2.4. Security and Machine Learning Foundations

💡 First Principle: AI systems require the same security diligence as any enterprise system—plus additional considerations for data used in training, prompts, and outputs. Secure AI isn't a feature you add later; it's a principle you design around from the start. Organizations that treat AI security as an afterthought face data breaches, compliance violations, and loss of user trust.

What happens when AI security is neglected? Training data may contain sensitive information that gets exposed. Prompts may inadvertently send confidential data to external services. Outputs may reveal information users shouldn't access. Think of AI like a highly capable employee who sees everything they're given access to—and might accidentally mention confidential details in casual conversation if you don't set boundaries. The exam tests whether you understand these risks and can recommend appropriate security measures.

Consider the flow of information in an AI system: data goes in (training, prompts), processing happens (inference, generation), and output comes out (responses, content). Each stage has security implications. A secure AI strategy addresses all three—because a leak at any point compromises the whole system.