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.
- 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
- 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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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.