Today’s attacks compromise identity as a primary vector to gain sensitive information from an organization for financial gain or social notoriety. These attacks are sophisticated, better funded, more organized than ever before making it imperative for organizations to immediately analyze potential threats and risks related to anomalous and suspicious behavior. So, what can organizations do? You can consider investing in user behavior analytics for cyber security.
As the name implies, user and entity behavior analytics helps businesses detect targeted attacks, financial fraud, and insider threats. It is generally used to analyze employees as they may have intentions to steal sensitive information of your business.
How is this possible? The solution uses highly advanced mathematical algorithms and big data analytics for the early detection of potential threats. The solution determines the difference between suspicious and normal behavior of an employee by comparing their behavior against dynamically created peer groups. Additionally, the solution prioritizes and categorizes identified risks for further investigation by sending alerts to the responders to resolve the situation.
The solution can be used alongside traditional defense-in-depth solutions to potentially identify a wide range of attacks, threats, or risks which might affect your business. Gurucul provides a Hybrid Behavior Analytics (HBA) architecture with the breadth of Identity Access Intelligence to User Behavior Analytics, and the depth from cloud apps to on-premises behavior.
Provides user profiling and behavior anomaly detection based on dynamic peer groups with machine learning rather than static groups and rule sets
- Predict, detect and deter insider threats, advanced threats and cyber fraud
- Protect intellectual property and predict and detect data exfiltration
- Detect high privilege account abuse, account hijacking and anomalous activity
- Prevent ID theft through risk-scored event time lines and end user self-audits
- Leverages dynamic peer groups for improved clustering machine learning and more accurate outlier analytics
- Detect malicious and abusive behavior that may otherwise goes unnoticed by traditional security monitoring systems
- Enhance SIEM and security analytics intelligence, plus improve DLP intelligence with risk-scored alerts based on behavior analytics
- Optimize security resources and time with self learning and training machine learning algorithms, UBA is a force multiplier for IT security teams, the data science for data volume
- Increase identity misuse and compromise detection and deterrence with self audits to third party contractors, partners, suppliers, buyers and consultants
- Hybrid architecture for on-premises and cloud apps
- Data Exfiltration
- IP Data Protection
- Cyber Fraud Detection
- Insider Threat Detection & Deterrence
- High Privilege Account Abuse
- Identity and Risk Analytics
- Account Hijacking
- Anomalous Behavior
- Threat Detection
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