AI-native systems present unique security and compliance challenges due to their dynamic, data-driven nature and reliance on machine learning models. This document outlines the core goals and guiding principles to ensure these systems remain secure, trustworthy, and compliant with regulatory standards.

1. Data Security

  • Goal: Ensure that all data used in AI systems is stored, transmitted, and processed securely.
  • Key Practices:
    • Encrypt data at rest and in transit.
    • Implement strict access controls and data masking.
    • Use secure data pipelines and audit logs for all data interactions.

2. Model Security

  • Goal: Protect AI models from theft, tampering, and adversarial attacks.
  • Key Practices:
    • Sign and verify model artifacts.
    • Monitor for adversarial input and model drift.
    • Use sandboxing and runtime protections during inference.

3. Privacy Compliance

  • Goal: Ensure AI systems comply with global data protection regulations (e.g., GDPR, CCPA).
  • Key Practices:
    • Enable data subject rights (access, deletion, correction).
    • Limit data retention and enforce purpose limitation.
    • Perform regular Data Protection Impact Assessments (DPIAs).

4. Transparency and Explainability

  • Goal: Provide clear insight into AI system decisions and operations.
  • Key Practices:
    • Document model training datasets, features, and decisions.
    • Enable explainability tools and dashboards for stakeholders.
    • Maintain traceability from input to output for audits.

5. Secure Deployment and Operations

  • Goal: Ensure secure and resilient deployment of AI systems in production.
  • Key Practices:
    • Use containerization and automated security scanning.
    • Implement incident detection and response for AI-specific threats.
    • Apply least privilege and zero trust principles to AI services.

6. Compliance and Governance

  • Goal: Maintain continuous compliance with internal policies and external regulations.
  • Key Practices:
    • Maintain auditable records of model lifecycle and decision-making.
    • Integrate compliance checks into CI/CD pipelines.
    • Assign governance roles for oversight of AI ethics and compliance.

7. Continuous Monitoring and Improvement

  • Goal: Regularly assess and improve the security posture of AI systems.
  • Key Practices:
    • Conduct threat modeling and red teaming of AI components.
    • Monitor for anomalies, bias, and emerging vulnerabilities.
    • Update policies and models in response to new risks.

About This Project

This repository serves as a living document and best practices guide for engineering teams, compliance officers, and security professionals working on AI-native platforms.

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