The Rapid Emergence of Unified Cyber Security Analytics Platforms

This blog article breaks down what a unified cyber security analytics platform is, the current security analytics challenges it addresses, the vast security analytics use cases it covers and the immense value it brings to SecOps. 

But first, let’s…. 

Imagine the Possibilities of a Unified Cyber Security Analytics Platform, Where: 

Analysts spend less time conducting wasteful investigations, chasing false positives and more time finding and responding to true threats.  

Engineers spend less time maintaining fragile, loosely bound systems and more time automating and maturing their SecOps program. 

Decision-makers spend less time stressing annual budget increases due to SIEM data cost hikes and more time maximizing budget to improve security posture.  

Fulfilling the Unkept Promises of Previous Technologies 

We’ve heard this before, right? The promises of traditional SIEMs have often fallen short. These legacy solutions, designed for a simpler IT era, struggle to navigate today’s complex, distributed, and interconnected environments. Their limitations in handling vast amounts of data and their inability to proactively defend against sophisticated threats without excessive human intervention have left security teams frustrated.

Below is a list of challenges a unified cyber security analytics platform is designed to address. Unfortunately, they have a compounding relationship. 

• Siloed Solution Complexity: SIEM, EDR, XDR, UEBA, SOAR, Identity Analytics, DLP, NTA…. the list of acronyms goes on and on. They all serve a purpose for SecOps, but when they operate as silos the operational complexity mounts and so do the operational costs. 
• Big, Diverse Data Visibility: The sheer volume and variety of data that organizations have to handle today is absurd and only increasing. The siloed security analytics tools SecOps teams use today are unable to handle all of it, leaving critical telemetry un-analyzed. This leads to gaps in detections and critical context missing for investigations and precise response. 
• Data Cost Control: The big and diverse data challenge amplifies the ongoing limited budget constraints of today’s CISOs. Not only are SIEM data ingestion costs skyrocketing, but having to find optimal ways for normalizing new data sources requires additional and expensive resources. We’ve seen the rise of data management solutions like Cribl, but yet again we’re throwing another siloed tool into the already cobbled together ecosystem. 
• Wasted Time: All of these challenges ultimately result in expensive resources doing mundane tasks to either maintain the status quo or spend inordinate amounts of time to derive insights. Time we no longer have. Time needed for meaningful work to help improve the security posture of the organization. Throwing more people at the problem isn’t a sustainable solution. 

So, What is a Unified Cyber Security Analytics Platform? 

In the simplest sense, a unified cyber security analytics platform is a big data platform purposefully designed for cyber security that incorporates the cutting edge capabilities of data science, machine learning and artificial intelligence. Some refer to it synonymously as the next generation of SIEM, or Next-Gen SIEM. This is a fair label given the centralized and critical role a SIEM is intended to play within SecOps.

Ultimately, a unified cyber security analytics platform is the convergence of capabilities found from siloed SIEM, XDR, UEBA, SOAR, NTA and Identity Analytics solutions—working in harmony rather than isolation. And the more advanced platforms like Gurucul include data management, streaming and optimization functions similar to Cribl. 

The unique capabilities of truly unified cyber security analytics platforms include: 

• Data and Storage Agnostic: These platforms are designed to expand the traditional definition of security data to encompass any information relevant to understanding your cyber risk. This includes business application data, user and entity behavior, identity data, IT operations data, and HR data. As a platform focused on advanced analytics, the storage location of your data is entirely dependent on your specific security data architecture requirements.
• Advanced Analytics: Instead of relying on static detection rules, a unified security analytics platform leverages cutting-edge data science to analyze vast and complex datasets. Machine learning (ML) threat detection models contextualize user and entity behavior anomalies with all relevant telemetry, uncovering hidden threats. Additionally, large language models (LLMs) contribute to the development of new detections and the refinement of existing models.
• SecOps Lifecycle Automation: Beyond delivering real-time, high-fidelity threat detection, a unified security analytics platform offers significant advancements in automation and orchestration, powered by AI and ML. This frees up SecOps teams to focus on strategic initiatives. The optimal platform streamlines data ingestion, normalization, filtering, enrichment, and routing, while providing pre-built ML detection models that deliver immediate value. Additionally, it centralizes relevant context and enables the automation of response playbooks tailored to your specific requirements.
• Cost and Data Control: A unified security analytics platform prioritizes both architectural control and cost optimization. The primary expense associated with advanced analytics is the computational resources required, making data volume a direct cost driver. However, modern data management capabilities enable you to filter out unnecessary data attributes, creating streamlined pipelines that significantly reduce costs compared to traditional SIEMs. You can confidently route non-critical data to lower-cost storage while retaining the ability to search these federated repositories for threat hunting and compliance purposes.
• Flexible Architecture: Finally, these unified cyber security analytics platforms are designed to be extremely modular and customizable, allowing you to unlock the capabilities needed to achieve your use cases and tailor the platform to conform to YOUR business requirements.

Multiple Use Cases from a Centralized Security Analytics Platform 

Rather than acquiring unique tooling for various segments of the security team, unified security analytics platforms serve as a centralized tool capable of assisting a range of teams in their respective missions. Most security analytics platforms account for multi-tenancy and privacy considerations, providing Role-Based Access Control (RBAC) and data masking to ensure confidentiality and compliance. Here are a few broad use case categories most organizations would leverage these platforms for: 

Threat Detection Investigation and Response (TDIR)

This is an obvious use case category given the nature of a security analytics platform. They deliver better threat detections than traditional and siloed tools, more centralized context around cyber risk for accelerated investigations and built-in automated response capabilities. Whatsmore, these platforms are uniquely suited to help eliminate blindspots across complex hybrid cloud environments, giving analysts complete visibility for a centralized dashboard.  Learn more about TDIR.  

Modern Compliance and Data Privacy Requirements 

Falling in-line with TDIR, is the growing compliance and regulatory requirements companies must abide by as everyone wakes up to the realities of cyber risks. Unified cyber security analytics platforms are well suited to meet the logging, reporting, auditing and alerting requirements of GDPR, HIPAA, PCI-DSS and SOX, helping companies demonstrate adherence to these standards. 

Insider Threat Management 

The underpinning of these unified security analytics platforms is User and Entity Behavior Analytics (UEBA), a critical component to predicting insider threats before exfiltration. Machine Learning insider threat detection models then add context to the behavioral deviations from identity and HR application data. This gives insider threat teams the context required to confidently open cases and work with HR and Legal to proactively mitigate true inside risks without breaking employee privacy laws. Learn more about Insider Threat Management. 

Identity Threat Detection and Response (ITDR) 

A growing cyber security approach due to identity-based attacks being the #1 cause of most breaches and unified cyber security analytics platforms are well positioned to support this use case. They are able to deliver real-time insight into the identity attack surface—giving security teams visibility into over-privileged, rogue, orphaned, or compromised accounts. Furthermore, unified security analytics platforms help analysts detect and respond to identity-based attacks before they compromise critical data. Helping them adopt a proactive stance against identity-based threats with capabilities like visibility into identity governance, risk scoring informed by identity-based indicators of compromise, and automated response playbooks. Learn more about ITDR.

The Gurucul Dynamic Security Analytics Platform, REVEAL 

Gurucul is the only cost-optimized security analytics company founded in data science that delivers radical clarity about cyber risk. Our REVEAL security analytics platform analyzes enterprise data at scale using machine learning and artificial intelligence. Instead of useless alerts, you get real-time, actionable information about true threats and their associated risk. The platform is open, flexible and cloud native. It conforms to your business requirements so you don’t have to compromise. Our technology has earned us recognition from leading industry analysts as the most Visionary platform and an Overall leader in product, market and innovation. Our solutions are used by Global 1000 enterprises and government agencies to minimize

their cybersecurity risk. Request a demo today!