Presented by Securonix

There’s a lot of hype around applying machine learning, cognitive learning, and artificial intelligence to detect security threats in an environment. While analytics capabilities have advanced, there are still practical limitations in applying AI, such as:

  • Lack of data that results from the inability to deploy sensors and collect data, and the overall visibility gaps in an environment.
  • AI systems take time to learn and develop patterns, but who has the patience to wait weeks or months to train the bots?
  • AI systems lack business context.

The practical approach is to use a combination of unsupervised machine learning, supervised machine learning, and predictive analytics through AI, with human support throughout this process. It’s your people who understand your environment the best. Equip senior Security Analysts with the ability to train and teach the system, and it will in turn prepare the junior Analysts.

What you’ll walk away with:

  • Best practices for applying human intelligence to aid AI and ML.
  • Recommendations for incorporating business context into the detection, prioritization, and response to security events.