From the course: AI Product Security: Secure Architecture, Deployment, and Infrastructure

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Security testing

Security testing

From the course: AI Product Security: Secure Architecture, Deployment, and Infrastructure

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Security testing

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- [Instructor] Security testing ensures that your AI systems are resilient against both adversarial and traditional attacks, from data poisoning to API misuse. Without proper testing, vulnerabilities can go unnoticed, leading to catastrophic failures in production. To implement this, in practice, conduct static analysis of your code. Use tools to scan your AI-specific code for vulnerabilities such as insecure data handling or hardcoded secrets. Catching issues early ensures they don't escalate into runtime problems. Test for adversarial robustness, simulate attacks using adversarial examples such as subtly-altered inputs designed to confuse your model. For example, test in an image recognition model with altered images to ensure its accuracy under manipulation. Perform dynamic testing, or DAST. Test deployed environments, including APIs for vulnerabilities like injection attacks or weak authentication. Simulate unauthorized API calls to validate that only authenticated users or…

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