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.
- 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
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.
- 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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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.