SIEM platforms collect and correlate security events, logs, and network data for analysis and operations. However, SIEMs are designed to collect data and normalize that data. They are excellent for detecting alerts but struggle when organizations deal with multi-pronged attacks using varying tactics. The raw data produced by queries can make it difficult to understand the scope of a threat. This can often lead to overlooked data and bad decisions. SIEMs are inflexible, and often overly complicated to use.
Many companies use their SIEM platform for regulatory compliance and monitoring. For more advanced management, look toward solutions that simplify cyber security analytics response processes and provide deeper insight. Implementing a cyber security analytics platform can help companies investigate, manage, and automate processes for faster, more targeted responses to threats.
Best Ways to Investigate Cyber Security AnalyticsSIEM systems detect alerts and prioritize responses, but they do not provide the in-depth investigative tools that a true cyber security analytics platform. Proper investigation requires a centralized orchestration system that is able to help analysts quickly identify and respond to multidimensional attacks. There are a number of problems with SIEM platforms that can inhibit a team’s ability to respond.
- SIEM platforms often require special knowledge to operate. Waiting for a person with the right skillset can delay response times.
- SIEM platforms are known for generating false positive alerts. A more advanced platform would be able to more accurately identify real threats and almost eliminate false positives.
- Most SIEM platforms do not offer customization. this means that organizations are forced to adapt their processes specifically to their platform or fund new development.
- Cyber security analytics platforms cast a wider net for data. Multidimensional attacks are likely to include multiple systems, networks, and devices. Security teams must be able to access, sort, and understand data from a wide variety of sources.
- Cyber security platforms offer huge upgrades in usability. This means less time fighting with your SIEM platform and more time responding to threats.
More advanced analytics platforms provide a more visual experience. This helps techs identify why the system has flagged specific threats and more accurately take steps to respond to multidimensional threats. A single image is sometimes able to convey information from thousands of logs that went into identifying a threat.
The Trend Towards Building Cyber Security OntologyNew systems can utilize cyber security ontology to better understand and classify events. Cyber security ontology refers to systems that attempt to provide context between data (events, incidents, etc.) and their entities (IP, user, removable devices) and their relationships. This context occurs across the entire network, rather than being limited to a singular system.
How to Manage Cyber Security AnalyticsSIEM platforms can be difficult to navigate. Modern cyber security analytics platforms ensure that analysts won’t have to navigate between a half dozen consoles, sift through hundreds of log rows, or manually run queries that take forever to return data. While SIEM platforms are necessary for compliance, having an additional layer of analysis tools on top of your SIEM will allow you to improve your ability to manage, free up resources to focus on threats, and provide a higher level of protection to the organization as a whole. The use of cyber security ontology technology allows systems to scale with corporate need. With so much data requiring management, staying agile is critical. Modern companies require systems that will scale up and down as needed.
Automation within Cyber Security AnalyticsAnother benefit of using a cyber security analytics platform is the fact that it will eliminate a lot of mundane, repetitive tasks that analysts typically have to perform themselves inside of a SIEM platform. Having standardized workflows helps to drive efficiency. Additionally, modern cyber security analytics platforms include integrations with popular platforms and software to simplify those workflows.
Analytics platforms use machine learning algorithms to facilitate better identification and classification of threats. This means that teams will have fewer false positives and a better understanding of high-risk threats as they happen. Integrating tools will help you to automate response processes and reduce threat response times across the board.
Cyber security ontology is the next step in cyber security automation. The ability to provide context to seemingly disconnected events across the entire network without forcing analysts to dig through logs is critical to fast, effective responses.