When attackers manipulate training data or trusted inputs, they can corrupt the very systems meant to defend against them. In this episode, we explore data poisoning—a type of vulnerability where attackers inject malicious or misleading data into machine learning models, behavioral analytics engines, or input streams used for automation. You’ll learn how this manipulation affects detection systems, recommendation engines, and even AI-based anomaly detection.We also discuss how data poisoning is relevant not just to future-facing systems but also to current-day logging, configuration management, and vulnerability scanning pipelines. Whether poisoning a firewall’s learning algorithm or corrupting a threat feed, attackers can use this subtle tactic to erode trust and effectiveness. This episode gives you a glimpse into emerging attack vectors while grounding you in the CySA+ exam’s expectations around integrity and data trust. Brought to you by BareMetalCyber.com

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