Customer Story

Real-time insider threat detection solution for a fortune 500 bank

Learn how a large US-based bank used predictive analytics and machine learning to identify and prevent insider information security threats across sensitive applications in its retail banking and wealth management divisions.

Challenges

  • Simple rule-based alerts proved inadequate for accurate and timely threat detection
  • An expensive and inflexible technology stack limited threat detection to only a few applications, exposing the bank to vulnerabilities
  • The existing solution was taking too long to develop and move use cases into production

Solution

Gathr enabled the use of predictive analytics and machine learning on a large data set from highly sensitive applications to automatically detect previously unknown threat scenarios and raise appropriate alerts to prevent predicted breaches.

Highlights

  • Ingestion and data processing from 5x more applications at a fraction of the cost
  • Data transformation in real-time
  • Use of machine learning models on the log and complex event data
  • Custom alerts to curb fraud in real-time

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