A Data Storage Foundation for Security Analytics

How are you collecting and storing data for security analytics?

The exponential growth of unstructured data has prompted innovations for creating insights. Security analytics enables proactive and real-time responses to security gaps and threats. Effective data storage and access are foundational to enterprise-class security analytics. With fast, scalable access to modern unstructured data, security operations teams can improve forensic analysis, anomaly detection, event monitoring, and more.

Unstructured log, event, packet, and flow data emanates from various entities in the IT infrastructure—on- and off-premises. Sources include:

• Applications
• Infrastructure (server, network, and storage)
• Virtual machines
• Containers
• Operating systems
• Security devices
• Clouds

Applications also produce a soaring amount of event data that captures user interactions. Leading solutions such as those offered by Splunk and Elastic have been effective at helping organisations leverage log and event data for security analytics (SIEM) use cases.

Security analytics often presents challenging requirements for collecting, delivering, and analysing log and event data. The growing need to respond to threats as they occur mandates real-time data processing. Effective correlation and threat analysis require a thorough data capture from across the digital environment and the ability to maintain a rich historical data set. Data systems must also be resilient in the face of increasing data and a constantly changing landscape of data-forwarding systems and ever-present risks and threats.

Real-time Threat Detection

Security analytics can demand a large amount of data, much of it real-time data. Enabling responsive security analytics means keeping pace with your evolving digital infrastructure. You can diagnose and analyse more threats with high-speed ingestion to capture high-volume, rapidly growing log, packet, flow, and event data. With reliable all-flash performance, you can keep up with demanding,complex queries and the real-time processing needed for rapid mean time to detect (MTTD) and the mean time to remediate (MTTR) security threats.

Historical Context

In addition to real-time analysis, you must have access to volumes of historical data to extend key security capabilities and apply advanced techniques such as anomaly detection and user & entity behavior analytics (UEBA). Easy retrieval of historical data enables longer-term analysis for advanced persistent threats (APT) to identify the potential origins of unauthorized, undetected access to your intellectual property (IP) or your customer’s personal and health data (PII/PHI). It also simplifies forensic analysis and evidence gathering in the unfortunate event you discover attacks or breaches.

You can achieve fast security analytics at any scale by adding blades for consistent, linear performance increases. You can retain all your data and keep it searchable by leveraging the elastic scalability of Everpure systems. Complex search and forensic analysis are simplified with quicker access to high volumes of historical data to address a broader range of APT and legal discovery requirements.

Resilient, Simplified Foundation

Ever-changing digital landscapes create new demands, such as:

• Data storage must be resilient for planned and unplanned changes.
• Storage must be capable of readily adding new source types and scaling to capture rapidly growing data sets.
• You must minimize planned and unplanned maintenance downtime to preserve the availability of security applications.
• Capacity increases require proactive planning to protect essential data.

Pure’s disaggregated architecture separates storage and compute resources for efficient, agile resource deployment. This approach maximises application uptime with nondisruptive scale and replacement, diminishing costly rebalancing, data re-hydration, and rebuild operations. Additionally, Pure1 offers AI-driven forecasting of capacity needs, simplifying essential workload planning.