Detect & Stop Insider Threats with Behavior Analytics
Insider Threat Whitepaper
It takes a combination of the right data sources, sophisticated machine learning and perceptive data science to pinpoint truly aberrant actions that are good indicators of insider sabotage, data theft or misuse.
Gurucul predicts, detects, and remediates the insider threat in real-time by combining:
User and Entity Behavior Analytics (UEBA): Unearth threats that bypass traditional detection and prevention systems with supervised, unsupervised, and semi-supervised ML algorithms tuned to detect insider threats. Leverage advanced techniques like deep-learning and text mining to perform sentiment analysis.
Identity Analytics: Add data and analysis of a user’s identities and entitlements, and put that information in context with the user’s peer groups, to vastly improve the process of pinpointing malicious insider activity.
The Self-Audit: Empower users to proactively monitor and report any suspicious access, fraud and activity on their own accounts.
For more information, read our whitepaper, “Uncover Insider Threats through Predictive Security Analytics.”
“ We can stop someone who is about to be terminated from coming in after hours and doing anomalous activity like downloading a large quantity of files or printing copious documents. Gurucul helps us be proactive in finding malicious insiders. ”
– Director of Identity and Access Management, Financial Services Firm
Gurucul is changing the way enterprises protect themselves against insider threats, fraud, account compromise and data exfiltration in both on-premises and cloud environments. The company’s Behavior Based Security Analytics and Intelligence platform uses machine learning and predictive anomaly detection algorithms to reduce the attack surface for accounts, and to eliminate unnecessary access rights and privileges. Identify, predict and prevent breaches with Gurucul Security Analytics.