Classic Security information and Event Management (SIEM) solutions, such as: IBM QRadar, Micro Focus ArcSight, Microsoft Sentinel, etc.

With the SAF platform's Search Anywhere™ feature, users can search not only within the SAF system's storage but also in external databases like Hadoop, databases, and Elasticsearch. If your infrastructure already collects the required information, SAF allows you to instantly search and analyze this data.

The SAF policy not only allows users to utilize a wide range of commands in the data processing pipeline engine, but also provides the ability to customize the search engine using plugins. SAF users can develop their own command that will execute the specific data processing logic they require.

SAF provides the ability to create a custom knowledge base. It consists of various knowledge objects such as scenarios, rules, articles, diagrams, and dashboards. Knowledge base allows for the integration of employees' intellectual contributions with the results of machine data analysis. It can utilize data received in SAF to update the knowledge base and generate dynamic reports. You can write documentation, job instructions, compile knowledge about connecting sources, and gather a database of correlation rules. And all of this can be done in SAF.

SAF allows you to collect data in almost any way. What cannot be obtained from the source can be collected by installing a SAF agent on the source. If agent collection is impossible, then agentless data collection should be used. It could be API, syslog, JDBC Input, ssh, etc.

Advantages of agent-based data collection in SAF
The Agent-based collection allows you to install a lightweight agent on the desired machine that will interact with SAF and receive commands, configurations for data collection. The Agent-based collection allows you to get data from a file on a specific computer or server, execute a script or request system information in order to send everything to SAF for further analytics.

Advantages of agentless data collection in SAF
Easy installation: Agentless data collection does not require additional software installation on target devices or servers. This simplifies the deployment process. Lower resource usage: Agentless data collection in SAF utilizes existing network infrastructure to obtain information.

Limited possibilities with external JDBC Driver. The Splunk JDBC Driver enables users to connect with live Splunk data, directly from any applications that support JDBC connectivity.

Traditional SIEMs are primarily designed to handle cyber security data sources like network logs, firewall logs, etc. They might not be optimized to handle non-cyber data, which could be valuable in a modern security context. If the SIEM doesn't support a particular data source out-of-the-box, integration often requires custom development, which can be time-consuming and may require specialized expertise.

Legacy SIEM systems may rely heavily on vendor-supplied plugins to collect data from different sources. If a plugin isn't available for a particular data source, collecting and analyzing data from that source can become complex.

The policy of SAF is to allow users to change everything that is possible to change. This can include the main color scheme of the system, fonts, and, of course, dashboards. Users can independently develop and modify panels on dashboards, change colors, arrange legends, and combine visualizations with each other. The user can achieve the desired appearance of the dashboard. The SAF policy also allows users to enhance the SAF engine with visualizations using an SDK and develop their own visualization plugin.

Splunk is known for its powerful visualization capabilities right out of the box, suitable for those who prefer immediate value without delving into customizations. While Splunk does offer customization options, it might not offer the same depth of tailoring as SAF, especially when it comes to altering core system aesthetics like color schemes and fonts.

Some advanced visualization plugins or integrations in Splunkbase might come with additional costs.

While Kibana offers visualization customization, it might not provide the same depth of system-wide aesthetic changes (like color schemes and fonts, using an SDK) as SAF.

Legacy SIEMs often lack the extensive visual customization features that platforms like SAF provide. They may not support wide-ranging changes to system aesthetics or dashboard design.

Unlike SAF's user-driven enhancement capabilities, legacy SIEM updates and enhancements are typically vendor-driven, potentially leading to longer wait times for specific features or improvements.

The SAF allows users to gather only the functionality they need. The modular structure allows modules to be added on the fly as needed, and the unified SAF ecosystem allows modules to communicate with each other, enriching useful data so that all necessary information is available to the user with a minimal number of mouse clicks.

Users can easily switch between modules and perform necessary tasks without the need to open separate programs or systems. Adding new modules or updating existing ones becomes much easier as they can be easily integrated with existing components.

A large community of developers and users means an abundance of shared knowledge, plugins, and solutions.

Splunk's ecosystem often involves components developed by various vendors. This can lead to integration challenges, especially when trying to ensure seamless communication between different tools or functionalities. Relying on specific vendors for certain functionalities within the Splunk ecosystem might lead to lock-in scenarios, reducing flexibility.

Due to the multi-vendor nature, automated data updates across different parts of the ecosystem might be less streamlined, potentially leading to more manual data inputs and a higher likelihood of errors.

ELK’s open-source nature means a broad community supports it, leading to regular updates, plugins, and solutions from developers worldwide.

The ELK components, while part of a unified stack, were developed as separate tools. Integrating other functionalities (beyond the core ELK capabilities) might pose challenges, especially if they are developed by different vendors.

Due to potential integration challenges with third-party tools, there might be less streamlined data updates across different parts of the ecosystem.

Classic SIEMs are specifically tailored for cybersecurity needs, offering refined tools and capabilities for threat detection and response.

While excellent for cybersecurity, classic SIEMs often lack the flexibility to venture into other areas like IT Ops or BI, limiting their versatility.Modern tool integration or the addition of non-security features might be challenging with classic SIEMs.

The SAF platform harnesses the potential of Root-Cause Analysis and Asset-Service-Models to provide a direct path to resource optimization and issue resolution.

The Big Picture Executive dashboard, fueled by data metrics and health indicators, offers a comprehensive view that guides resource allocation decisions with precision.

Rapidly identify bottlenecks, uncover problem origins, and facilitate targeted solutions.

By enabling rapid problem diagnosis, SAF ultimately reduces resource drain. Its role-based user access ensures relevant stakeholders have the insights they need.

Utilizing Asset-Service-Models tools within the SAF platform, which includes interconnected metrics and indicators, enables rapid identification and resolution of underlying issues. This approach facilitates a visual understanding of the causes of malfunctions and their consequences.

Splunk is renowned for its robust data analysis capabilities, processing vast amounts of data to deliver actionable insights.

Unlike SAF’s direct and structured approach to root-cause analysis, Splunk might require additional configurations or apps to achieve the same level of insight (like ITSI app).

Elasticsearch is renowned for its powerful data indexing and search capabilities.

While open source at its core, advanced features or large-scale implementations with ELK might involve additional costs.

ELK can provide a lot of insights, but may not directly offer the structured root-cause analysis in the way SAF does, potentially requiring additional configurations or tools.

The Cyber Security Module enriches the Security Analytics Platform with curated content. It encompasses ready-made correlation rules, incident detection mechanisms, response playbooks, configurations to integrate any security tool with SAF, and pre-installed dashboards, all delivered in the form of periodic content updates.

Curated Content. The Cyber Security module enriches the Security Analytics Platform with curated content. This includes ready-made correlation rules, incident detection mechanisms, response playbooks, configurations to integrate any security tool with SAF, and pre-installed dashboards, all delivered in the form of periodic content updates.

Ready-Made Correlation Rules. SAF Systems provides ready-made correlation rules, enabling quick and effective identification of potential threats and network anomalies.

Response Playbooks. SAF Systems offers response playbooks, streamlining and automating incident response processes.

Integration with Security Tools The module allows integration with any security tool within SAF, ensuring centralized security management and analysis.

Periodic Content Updates. The module receives periodic updates, ensuring the security system remains current and effective.

Splunk ES is a well-known player in the security information and event management (SIEM) space, trusted by many large organizations for its robust capabilities.

Splunk ES offers a flexible framework that supports various integrations, including threat intelligence feeds, helping organizations stay ahead of threats.

Splunk ES can become expensive, especially for larger organizations with massive data volumes.

Some users report that the initial setup and tuning of Splunk ES can be complex, requiring specialized expertise.

Writing complex correlation rules on DSL can be a challenge without a solid understanding of this language.

Unlike some dedicated SIEM solutions, ELK lacks a native correlation engine. This means that creating correlation rules often involves manual scripting, which can be time-consuming and error-prone.

While Elastic has been expanding its security offerings, traditional ELK might not offer as many out-of-the-box SIEM features as dedicated SIEM platforms.

Over the years, vendors have added a wide range of correlation rules to detect known threats. These can be beneficial for organizations that don't have the resources to define custom rules.

While legacy SIEMs offer numerous benefits, it's essential for organizations to evaluate their specific needs and challenges. In some cases, a newer solution might offer advantages in terms of usability, scalability, and integration capabilities.

When evaluating the cost-efficiency of various log management and analytics platforms, SAF emerges as a compelling choice. Based on the provided price comparison:

• SAF is the baseline at 100%.
• ELK costs 154% of the SAF price, translating to an additional 54%.
• Splunk is at a steep 283%, nearly three times the price of SAF.
• MS Sentinel (classic SIEM) tops the chart at 356%, making it over three and a half times costlier than SAF.

The clear advantage SAF offers in terms of pricing allows organizations to allocate resources more effectively and potentially invest in other critical areas of their infrastructure or security posture. When considering budget constraints and seeking an economical yet efficient solution, SAF offers notable cost benefits over its competitors like Splunk, ELK, and MS Sentinel (as a example of classic SIEM).