Employing machine learning in a security environment - A data science-driven approach

No matter where you look in the security world today, you’ll see the terms machine learning and artificial intelligence (AI). There’s been a great deal of interest in machine learning and AI as security vendors and their customers look for better ways to improve their security posture and fight against advancing cyberattacks. Machine learning and AI offer breakthroughs in solving problems in many other areas of our lives, so it’s only natural to try to use them to make similar breakthroughs in the field of security.

Unfortunately, there’s a lot of hype and misinformation surrounding what machine learning and AI can do to improve security. In this paper, you will discover the most critical things you need to know about applying machine learning and AI in your security environment. You will also learn to recognise the most significant opportunities and challenges for using machine learning and AI to improve your security team’s ability to swiftly detect and respond to cyberthreats.

The evolving need for machine learning, AI, and data science

Machine learning, artificial intelligence, and data science are terms with shifting definitions. For the purposes of this paper, we’ve defined the following terms:

  • Artificial Intelligence (AI): The science of enabling a computer to automate something a human would normally do that requires intelligence, analysis, and decision making.
  • Machine Learning: The science of enabling computers to learn without being explicitly programmed to do so. Machine learning applies statistics and algorithms at scale on large amounts of data. One of the goals for machine learning is to achieve artificial intelligence.
  • Data Science: The discipline of extracting information from data. Data science is a broad field that includes machine learning.


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