Fraud Prevention

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 learn from what you already have and start 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.

→  Predictive threat detection

→  Automated real-time analytics enabling in-line preventative actions

→  Ability to analyze massive volumes of data including historical transactions

→  Compliance reporting and dashboards

→  Case management capabilities

→  Out-of-box use cases depending on the type of data set

Solution Tour

  • User Risk &Threat Monitoring

  • High Privileged Account (HPA) Monitoring

  • Application & Data Risk Monitoring

  • Advanced Enterprise Fraud Detection

Securonix continuously builds a comprehensive risk profile of a user based on identity/employment, security violations, IT activity and access, physical access, and even phone records. All identity, activity, and access characteristics are compared to their baseline, their peers, and known threat indicators to identify true areas of risk. All results are scored and presented in interactive scorecards.

HPAs are a primary source of insider misuse and a platform for their attacks. Securonix automatically identifies HPAs such as administrator, service, and shared accounts then monitors them for abnormal behavior associated with an attack while linking the high-risk behavior back to a real user and their risk profile to give the potential threat full context.

Insiders attack sensitive data, transactions, or the systems that host them. Securonix addresses this threat by monitoring critical applications and systems at the transaction, data set, and sensitive user record level. Similar to a user, Securonix continuously builds a risk profile for all applications and systems identifying all high-risk users, access, and activity associated with their sensitive data and transactions. All results are scored and presented in application risk scorecards.

Insider fraud is typically conducted over a long period of time or through complex activity designed to get around the known threat or “signature-based” detection methods. Securonix addresses this blind spot with advanced “signature-less” behavior and peer based outlier analysis techniques that are highly effective at identifying “slow and low” and complex fraud attacks.