Security, Compliance, and Governance¶
Building AI applications requires careful attention to security, compliance, and governance. This guide covers essential practices and considerations for developing secure and compliant AI systems.
Overview¶
- Security: Best practices for securing AI applications and infrastructure
- Compliance: Regulatory requirements and industry standards
- Governance: Organizational structure and policy frameworks
- Monitoring: System observability and performance tracking
Core Components¶
Security¶
- Access control and authentication
- Data protection and privacy
- Model security and robustness
- Infrastructure security
Compliance¶
- Regulatory requirements
- Industry standards
- Documentation and reporting
- Audit trails
Governance¶
- Policy frameworks
- Decision-making processes
- Risk management
- Ethical considerations
Monitoring¶
- System metrics and observability
- Performance tracking
- Alerting and incident response
- Analytics and reporting
Human-in-the-Loop¶
Human oversight is essential, and often legally required, for important AI processes. This section covers approaches to incorporating human judgment and control in AI systems.
Tools and Frameworks¶
A Python and TypeScript toolkit enabling AI agents to communicate with humans in tool-based and asynchronous workflows. Incorporating humans-in-the-loop allows agentic tools to access more powerful capabilities while maintaining oversight.
Implementation Patterns¶
- Review Workflows: Processes for human review of AI outputs
- Intervention Points: Strategic points for human oversight
- Feedback Loops: Systems for incorporating human feedback
- Escalation Procedures: Clear paths for handling edge cases