Detect Threat and
Fraud in Custom
Rules and Statistics-Based Detection Don’t Work
Enterprise organizations run several custom-built applications to uniquely serve customers and employees. Each application also has custom business logic embedded within, which changes frequently based on customer requirements. As a result, existing rule-based detection solutions haven’t been able to keep up.
The two primary challenges in detecting rogue insiders within custom applications are:
- Business logic differs per application, requiring separate rule maintenance.
- Rule-based detection techniques require extensive maintenance to evolve with business logic changes.
AI, Supervised ML, and Threat Hunting are Costly and Inadequate
Existing methods to detect fraud and threats in custom enterprise applications are ineffective and require costly resources. Some teams employ AI and supervised ML to detect suspicious transactions, but applying these techniques to frequently changing business logic is impractical. Threat Hunting at the application layer requires too much familiarity with business logic to adequately staff and is unlikely to provide results.
This is where RevealSecurity's TrackerIQ comes in.
User Journey Analytics Applied
to Any Custom Application
RevealSecurity’s TrackerIQ continuously learns sequences of activities performed by every employee in custom applications. As business logic changes, user journeys evolve to keep up, so TrackerIQ continues to monitor sequences of activities across multiple journeys to create a set of ‘normative’ user journey profiles.
As it learns about all application employee profiles, TrackerIQ understands potential outliers within a user journey and tags them as anomalies. Custom applications often mandate custom transaction and event logging scenarios. TrackerIQ handles custom event logging and tagging within its intuitive configuration interface. It also supports a wide variety of application logging repositories like Splunk and Kafka streams.