Data Retention

Skill Profile

(Select at least one profile to enable specific modules)

[ ] DevOps
[x] Backend
[ ] Frontend
[ ] AI-RAG
[ ] Security Critical

Overview

Data retention is a critical component of data governance, ensuring that data is kept only as long as necessary for legal, business, or regulatory purposes. This skill provides comprehensive patterns for implementing retention policies, automated deletion, archival strategies, and compliance with various regulations.

Why This Matters

Data retention is essential for:

Legal Compliance: Meeting GDPR, SOX, HIPAA, PCI DSS requirements
Cost Management: Reducing storage costs by deleting unnecessary data
Risk Mitigation: Reducing data breach exposure
Operational Efficiency: Maintaining optimal database performance
Audit Readiness: Providing evidence for compliance audits

Core Concepts & Rules

1. Core Principles

Follow established patterns and conventions
Maintain consistency across codebase
Document decisions and trade-offs

2. Implementation Guidelines

Start with the simplest viable solution
Iterate based on feedback and requirements
Test thoroughly before deployment

Inputs / Outputs / Contracts

Inputs:
- <e.g., env vars, request payload, file paths, schema>
Entry Conditions:
- <Pre-requisites: e.g., Repo initialized, DB running, specific branch checked out>
Outputs:
- <e.g., artifacts (PR diff, docs, tests, dashboard JSON)>
Artifacts Required (Deliverables):
- <e.g., Code Diff, Unit Tests, Migration Script, API Docs>
Acceptance Evidence:
- <e.g., Test Report (screenshot/log), Benchmark Result, Security Scan Report>
Success Criteria:
- <e.g., p95 < 300ms, coverage ≥ 80%>

Skill Composition

Depends on: None
Compatible with: None
Conflicts with: None
Related Skills: None

Quick Start / Implementation Example

Review requirements and constraints
Set up development environment
Implement core functionality following patterns
Write tests for critical paths
Run tests and fix issues
Document any deviations or decisions

# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions

Retention policies are documented and approved
Legal requirements are understood
Database supports soft delete operations
Legal holds can be tracked and enforced

Compatibility

GDPR Article 5(1)(e) compliant
SOX Section 404 compliant
HIPAA Security Rule compliant
PCI DSS Requirement 3 compliant

Test Scenario Matrix

| Scenario | Input | Expected Output | Priority | |----------|-------|-----------------|----------| | Create retention policy | Policy details | Policy created with ID | P0 | | Delete expired data | Data type | Count of deleted records | P0 | | Archive old data | Data type | Count of archived records | P1 | | Create legal hold | Case details | Hold created with ID | P0 | | Release legal hold | Hold ID | Hold marked as released | P0 | | Check compliance | Data type, retention period | Compliance status | P0 | | Soft delete record | Table name, record ID | Record marked as deleted | P0 | | Hard delete record | Table name, record ID | Record permanently deleted | P0 |

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

Top Threats: Injection attacks, authentication bypass, data exposure

[ ] Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
[ ] Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
[ ] Authorization: Validate user permissions before state changes

2. Performance & Resources

[ ] Execution Efficiency: Consider time complexity for algorithms
[ ] Memory Management: Use streams/pagination for large data
[ ] Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

[ ] Design Pattern: Follow SOLID principles, use Dependency Injection
[ ] Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

[ ] Logging Standards: Structured JSON, include trace IDs request_id
[ ] Metrics: Track error_rate, latency, queue_depth
[ ] Error Handling: Standardized error codes, no bare except
[ ] Observability Artifacts:
- Log Fields: timestamp, level, message, request_id
- Metrics: request_count, error_count, response_time
- Dashboards/Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

Thinking Process: Analyze root cause before fixing. Do not brute-force.
Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
Self-Review: Check against Guardrails & Anti-patterns before finalizing.
Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

[ ] Tests passed + coverage met
[ ] Lint/Typecheck passed
[ ] Logging/Metrics/Trace implemented
[ ] Security checks passed
[ ] Documentation/Changelog updated
[ ] Accessibility/Performance requirements met (if frontend)

Anti-patterns

Indefinite Retention: Keeping data forever without justification
Premature Deletion: Deleting data before retention period expires
Ignoring Legal Holds: Deleting data under legal hold
No Audit Trail: Not logging deletion operations
Manual Deletion: Relying on manual deletion processes

Reference Links & Examples

Internal documentation and examples
Official documentation and best practices
Community resources and discussions

Versioning & Changelog

Version: 1.0.0
Changelog:
- 2026-02-22: Initial version with complete template structure