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.
Ensure successful data ingestion on the cloud: Strategies for 2021
Dec 04, 2020 | 10:00 am PT / 1:00 pm ET