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
Meet Gathr.
The only all-in-one data pipeline platform
- One platform to do it all - ETL, ELT, ingestion, CDC, ML
- Self Service, zero-code, drag and drop interface
- Built-in DataOps, MLOps, and DevOps tools
- Cloud-agnostic and interoperable
Data
Ingestion-
Change Data
Capture -
ETL/ELT Data
Integration -
Streaming
Analytics -
Data
Preparation -
Machine
Learning