Summary
In this module, you learned about the essential security controls that should be implemented when building and operating AI systems. You explored controls across the full AI application lifecycle:
- Supply chain security: How to evaluate open-source AI libraries for security risks, including AI-specific concerns like model provenance and serialization vulnerabilities
- Content filtering: How input and output filters detect and block harmful content, prompt injection attempts, and policy violations
- Data security: How agent identity management and access controls ensure AI systems only access data the user is authorized to see
- Metaprompts: How well-designed system prompts serve as a behavioral security control, establishing ground rules that mitigate jailbreaks and manipulation
- Grounding: How connecting AI responses to verified data reduces fabricated outputs and constrains the model's scope
- Application security: How traditional security best practices extend to AI-specific components, including agent tool security and secure development lifecycle practices
- Monitoring and detection: How AI-specific monitoring detects attacks in progress by analyzing interaction content and agent behavior patterns
No single security control is 100% effective. Implement layers of controls to achieve a defense-in-depth approach to AI security. And remember that traditional security controls remain essential—they protect the infrastructure that supports your AI systems.
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