December 9, 2024
3 Common Challenges To Avoid While Implementing SOAR
August 20, 2021Quantifying Risk To Increase SOAR Effectiveness
September 13, 20213 Common Challenges To Avoid While Implementing SOAR
August 20, 2021Quantifying Risk To Increase SOAR Effectiveness
September 13, 2021Demystifying the Cognitive Capabilities of SOAR
The modern threat landscape is populated by serious threat vectors, and malicious actors armed with sophisticated attack tools. To stay ahead of them, modern organizations need a way to proactively understand the threat landscape, and mitigate real-world threats. And for this, SOAR tools are absolutely vital.
Gartner defines Security Orchestration, Automation and Response or SOAR as “technologies that enable organizations to collect inputs monitored by the security operations team”. By leveraging SOAR tools, enterprises can strengthen their threat detection capabilities, streamline incident analyses and response procedures, and automate security operations.
Next-gen SOAR platforms also leverage Artificial Intelligence (AI) and Machine Learning (ML) to provide cognitive capabilities that further strengthen an organization’s ability to detect, mitigate and prevent threats.
Collect Actionable Threat Intelligence
As cyber threats increase in frequency and scale, organizations need an efficient and effective way to collect, analyze and correlate security event data from multiple sources. This is difficult to do manually, but easier with SOAR. SOAR integrates multiple disparate tools within a single platform, and provides an automated and orchestrated response throughout incident identification, containment, eradication and recovery.
Nonetheless, organizations still find it challenging to collect actionable threat intelligence to prop up their offensive and defensive capabilities. In these efforts, SOAR with Artificial Intelligence and Machine Learning is indispensable. AI/ML-powered SOAR can analyze large data sets, and instantaneously identify anomalies, vulnerabilities or suspicious patterns that may indicate a threat. This automatic correlation reveals whether a current incident shares common elements with past incidents
Improved Real-time Threat Detection Accuracy
New SOAR solutions apply cognitive automation to improve the accuracy of threat detection in real time. Through Machine Learning techniques, security teams can rapidly analyze alerts from multiple sources, including Security Information and Event Management (SIEM) platforms, User Behavioral Analytics (UBA), Endpoint Detection and Response (EDR), vulnerability scanners, external threat intelligence feeds, and other contextual data. With artificial neural network (ANN)-based models, they can detect and mitigate phishing attempts, malware attacks, and other kinds of threats.
Armed with deep ranking and correlation algorithms, these SOAR tools can perform sophisticated analyses that SIEM systems with simple, rule-based matching algorithms just cannot do. Moreover, cognitive SOAR systems can also accept feedback to self-learn from experience, and improve their threat detection and mitigation accuracy over time.
Triage Alerts for More Effective Incident Response
A cognitive SOAR platform helps security personnel triage, prioritize and investigate alerts by severity or impact. Cases historically deemed malicious are given higher priority, while those that were previously flagged as false positives are assigned a lower priority. Machine Learning generates a list of previous similar cases, so analysts can understand the historical context and use this information to guide their response actions.
ML continuously analyzes every analyst’s case pipeline, and reviews their previous performance. This enables security administrators to assign the best analysts to investigate a particular incident. The platform also prioritizes/reprioritizes analysts’ queues to ensure that they prioritize the most critical cases, and accelerate incident response as required.
Distinguish Genuine Threats from False Positives
SOAR platforms gather large amounts of threat data from multiple sources. Therefore, they are beset by the common problem of false positives. False positives occur when the platform flags a security alert that doesn’t actually exist. They often lead to “alert fatigue”. As security analysts are burdened with more alerts to validate and address, they are distracted from dealing with legitimate security alerts.
SOAR tools with cognitive automation automate alert analyses, and also consider event context as they analyze each alert. These features enable them to more easily distinguish false positives from genuine threats, and at faster speeds than non-cognitive SOAR tools. As a result, security teams who were previously overwhelmed by huge alert volumes can now focus on addressing real issues, and on strengthening the organization’s security posture.
Detect Zero-day and Other Unknown Threats
A zero-day threat refers to a recently discovered (and therefore unpatched) security vulnerability that a threat actor can potentially exploit to access enterprise systems or data. Until the vulnerability is patched, a zero-day threat can leave a network vulnerable for days, weeks or even months.
Security teams that are worried about zero-day threats and attacks should consider SOAR with cognitive automation. Cognitive SOAR platforms with Machine Learning are not limited to rule-based analyses. Instead, they can go beyond such imperfect capabilities to detect new threats including unknown and zero-day threats that many other non-cognitive security tools miss.
Conclusion
SOAR platforms, like SIRP, are already a must-have in the modern organization’s security toolkit. And now, organizations can further maximize the effectiveness of their security operations, automate threat hunting, and respond faster and better to security events with a cognitive SOAR platform. Security attacks are already more frequent and more sophisticated than ever before. The future is likely to be even more vulnerable, so organizations should definitely explore cognitive SOAR platforms sooner rather than later.
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