Gartner Research

Assessing the Impact of Machine Learning on Security

Published: 06 May 2019

Summary

Security and risk management technical professionals are flooded with artificial intelligence and machine learning marketing from vendors. This research explores real-world examples to assess the effectiveness of AI and ML approaches in improving security posture.

Included in Full Research

  • Overview of Machine Learning
  • Drivers for Machine Learning Adoption in Security
  • Use Cases, Data Sources and Methods
    • Use Cases
    • Data Sources
    • Methods
  • Real Machine Learning Use Cases in Security
  • Contending With Relentless Marketing Hype
    • Confusing Messaging With Significant True Elements
    • Somewhat Misleading, Confusing and Minimally Accurate Claims
    • Intentionally Dishonest Framing and Vast Exaggerations
    • Proven Machine Learning Techniques That You Can Leverage for Security Today
  • Strengths
  • Weaknesses
  • Cut Through the Hype
  • Stay Abreast of Emerging Threat Vectors
  • Employ a Use-Case-Based Approach to Selecting Tools
  • Start With Desired Outcomes
  • Test ML-Based Products, and Conduct POCs
    • Test the Use Cases on Production Data
    • Prepare for a Long POC
    • Assess Alert Volume and False-Positive Rates
    • Get a Feel for the User Experience
    • Evaluate Triage and Decision Support Features
    • Avoid the Distraction of “POC Gems”
  • Supervised Machine Learning Model
  • Unsupervised Machine Learning Model
  • Semisupervised Machine Learning Model
  • The Ensemble Approach

Analysts:

Augusto Barros,  Anton Chuvakin,  Anna Belak

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