Why Good Security Analytics Is Important
You can’t defend against threats that you don’t know exist. SOCs need visibility into every component of their organization’s ecosystem to identify and respond to potential threats.
However, raw data is of little value to a SOC analyst. Most indicators of an attack can easily be dismissed as noise or normal operations. Only by collecting and aggregating multiple different sources of information can a security analyst gain the context required to pick out the true attacks from the false positives.
This is the role of security analytics. It ingests the raw data produced by security tools, computers, and other systems and analyzes it to pick out patterns and trends that could indicate a potential incident. These alerts, along with the data used to generate them, are then presented to the analyst, enabling them to more rapidly and accurately assess the situation and respond to the potential threat.
How Security Analytics Works?
Security analytics is all about associating pieces of data together to create a story that describes the activities of a potential threat within an organization’s network. Building this story requires a security analytics tool to find associations between events and pick out ones that indicate a potential threat.
This can be accomplished using a variety of techniques, including:
- Signature Detection: For known threats, it is possible to describe exactly what the threat looks like or does within a compromised environment (such as ransomware’s encryption of files). By using these signatures, a security analytics tool can quickly and easily determine the presence of a threat. From there, it’s possible to work backward and forward to learn about the entire attack chain.
- Anomaly Detection: By definition, an attacker performs unusual actions within a compromised system, such as stealing data or encrypting files. Anomaly detection looks for activities that are outside of the norm, which may indicate an intrusion.
- Pattern Detection: Some events are benign on their own but suspicious or malicious when combined with others. For example, a single failed logon might be a mistyped password, while many may indicate a credential stuffing attack. Much of security analytics is looking for patterns, using signature-based or anomaly-based detection or both.
- Machine Learning: Signature and anomaly detection are useful if you can define “malicious” or “normal”, but this is not always a simple task. Machine learning algorithms applied to security analytics can teach themselves how to recognize potential threats and differentiate them from false positives.
In the end, security analytics boils down to pattern detection and statistics. However, finding patterns or oddities tells security analysts where to focus their attention, making them more effective at identifying and responding quickly to real threats.
The Evolution of Security Analytics
Security analytics began with the security information and event management (SIEM) system, which started out as a log collection solution and adapted to also offer security analytics. This enabled them to translate the massive amount of information available to them into usable and valuable threat intelligence for SOC teams.
Security orchestration, automation, and response (SOAR) tools are leveraging security analytics by automating the response to the detected threats. This enables incident response to be performed at machine speed, which is essential as attacks become increasingly widespread and automated.
Today, solutions are becoming more targeted in their use of security analytics. Extended detection and response (XDR) solutions are embedding security analytics as part of the overall offering which offers a consolidation of SIEM, SOAR, security analytics and security solutions into a holistic single pane of glass for the security analyst. XDR takes security analytics to the next level by feeding the algorithms not only singular security events but enrichment of raw telemetry and threat intel, thus allowing a higher level of accuracy for analytics based detections.
Security Analytics with Check Point
Generating effective threat intelligence requires a solution with robust security analytics capabilities. Check Point solutions are designed to ingest and analyze threat information from a variety of sources to provide high-value threat intelligence.
At the macro scale, Check Point ThreatCloud AI analyzes 86 billion security events per day to detect new threats, malware variants, and attack campaigns. The threat intelligence generated by ThreatCloud AI is combined with data specific to an organization by Check Point Infinity products to provide more targeted security insights and threat detection.
Effective security analytics is crucial to an organization’s threat detection and response strategy. To learn more about the analytics capabilities of Check Point Infinity SOC and how it can help to improve threat detection while eliminating false positives, you’re welcome to check out this demo video.
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