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Analyzing the Black Box: A Crash Course in AI-Native Cybersecurity

Uncover the mechanisms behind AI-powered cybersecurity in Chapter 9 of our Convergence of AI + Cybersecurity web series.

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Ponentes

Dr. Dan Shiebler

Head of Machine Learning · Abnormal AI

Dr. Rumman Chowdhury

Responsible AI Fellow · Harvard University

Información clave

AI-driven cybersecurity models carry risks of opacity, requiring strategies for transparency and accountability.

A hybrid security model combining AI automation with human oversight is a recommended approach for effective defense.

Scaling AI security solutions requires specific considerations to future-proof the technology as threats evolve.

As AI becomes increasingly integral to cybersecurity, understanding its intricacies is more critical than ever.

In Chapter 9 of The Convergence of AI + Cybersecurity series, Abnormal’s Head of Machine Learning, Dan Shiebler, was joined by AI expert Dr. Rumman Chowdhury to demystify the so-called "black box" of AI-driven cybersecurity.

In this insightful webinar, our experts in AI ethics and security explored how AI algorithms make decisions, the potential risks of opaque AI models, and strategies for ensuring transparency and accountability in AI-powered cybersecurity solutions.

Watch this on-demand webinar to learn:

  • Essential concepts and tools needed to navigate the complexities of AI
  • How to build a hybrid security model using AI automation and human oversight
  • Key considerations for scaling and future-proofing AI solutions
  • And more!

Fill out the form to view the webinar.

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