SIEM, Security Information and Event Management, has been the gold standard in cybersecurity for decades. However, technology has not delivered on its promises of better security and real-time threat detection. Even though there are massive numbers of players in this space, the technology is not performing.
Unfortunately, the functional limitations of SIEM are the same today as they were 15 years ago. Log aggregation is not an effective solution to handle the modern cybersecurity landscape.
What, exactly, is SIEM, and why is it failing the cybersecurity industry? Much of it has to do with data volume. A large enterprise can generate upwards of 25,000 events per second and require more than 50 TB of data storage.
A Basic Definition of SIEM
According to Gartner, SIEM is a technology that supports threat detection, compliance, and security incident management through the collection and analysis of security events. SIEM encompasses a wide range of event and contextual data sources and acts in near real-time and historically.
At its core, SIEM provides wide-ranging log event collection and management along with the ability to analyze log events and other data from a broad array of data sources.
Typically, SIEM provides incident management, reporting, and a dashboard to streamline visualization and action.
The Original Intent of SIEM
Originally, SIEM was meant to be a central repository of information for teams to use for search and investigation activities on machine-generated data. Initially, the use cases for SIEM were built around compliance requirements.
SIEM was supposed to aggregate and analyze events on the network. Aggregating machine-generated data sources and event-capture endpoints supposedly provided greater visibility into the challenges of network infrastructure security and compliance capabilities.
What SIEM Actually Provides
SIEM remains useful for search and investigation for historical log data so that you can meet compliance requirements. It has always been exceptionally good at its originally intended use.
Unfortunately, it cannot be applied effectively for real-time data analysis and predictive threat detection. It simply was not designed for this.
Security Operations Center (SOC) teams understood the practical applications it could deliver. However, attempts to add collections of queries, dashboards, and recordings onto a log data aggregator has caused a system-wide breakdown in performance.
Why SIEM Fails to Deliver in Today’s Environment
First, massive volumes of data are required to provide a holistic view of the security landscape, and the amount of data continues to grow daily. Between 2010 and 2017, the volume of data created annually worldwide grew from two zettabytes to 26 ZB. It is expected to grow to 149 ZB by 2024.
Once aggregated, this vast amount of data must be normalized and optimized for specific use cases. Each vendor requires that data be in a proprietary format for processing. Only then will the system meet compliance requirements.
Second, the requirement to analyze data in real-time, so the SIEM can offer predictive threat detection. In 2019, for the United States alone, there were 1.4 million data breaches recorded, costing over eight million US dollars per breach on average.
Current SIEM solutions can’t handle the volume. The fundamental architecture breaks down due to:
- The latency built into the solution for dashboards and reporting
- The expanding number of correlation rules to cover emerging attack patterns
- The time required to aggregate and consolidate information, and optimize it for analytics and query as it moves from source to source
- The time required to enter the data into another normalized repository to be applied to a specific use case
The time required for data processing, transition, aggregation, and the normalization does not allow real-time threat detection using today’s SIEM solutions. The only beneficiary of security through log aggregation is the SIEM vendor.
Moving From Historical to Predictive Intelligence
SIEM is the solution of choice for cybersecurity, even though it doesn’t deserve such an elevated status in its current state.
A solution built to aggregate, normalize, and optimize large volumes of log data is perfect for a historical investigation into security events. However, it was never designed for real-time event processing to provide predictive threat detection.
In a world where data volume has reached mythic proportions, and hackers throw out old tricks and develop new threats, SIEM must innovate or step aside for a solution designed for the world as it is and what it will be.
There is a way to create a security information and event management solution that can keep up with reality. The first step is to realize how poorly SIEM functions now, and bring new advances in technology, such as artificial intelligence, to the table.
MixMode’s self-supervised AI capabilities can save time and energy your SecOps team can spend on other important initiatives.
- Continually monitors network evolution, comparing it against an AI-created baseline.
- Requires far less human input and interaction, reducing human error and increasing efficiency.
- Context-aware intelligence results in far fewer false positives.
- Baseline is created in a few days vs. several weeks for other AI-enhanced platforms.
- AI-prioritized reports significantly reduce time spent sifting through SIEM logs
- Comprehensive network monitoring robust enough to handle the enormous tech stacks enterprises must manage today
MixMode can be used alongside an existing SIEM, as well, creating overlapping security measures that eliminate gaps. Request a demo today.