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The most recent articles & research from Devo
SOC analysts suffer from alert fatigue caused by too many data sources and platforms, too little context in investigations, too few people, and too little time. Mature cybersecurity teams manage this challenge by leveraging an integrated set data analytics of capabilities from best of breed solutions to establish an end-to-end experience from data collection to response. But the future SOC requires more: launching a plan for implementing advanced capabilities, including next-gen security analytics, community threat sharing, and orchestration and automation.
Cybersecurity analytics analyzes and enriches traditional and non-traditional security data sources with context to extract meaningful, actionable insight. Next-gen security analytics solutions must combine a petabyte-scale data platform with advanced techniques, including machine learning and anomaly detection, to enable analysts to identify threats that matter most to the business.
Centralized log management lays the groundwork for an enterprise’s data architecture by collecting, storing, analyzing, and enriching all event data. Full visibility is the goal, but bringing in data from anywhere requires a shift in architecture, one built for petabyte-scale, high-speed ingestion and analysis. The modern SOC relies on an ELM platform to ease access to security data and provide context to inform threat hunting, detection, investigation, and response.
EDR provides enterprise-wide surveillance on endpoints, enabling SOCs to identify threats through hunting techniques and validate potential threats. Analysts use EDR for its rich visibility into enhanced endpoint data and ability to automatically execute on rules to respond to confirmed threats. But storage costs make it impossible for many companies to retain all their EDR data for more than a few days or months. This is where modern ELM platforms come in handy – they can collect, store, and analyze years of data, enabling analysts to gain full visibility for a complete threat story.
Threat actors are taking new approaches, stealthily breaking past network defenses. NTA combats these attacks by capturing, monitoring, and analyzing all network traffic, speeding detection and response. NTA solutions use a combination of machine learning, advanced analytics, and rule-based detection to reveal suspicious network activity based on network traffic data. SOCs can leverage this insight into network communications to identify potential malicious activity like port scanning or DDoS.
SIEM has long been considered a key component of SOC technology. SIEMs are used to monitor logs and detect and alert on high-priority events for rapid investigation. However, traditional SIEM wasn’t built for the petabyte-scale world we face today; as a result, they struggle with query performance, which impacts investigation accuracy and speed. SIEM is only as powerful as the SOC’s ability to enrich log data with context, deliver insight, and put that insight to work through orchestration, automation and facilitated collaboration. The next-level SOC brings together key capabilities of the security stack, like detection and orchestration, into a single operational view to allow analysts to operate on what matters.
Natively embedding threat intelligence to deliver context-rich data lets analysts keep up with adversaries. SOC analysts who leverage tactics, techniques, and procedures (TTPs) and indicators of compromise (IOCs) track, observe, and understand potential threats in their environment. However, this is dependent on high-confidence, high fidelity intelligence relevant to the business. Otherwise, the torrent of information dispersed across multiple products and threat feeds can obstruct the analyst’s view, rather than inform it. Modern SOCs participate in both internal and external threat sharing to increase intel quality and contribute to the security community. The SOC should converge on a central location for all enriched data to drive more actionable insight.
User and entity behavior analytics solutions use a combination of baselining and machine learning analytics to detect unusual entity behaviors that deviate from the established “norm”. This creates a twofold challenge: sophisticated attackers who conduct long-term reconnaissance to learn and mimic organizational behavior patterns can hide in plain sight, and UEBA requires continual tuning to reduce the number of false positives. While data provided by UEBA solutions is becoming standard fare, the category needs new approaches to behavior modeling, as well as integration with SIEM and ELM for richer context.
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