Case Study

Real-time Insider Threat Detection using Machine Learning

Insider threats are one of the biggest cybersecurity risks to banks today. These threats are increasingly becoming more frequent, more difficult to detect, and more complicated to prevent. A large US-based bank chose StreamAnalytix 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

The StreamAnalytix advantage

StreamAnalytix 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. Some solution highlights:

  • Ingestion and data processing from 5x more applications, at a fraction of the cost: Enabled ingestion of data from 80-90% of customer-facing and operational applications
  • Data transformation in real-time: In-memory data transformation allowed faster data quality scoring, data cleansing, and data enrichment
  • Use of machine learning models on log and complex event data: Enabled automated, continuous, and accurate anomaly detection
  • Custom alerts to curb fraud in real-time: Enabled appropriate real-time alerts and actions to prevent predicted breaches

Results

  • 5x expansion in scope
  • 10x cost reduction
  • 4x boost in performance
  • 10x faster application development and production
  • Enhanced threat detection accuracy and timeliness

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Detect and prevent insider threats with real-time data processing and machine learning

Insider threats are one of the most significant cybersecurity risks to banks today. These threats are becoming more frequent, more difficult to detect, and more complicated to prevent.

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