Threat detection and response is a critical function of any security operations teams. Learn how it is leveraged and capabilities in highly effective SOCs.
Rapid and early identification of a threat is a critical step in the security lifecycle, especially as threat actors are learning how to breach organizations at an alarming rate. However, the complexity of multi-stage, covert attacks can make detection difficult. Advanced threat detection solutions typically employ correlation rules, context enrichment, entity analytics, and other detection content to detect exploits.
SOC analysts consistently consider their jobs to be painful, due to never-ending alerts, time wasted investigating false positives, and the high-stakes, low-rate-of-return circumstances.
Detection rules are highly customized to the network, systems, and users, and can require a significant time commitment to create, monitor, and refine alert definitions.
Detections can only be accurate if based on relevant threat, network, and data context, but many tools can’t scale to that level of correlation.
It’s getting harder to identify attacks that are subverting standard detection technology. SOCs must use behavioral observations, real-time intelligence, and context to more quickly identify suspicious activity. However, it’s difficult to bring together the massive volumes of data, context, and threat and malware intelligence. High-powered cybersecurity analytics solutions for collecting, correlating, and analyzing all security-relevant data – with a focus on automated enrichment – help improve signal, reduce noise, and detect the threats that matter most to the business.
SOC analysts contend with a seemingly endless number of alerts. Advanced threat detection solutions automatically prioritize threats based on adaptive intelligence for faster alert triage and investigation. This shift enables analysts to reduce overall investment in non-critical alerts, and focus on investigation and response where it matters most. Modern tools are typically supported by a practitioner-defined alerting framework, an approachable UI, and machine learning for intelligent risk prioritization and categorization.
SIEM produces an unmanageable number of alerts, which must be validated through the review cycle to be labeled an official incident. To do so, SOC analysts pivot and iterate on petabytes of event data, drawing on threat intelligence and behavioral analytics to confirm the threat presence. To get the job done, the modern SOC should leverage high-speed security analytics with simple and agile querying, complete visibility into historical and real-time data, and an intuitive investigative workflow providing automated, pre-populated information.
Incidents are passed on to response and IT teams for clean-up actions. Historically, SOCs relied on disparate tools and unreliable integrations to shift to next steps, which simply isn’t working anymore. Triage and investigation knowledge should be searchable, shareable, and exportable to enable persistence of earlier efforts. The future SOC leverages a next-gen SIEM as a central hub for all data and process, with native or tightly integrated components across the security operations lifecycle, from identification to response.
