The emergence of AI tools in cybersecurity creates many opportunities and uncertainties. A focus group with advanced graduate students in cybersecurity revealed the potential depth and breadth of the challenges and opportunities. The salient issues are access to open source or free tools, documentation, curricular diversity, and clear articulation of ethical principles for AI cybersecurity education. Confronting the "black box" mentality in AI cybersecurity work is also of the greatest importance, doubled by deeper and prior education in foundational AI work. Systems thinking and effective communication were considered relevant areas of educational improvement. Future AI educators and practitioners need to address these issues by implementing rigorous technical training curricula, clear documentation, and frameworks for ethically monitoring AI combined with critical and system's thinking and communication skills.

It seems like just about every security vendor touts some kind of AI or machine learning embedded within their products. But do you really need this technology to have an effective defense? The answer lies in looking at modern methods of attack and seeing whether legacy methods of detection can be successful. In all of these modern-day attacks, the legacy methods of detection -- especially those dependent on historical information, such as signature-based detection and heuristics -- simply won't get the job done. The beauty of machine learning for cybersecurity is right there in the name: It's always learning.