How to Build and Secure AI Systems for Production (Step-by-Step)
Deploying machine learning models in corporate environments introduces an entirely new set of security challenges that traditional software frameworks don't account for. This deep dive breaks down how to architect resilient systems that maintain their integrity under pressure. We look closely at everything from pipeline construction to runtime monitoring, ensuring your engineering team can identify risks before they hit production.
You will walk away with a clear blueprint for implementing data sanitization, verifying third-party weights, and setting up robust access controls. We also look at practical defensive strategies, like input rate-limiting and behavior analytics, to catch exploitation attempts early. Whether you are a security analyst or a machine learning engineer, this walkthrough bridges the gap between theoretical AI safety and hard-nosed engineering reality.
What You Will Learn
00:00 - Introduction to Secure AI Infrastructure
02:15 - Threat Modeling: How to Build and Secure AI Systems for Production
05:40 - Securing the Pipeline against AI Data Poisoning
09:15 - Defending LLMs against Prompt Injection Attacks
14:30 - Implementing Adversarial Robustness Testing in CI/CD
19:00 - Monitoring and Logging Machine Learning Vulnerabilities at Scale
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