Episode 33 — AI Security Primer: Threats and Defenses
This episode introduces the security challenges unique to artificial intelligence systems. Unlike traditional software, AI models can be attacked through their training data, architecture, or outputs. Threats include data poisoning, where adversaries manipulate inputs to corrupt models; evasion, where attackers craft adversarial examples to fool predictions; and model theft, where proprietary models are extracted or copied. For certification exams, learners should be able to identify these categories of threats and understand basic defense strategies.
We then examine countermeasures. Defenses include securing data pipelines, applying adversarial training to harden models, and monitoring predictions for anomalies. For example, image classifiers can be protected against pixel-level manipulations by testing robustness across varied conditions. Intellectual property concerns can be mitigated with watermarking or controlled API access. Troubleshooting involves recognizing when a system’s failure stems from adversarial interference rather than ordinary error. Best practices stress defense-in-depth, where multiple layers of safeguards reduce overall exposure. Exam scenarios may describe suspicious model behavior and ask which attack is most likely, or which defense best mitigates the risk. By grounding AI in strong security practices, learners prepare to design systems resilient to adversaries. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.
