Cyberattacks have taught enterprises that the value of acting early pays off. Most of them realize that SOC is critical in securing expanding business environments driven by the cloud. However, the effectiveness of the SOC remains a big question. With the rapid proliferation of digitization, mobile users, connected devices, and data centers located in the cloud and on-premise across geographies, the expectations from SOC are at an all-time high. The performance demands have peaked, and security teams expect the SOC to have more steam in tackling every intelligent threat. Continuous monitoring of large enterprise environments of this scale and size from intelligent intrusions is a highly arduous task. It is hard to correlate log files from multiple sources to detect a threat and alert the SOC team. Most correlation rules are written manually after the incident, and critical events are often missed, leading to many false positives. These rules are ineffective when dealing with a broad range of attack vectors that make one event look different from another. Relying on manually drafted correlation rules isn’t recommended; It generates huge alert volumes, making it nearly impossible for a SOC team to pick and fix the one that matters. This flood of alerts denies the clarity that every analyst looks to detect an attack. If they try to filter real attacks from this noise, analysts can either miss the attack or find them extremely late.
Detecting an event shouldn’t be considered an analyst’s errand as modern cyber attacks’ are relentlessly innovating new threat vectors with spectacularly high levels of sophistication to establish their dominance.
The changing threat landscape presents a strong case for enterprises to adopt a more active and interventionist approach to mitigating the risks posed by complex attack vectors. The rivalry will not end, and enterprises must tilt the balance of power towards them. When coalescing a strategy to compete with antagonizing attackers, one must ditch the strictures of a manually operated SOC. Existing security realities are forcing a realization that there is no substitute to security automation.
Machine Learning and SOC
Machine Learning can help analysts find critical security events, increasing their productivity and outcomes. ML can detect patterns, infers conclusions, binds data from multiple sources, and can reduce the number of alerts without depending on the analyst’s experience to detect the event. ML-driven SOCs can prevent threats through endpoint security, detect and investigate threats through extended detection and response (XDR), and leverage security orchestration capabilities to implement automated playbooks for faster incident response. For detecting intelligent attacks, the ML-driven SOC uses XDR features to learn from the data in the data lake (a centralized source of log data) to get insights into the detected activity. This helps the SOC establish accepted baseline behavior and differentiate it from malicious ones. To achieve the desired outcomes, the ML models must be trained to categorize data. The data required to train the ML model is of two types. Labeled data and Unlabelled data. Labeled data means tagged data, mostly related to a prior understanding of it. It might have more than one attribute value, helping the ML algorithm sort and classify them. This type of data is used for supervised learning models to derive mathematical models and enable the system to classify data. However, this learning model can work well only with finite data categories. Unlabeled data does not have any labels tagged to them. This is the type of data that ML models need to classify.
Supervised machine learning uses labeled data sets to make informed decisions. The data can be suspicious files or malware infected files, which can help the system know the difference between infected files and ones that are not. This form of learning requires a continuous feed of data to tackle evolving threats and works well when the number of data sets is finite. Unsupervised machine learning is used when the number of data sets is not defined. This model constructs math models from categorized data sets to find patterns in input data to derive output and recognize any anomalies in data.
Machine learning enhances the system’s ability to understand and predict output based on different algorithms and data without any programming. This helps a SOC to detect any anomalies and signatureless threats automatically. However, ML cannot replace a security analyst. It helps them be proactive by automating the detection and response of incidents that the analyst could have missed in a heap of alerts.
ML can maximize SOC outcomes through effective threat detection, vulnerability management, and by enabling quality security recommendations. An ML-driven SOC, along with the experience of analysts, can be a potent force in detecting zero-day exploits, malware, ransomware with high precision to ensure better levels of protection. ML’s ability to continuously and dynamically learn from normal baseline behaviors, network traffic patterns, system usage across the organizational environments make it even more reliable to mitigate attack threats. ML can automatically analyze variables and data sets to quickly predict and detect threats, vulnerabilities, and weaknesses before even an attacker can exploit them. By effective ingestion of threat intelligence, ML can improvize threat response by implementing the right playbook, reducing the investigation time of analysts by providing them the right point of reference to act, and minimizing the meantime to detect and respond. It can provide suggestions on identifying risks and how to mitigate them in the future. ML gives us the option of taking a more thoughtful security approach by combining different data types and cognitive reasoning to draw inferences and enable better security predictions and decisions.
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