support for Multi-tenancy: Augments existing RLS security measures by providing tenant-specific PII handling capabilities.

Functional Requirements

1. PII Detection Engine: Develop a modular engine capable of identifying various types of PII within workflow data payloads.
2. Customizable Detection Rules: Enable administrators to customize PII detection rules, tailoring the engine to recognize project-specific PII patterns.
3. Redaction and Encryption: Provide mechanisms for the automatic redaction or encryption of identified PII.
4. Audit and Compliance Logging: Implement logging mechanisms for PII detection and handling actions to support auditability and compliance, without storing PII in logs.
5. Management API: Develop secure API endpoints for the management of PII detection rules.

Non-Functional Requirements

• Performance: Ensure PII detection and handling introduce no more than 10ms additional latency per workflow run.
• Security: Adhere to best practices in secure data handling and processing, particularly for operations involving PII.
• Reliability: Achieve a 99.9% accuracy rate in PII detection to minimize false positives and negatives.
• Maintainability: Ensure the PII detection rules engine is modular, well-documented, and easy to extend or modify.

Technical Specifications

Architecture Context

• Integration with SvelteKit: Leverage SvelteKit for building the UI components for PII rule management.
• Utilization of Supabase Postgres: Utilize Supabase for storing PII rules and audit logs, leveraging its real-time capabilities.
• Extension of Workers Architecture: Incorporate PII detection tasks within the existing workers' architecture, ensuring seamless integration with workflow processing.

Implementation Approach

1. PII Detection Engine Design: Outline and develop the architecture of the PII detection engine, ensuring modularity and extensibility.
2. Data Model Extension: Update schema.sql to include new tables for pii_rules and pii_audit_logs.
3. Rule Management API: Implement secure API endpoints for PII rule CRUD operations.
4. Integration and Performance Testing: Conduct thorough testing to ensure seamless integration and adherence to performance benchmarks.
5. Deployment and Monitoring: Roll out the feature in a phased approach, closely monitoring performance and functionality for optimizations.

Data Model Changes

• pii_rules Table: To store PII detection rule configurations.
• pii_audit_logs Table: To log actions related to PII without including the PII itself.

API Endpoints

• POST /api/pii-rules: To create a new PII detection rule.
• GET /api/pii-rules: To list all PII detection rules.
• PUT /api/pii-rules/{id}: To update an existing PII detection rule.
• DELETE /api/pii-rules/{id}: To remove a PII detection rule.

Acceptance Criteria

• PII detection engine functional and integrated into workflow processing.
• Customizable PII detection rules can be created and managed via API.
• Workflow data is automatically redacted or encrypted based on PII rules.
• Audit logs accurately reflect PII handling actions.
• System performance meets specified benchmarks with less than 10ms overhead.

Dependencies and Prerequisites

• Access to MeshHook's existing codebase and development environment.
• A valid Supabase account for database operations.
• Familiarity with global PII regulations for compliance considerations.

Implementation Notes

Development Guidelines

• Adhere to MeshHook's coding standards, ensuring new code is modular, well-commented, and secure.
• Implement comprehensive unit and integration tests, adopting a TDD approach where feasible.

Testing Strategy

• Perform extensive unit and integration testing, focusing on the PII detection accuracy and system performance.
• Conduct security audits to validate the safe handling of PII.

Security Considerations

• Use TLS for all data in transit.
• Implement least privilege access controls for API endpoints.
• Regularly rotate any encryption keys used for PII data.

Monitoring and Observability

• Set up detailed logging for rule management operations and PII handling actions.
• Monitor performance metrics to identify and mitigate any impact from PII processing.
• Establish alerting mechanisms for operational failures in the PII detection process.

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This PRD was AI-generated using gpt-4-turbo-preview from GitHub issue #148
Generated: 2025-10-10

📎 Generated Documentation

• 📄 PRD Document: 148-pii-detection-rules.md
• 🎨 PlantUML Diagram: 148-pii-detection-rules.puml
• 🖼️ Diagram Image: 148-pii-detection-rules.png

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This issue body was auto-generated from the PRD. Original issue content is preserved in the PRD document.
Last updated: 2025-10-10