Proofpoint Insider Threat Management vs Splunk User Behavior Analytics comparison

Proofpoint Insider Threat Management offers comprehensive insights into internal risks, emphasizing detection and response to insider threats effectively.

Proofpoint Insider Threat Management provides a robust platform for identifying, managing, and mitigating internal security threats within organizations. It leverages analytics to monitor user activities and detect anomalies that might signify potential threats. Users benefit from enhanced visibility into internal systems and processes, which strengthens their information security protocols. By focusing on insider threats, it helps organizations safeguard sensitive data and maintain compliance with industry regulations.

• User Activity Monitoring: Tracks user behaviors to identify abnormal activities.
• Data Loss Prevention: Ensures data integrity by preventing unauthorized data transfers.
• Advanced Analytics: Uses sophisticated algorithms to detect potential insider threats.
• Incident Response: Automates alerts and responses to detected threats, facilitating quick intervention.

• Improved Security: Enhances protection against unauthorized internal activities, reducing data breaches.
• Compliance Support: Assists in meeting industry regulations through detailed monitoring and reporting.
• Cost Efficiency: Helps organizations save costs on potential data leak incidents and breaches.
• Operational Efficiency: Streamlines threat detection and response processes, optimizing resources.

Many industries implement Proofpoint Insider Threat Management to protect sensitive information and comply with regulatory standards. It is particularly beneficial in sectors such as finance and healthcare, where data security is critical. By integrating it with their current infrastructure, organizations can effectively track and manage insider threats, ensuring data is handled securely and responsibly.

Splunk User Behavior Analytics is a behavior-based threat detection is based on machine learning methodologies that require no signatures or human analysis, enabling multi-entity behavior profiling and peer group analytics for users, devices, service accounts and applications. It detects insider threats and external attacks using out-of-the-box purpose-built that helps organizations find known, unknown and hidden threats, but extensible unsupervised machine learning (ML) algorithms, provides context around the threat via ML driven anomaly correlation and visual mapping of stitched anomalies over various phases of the attack lifecycle (Kill-Chain View). It uses a data science driven approach that produces actionable results with risk ratings and supporting evidence that increases SOC efficiency and supports bi-directional integration with Splunk Enterprise for data ingestion and correlation and with Splunk Enterprise Security for incident scoping, workflow management and automated response. The result is automated, accurate threat and anomaly detection.