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Data Analytics for Digital Security

Technological innovations in the fields of machine learning, artificial intelligence and automation in cyber-security have all contributed to the fervent increase of the security analytics market. This elusive market, however, is currently hidden amidst a turbulent and ever-changing digital security ecosystem. Although it is an essential link in the intelligence value chain and a crucial component on the fight against cyber-attackers it also represents a quite misunderstood piece of the market with obscure borders and details ranging from User and Entity Behavioral Analytics (UEBA) to SIEM (Security Information and Event Management) and Intrusion Detection System/Intrusion Prevention System (IDS/IPS).

This report delves into the security analytics market analyzing it from its most basic components and source of intelligence all the way to macro-scale and demystifying its technological collision with Big Data and cloud computing. The report includes insights gathered from AI and machine learning powerhouses like IBM and Cisco,  leading security product vendors like LogRhythm, McAfee, and Crowdstrike as well as agile and quite innovative vendors like  DarkTrace, empow and Exeon Analytics. The security analytics market is also further investigated and segmented according to an endpoint, network, cloud, and on-premises perspective and from an end-market perspective covering commercial and enterprise, banking and finance, healthcare, government, and public sector.

 

Table of Contents

  • 1. EXECUTIVE SUMMARY
  • 2. ADVANCED ANALYTICS IN THE DIGITAL SECURITY DOMAIN
    • 2.1. Demystifying Analytics in Digital Security Endeavors
    • 2.2. Sources of Intelligence and Implementing Third-Party Options
    • 2.3. Deep Learning and Anomaly Detection
    • 2.4. Market Sizing for Digital Security Analytics
  • 3. STRATEGIC RECOMMENDATIONS AND INSIGHTS
    • 3.1. Prerequisites for "Dependable Intelligence"
    • 3.2. The Misrepresentation of Security Analytics in Cyber-Forensics
    • 3.3. Application-Specific versus a Multi-Facited Approach
    • 3.4. The Big Picture versus Finding the Source
    • 3.5. Demystifying Big Data in Security Analytics
  • 4. VENDOR PROFILES
    • 4.1. IBM
    • 4.2. LogRhythm
    • 4.3. Cisco
    • 4.4. CrowdStrike
    • 4.5. McAfee
    • 4.6. Darktrace
    • 4.7. Exeon Analytics
    • 4.8. empow
    • 4.9. Dojo