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Uncovering Fraud Using Security Analytics Approach
CHALLENGE: Internal control systems are not effective in detecting fraud
In order to effectively detect fraud, enterprises should be able to analyze massive volumes of transactions across disparate applications and systems over a period of time. This enables you to baseline what normal activities look like and detect deviations in behavior. Traditional systems using static rules do not have the capabilities to learn from your data and hence can end up generating a lot of positives.
SOLUTION: Support ability to ingest and analyze historical data
Securonix uses patented machine learning techniques that analyze data in real-time to profile your transactions, identify normal and detect outliers. The solution supports ability to ingest and analyze historical data so that it learns from what you already have and starts detecting outliers immediately upon deployment.
Purpose-Built Analytics for rapid, consistent and quality analysis across key sources
Big Data Scale to support real-time data mining and threat detection against large data feeds
Automated Correlation and Enrichment of identity and threat information across multiple internal and external sources
Peer Group Analysis of users’ behavior and access against their peers for automated outlier anomaly detection
Behavior Analysis of users, peer groups, accounts, and systems for signature-less detection of insider threats
Application & Data Risk Visibility for monitoring insider threats at the targets
Advanced Scoring & Visualization for effective, efficient, continuous reporting of insider risk and threat levels
BENEFITS: Proactive not reactive
Securonix gives organizations visibility into the highest risk activities in their environment and the tools to monitor, manage, report and investigate them.