SEC595: Applied Data Science and AI/Machine Learning for Cybersecurity Professionals

AI is a major topic of discussion today—and rightfully so. But for those of us in cyber security, it's crucial not only to understand how to use AI for security, but also to recognize the threats targeting AI models, their ecosystems, and how to defend and secure them effectively.
Organizations often analyze their competitors in detail—understanding pricing, strategy, and market positioning. But when it comes to cyber adversaries, this business-minded lens is rarely applied. This talk explores why understanding the economic and operational models of attackers—ransomware gangs, data brokers, hacktivists—can radically improve security decision-making. By framing threats in financial terms, from downtime impact to the going price of cracked credentials, we make risk real for non-technical stakeholders. And this insight isn't just important for CISOs. From the C-suite manager to the most technical operator, everyone benefits from speaking a shared economic language. Through practical examples and use cases, we’ll demonstrate how this mindset transforms risk discussions into something everyone—from boardroom to command line—can act on.
Registration: All students who register for a 4–6 day course will be eligible to play NetWars for free. Registration for this event will be through your SANS Account Dashboard the week of the event.
About DFIR NetWars: Focused on digital forensics, incident response, threat hunting, and malware analysis, this tool-agnostic approach covers everything from low-level artifacts to high-level behavioral observations.
Registration: All students who register for a 4–6 day course will be eligible to play NetWars for free. Registration for this event will be through your SANS Account Dashboard the week of the event.
About DFIR NetWars: Focused on digital forensics, incident response, threat hunting, and malware analysis, this tool-agnostic approach covers everything from low-level artifacts to high-level behavioral observations.