Securonix Next-Gen SIEM vs Splunk User Behavior Analytics comparison

Microsoft Sentinel is a scalable, cloud-native, security information event management (SIEM) and security orchestration automated response (SOAR) solution that lets you see and stop threats before they cause harm. Microsoft Sentinel delivers intelligent security analytics and threat intelligence across the enterprise, providing a single solution for alert detection, threat visibility, proactive hunting, and threat response. Eliminate security infrastructure setup and maintenance, and elastically scale to meet your security needs—while reducing IT costs. With Microsoft Sentinel, you can:

- Collect data at cloud scale—across all users, devices, applications, and infrastructure, both on-premises and in multiple clouds

- Detect previously uncovered threats and minimize false positives using analytics and unparalleled threat intelligence from Microsoft

- Investigate threats with AI and hunt suspicious activities at scale, tapping into decades of cybersecurity work at Microsoft

- Respond to incidents rapidly with built-in orchestration and automation of common tasks

To learn more about our solution, ask questions, and share feedback, join our Microsoft Security, Compliance and Identity Community.

Securonix Security Analytics SNYPR is a next-generation security analytics platform that transforms big data into actionable security intelligence, enabling you to take care of so much more than simply your SIEM (security information and event management) needs. In addition, it contains all of the tools that you may need to enable your organization to successfully handle both log management as well as UEBA (user and entity behavior analytics)-related tasks. The SNYPR management platform gives users the ability to combine security orchestration, automation, and response, security information and event management, network traffic analysis, and user and entity behavior analytics. This single technical environment does away with your need for multiple security, management, and analytics solutions.

Securonix Security Analytics SNYPR’s unified platform can be scaled up to handle up to one million security events every second. While this load may seem heavy, SNYPR handles it with ease. It is able to reduce incidents of false security positives by 60%. The access certification workload that IT administrators and managers need to deal with can be reduced by as much as 90%.

The model that this platform uses is based on a machine learning algorithm. This model gives Securonix Security Analytics’s SNYPR platform a number of extremely valuable capabilities. The platform gathers many different types of data and applies what it learns to threats as they arise. The system assigns threats risk values to determine where the areas of highest need are. Machine learning also allows you to respond to slow acting threats by using historical data to inform your response.

All of the data that the system gathers is stitched together and used to create a complete picture of the risks that the system faces. Any blind spots that may exist are exposed by the collaborative UI that compiles the system data in a single location. This also increases your ability to monitor advanced application threats. 

Key Features

Some of Securonix Security Analytics’s SNYPR platform’s key features include:

• The ability to enrich all data that the SNYPR platform collects. When SNYPR gathers information, it applies relevant data which can be used in the future to gauge whether or not a particular event is a threat.
• The ability for data redundancy to automatically take place. All of the data that is gathered, analyzed, and processed by SNYPR is automatically copied and distributed across the system. If there is a failure in any particular part of the system, the information will still be preserved.
• The ability to track historical issues and use that information to help deal with current threats. The SPOTTER feature allows analysts to look back at both old data and the contextual information that is attached to it. They can then use that data to inform their responses to similar threats that they are currently dealing with.

Reviews from Real Users

Securonix Security Analytics SNYPR platform stands out among its competitors for a number of reasons. Two major ones are its ability to significantly reduce the number of false positives that administrators have to deal with and the way that it incorporates contextual information into security events to reduce the time spent finding solutions to problems that arise.

Peerspot users note the effectiveness of these features. One user wrote, “Securonix’s analytics-driven approach for helping to find sophisticated threats and reduce false positives is pretty good. We are allowed to fine-tune according to our requirements and our clients' requirements, which does reduce false positives. In the last 24 hours, the total number of policies with triggers was 233. When I started with this product, the false positives were 561. Therefore, the solution has helped by tuning or reducing false positives.”

Another user noted, “The way that a Securonix is able to put a lot of the contextual information into the events is very helpful. That has reduced the amount of time required for investigating, ‘Hey, this might be something I need to look at,’ and then doing further research. It puts all of those violations in one event or case, so that you can look at different types of violations that all correlate. That has reduced the amount of time for researching some of those cases. It's dependent upon the scenario, but in some cases it could save an hour of going out and doing a bunch of individual searches.”

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.