Change Tracker Gen7 R2 vs Splunk User Behavior Analytics comparison

Change Tracker Gen7R2 solves IT Security and the problems that plague all organizations – the overwhelming noise of change control and ensuring the integrity of IT systems. Completely redesigned with both security and IT operations in mind, Change Tracker Gen7 R2 is the only solution designed to reduce change noise and the complexity of integrity monitoring and policy management all while allowing for unprecedented scalability and management that meets the most demanding enterprise environments.

Gen7R2 enables organizations to:

- Define the systems that need protection

- Ensure those systems are secured, compliant and fit for purpose at all times

- Provide intelligent change control to ensure systems remain in a ‘known secure and compliant state’

- Enable organizations to move projects securely from Development to Operations

Gen7 R2 integrates with leading Service desks and Change Management solutions to reconcile the changes that are actually occurring within your environment with those that were expected and part of an approved Request for Change. Security and IT Service Management (ITSM) have traditionally observed and managed change in two very different ways. By linking the changes approved and expected within the ITSM world with those that are actually happening from a security perspective, SecureOps is delivered and underpins effective, ongoing security and operational availability.

With Gen7R2 you have the ability to reduce change noise by more than 90%, leaving only changes that are unknown, unwanted, unexpected or potentially malicious in nature for further investigation.

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.