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Ai Audit Trail

AI Audit Trail & Accountability is a system that records and tracks

personAuthor: jakexiaohubgithub
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Ai Audit Trail

Skill Profile

(Select at least one profile to enable specific modules)

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

Overview

AI Audit Trail & Accountability is a system that records and tracks all AI decision-making comprehensively, enabling backward investigation (Audit) and accountability for outcomes. This is especially important for organizations that must comply with regulations such as the EU AI Act and NIST AI RMF.

Why This Matters

  • <Benefit>: <short explanation>
  • <Benefit>: <short explanation>
  • <Benefit>: <short explanation>

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

  1. Review requirements and constraints
  2. Set up development environment
  3. Implement core functionality following patterns
  4. Write tests for critical paths
  5. Run tests and fix issues
  6. Document any deviations or decisions
# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions / Constraints / Non-goals

  • Assumptions:
    • Development environment is properly configured
    • Required dependencies are available
    • Team has basic understanding of domain
  • Constraints:
    • Must follow existing codebase conventions
    • Time and resource limitations
    • Compatibility requirements
  • Non-goals:
    • This skill does not cover edge cases outside scope
    • Not a replacement for formal training

Compatibility & Prerequisites

  • Supported Versions:
    • Python 3.8+
    • Node.js 16+
    • Modern browsers (Chrome, Firefox, Safari, Edge)
  • Required AI Tools:
    • Code editor (VS Code recommended)
    • Testing framework appropriate for language
    • Version control (Git)
  • Dependencies:
    • Language-specific package manager
    • Build tools
    • Testing libraries
  • Environment Setup:
    • .env.example keys: API_KEY, DATABASE_URL (no values)

Test Scenario Matrix (QA Strategy)

| Type | Focus Area | Required Scenarios / Mocks | | :--- | :--- | :--- | | Unit | Core Logic | Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage | | Integration | DB / API | All external API calls or database connections must be mocked during unit tests | | E2E | User Journey | Critical user flows to test | | Performance | Latency / Load | Benchmark requirements | | Security | Vuln / Auth | SAST/DAST or dependency audit | | Frontend | UX / A11y | Accessibility checklist (WCAG), Performance Budget (Lighthouse score) |

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

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