Employing Machine Learning in a Security Environment

Recognizing Significant Opportunities and Challenges for Applying Machine Learning and AI in Your Environment

Recently, the terms “machine learning” (ML) and “artificial intelligence” (AI) have proliferated the security space. While there is a great deal of potential as to how these technologies can improve your security posture, there is also a lot of hype and misinformation surrounding what machine learning and AI can do today to improve security.

In this white paper, you will discover the most critical things you need to know about applying ML and AI in your security environment. You will also learn to recognize the most significant opportunities and challenges for using ML and AI to improve your team’s ability to swiftly detect and respond to cyberthreats.

You’ll learn:

  • Why the need for ML, AI, and data science is evolving in the security space

  • The difference between supervised and unsupervised learning

  • The hype and the reality

  • How machine learning can improve your security operations

  • UEBA-specific use cases for machine learning

  • Potential pitfalls

Forrester estimated in a recent report that investments in AI-based technologies, which are driven by machine learning, would triple from 2016 to 2017.