When it comes to protecting a network from fraud, organizations need to keep a watchful eye on a wide range of activities that are frequently difficult to detect. Acts of fraud frequently involve a series of legitimate activities that individually do not warrant notice. However when they are observed in the right sequence over time, pattern recognition can detect that suspicious activity is taking place.
Compounding the problem is the fact that many organizations fail to maintain a usable digital paper trail or lack the pattern recognition, visualization and anomaly detection capabilities to conduct accurate and quick forensic analysis on user behavior. Performing investigations involves manually looking at audit records and other log data after the fact and real-time detection is frequently nonexistent.
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LogRhythm provides organizations with automated log collection and analysis with advanced correlation and pattern recognition to help detect and prevent fraudulent activity.