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User Behavioural Analytics
User Behavioral Analytics
Our User Behaviour Analytics (UBA) module serves as an additional layer to enhance your security measures by providing comprehensive visibility, identifying compromised accounts, and detecting any abnormal or malicious activity carried out by insiders, thereby reducing the risk of potential threats.
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Anomaly Detection:
UBA uses machine learning algorithms to identify anomalous user behavior on a network, and flag suspicious activity that may indicate a potential security threat.
Threat Intelligence Integration:
UBA can integrate with external threat intelligence feeds, to provide additional context and enhance the accuracy of its threat detection capabilities.
User Profiling:
UBA creates profiles for each user on a network, and uses this information to identify deviations from normal behavior that may indicate a security threat.
Risk Scoring:
UBA assigns a risk score to each user based on their behavior, to prioritize potential security threats and facilitate rapid response.
Insider Threat Detection:
UBA can identify potential insider threats by analyzing patterns of user behavior, and flagging any activity that is inconsistent with an employee's role or responsibilities.
Automated Response:
UBA can automatically respond to security threats, by isolating compromised accounts, blocking access to sensitive data, or alerting security teams to take action.
User Behavioral Analytics
By continuously monitoring and analyzing user behavior, User Behavioral Analytics (UBA) can swiftly detect and mitigate security threats and vulnerabilities around the clock. This is achieved through real-time log analysis and security orchestration, automation, and response tools, which aid in investigations, threat hunting, and response actions. The integration of threat intelligence with human expertise enables advanced analytics and contextualized event analysis, providing a more comprehensive approach to cybersecurity.
Overview
User Behavioral Analytics (UBA) is a cybersecurity technology that uses machine learning algorithms to monitor and analyze patterns of user behavior on a network, and detect potential security threats in real-time. UBA creates profiles for each user, and identifies any anomalous or suspicious behavior that may indicate a security threat, such as insider threats, account compromises, or data exfiltration attempts. UBA solutions typically integrate with other security technologies and threat intelligence feeds, to enhance their detection capabilities and provide additional context. By leveraging machine learning and artificial intelligence, UBA provides a powerful tool for enhancing an organization’s security posture, by enabling rapid detection and response to potential security threats.
The Challenge
he problem that User Behavioral Analytics (UBA) aims to solve is the difficulty of detecting and responding to potential security threats in a timely and effective manner. Traditional security solutions often rely on static rules and signatures to identify threats, which can be easily bypassed by sophisticated attackers. Additionally, these solutions often generate a high volume of false positives, making it difficult for security teams to distinguish real threats from noise.
The Solution
The solution that UBA provides is the use of machine learning algorithms to analyze patterns of user behavior on a network, and detect potential security threats in real-time. By continuously monitoring user activity and creating profiles for each user, UBA can quickly identify any anomalous or suspicious behavior that may indicate a security threat. UBA also integrates with other security technologies and threat intelligence feeds, to enhance its threat detection capabilities and provide additional context. By leveraging machine learning and artificial intelligence, UBA provides a more comprehensive and effective approach to cybersecurity, enabling organizations to rapidly detect and respond to potential threats before they can cause significant harm.
Key Features
Instantaneous Threat Identification
- Continuous monitoring of all attack
- Utilize abnormality detection system
Wide Range of Detection Assessment
- Monitor Misconfigurations
- Trace risky applications that can facilitate or conceal a cyber-attack
Intelligent automation and swift reaction
- Centralized automation support
- Enhanced precision
- Reduced recovery time for remediation
Data-driven detection
- Machine learning and behavior analysis
- Expose malicious activity features
- Signatureless
Real-time attack detection
- Match a threat certainty score
- Auto-aggregate all detections and confidence scores
- Identify high-risk hosts
Ranking critical assets
- Obtain a graphical overview of threats
- Establish a complete threat context
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