Leveraging AI/ML for next-gen SOC environments

Sponsored Post This article discusses some of the challenges traditional SOCs face and how integrating artificial intelligence/machine learning (AI/ML) modules could help solve the challenges faced by security professionals and organizations.

The Security Operation Center (SOC) is the central hub for an organization's cybersecurity operations. Its core responsibility is monitoring and protecting the business against threats and cyberattacks. Although traditional SOCs are effective, necessary enhancements must be made to match the pace of cyber threats.

The SOC monitors and analyzes an organization's security posture in real-time. It detects, responds to, and mitigates security threats to protect the organization's assets and data. The SOC also investigates escalated security incidents, sometimes involving forensic analysis to understand the nature of threats and prevent future occurrences.

A traditional SOC depends on manual processes, rule-based detection, and reactive strategies. In contrast, a modern SOC uses artificial intelligence and machine learning technologies to improve threat detection, response, and remediation. It focuses on proactive threat hunting, behavioral analytics, data enrichment, and automated responses, allowing for faster and more accurate handling of security incidents.

Challenges of the traditional SOC

Some of the key challenges traditional SOCs face on a daily basis include:

- Overwhelming data volume: SOCs receive a large amount of data, including logs and alerts, daily. Manually analyzing this data might be time-consuming and inefficient for some SOC analysts.

- Reactive rather than proactive: Traditional SOCs tend to be more reactive, focusing on responding to incidents after they occur. This approach doesn't prioritize proactive threat hunting or preventive measures, leaving organizations more vulnerable to advanced persistent threats (APTs) and sophisticated attacks that evade detection until the damage is done.

- The lack of data enrichment in SIEM systems: This creates significant challenges for SOCs, including limited alert context, slower investigations, and higher false positive rates. SOC analysts struggle to fully understand threats, correlate related events, and automate responses effectively without enriched data. This results in delayed threat detection and response, increasing the risk of missed or overlooked security incidents.

Artificial Intelligence and Machine Learning are changing how we approach cybersecurity, especially within security operations. These technologies empower SOCs to detect, analyze, and respond to emerging threats faster and more accurately than traditional methods.

The role of AI/ML within a SOC extends beyond alert triaging or automated responses. It also encompasses critical functionalities like comprehensive log management, data enrichment, and a significant reduction in false positive generation. AI/ML enables SOCs to process extensive security telemetry in real-time, detecting anomalies and patterns that conventional rule-based systems might miss. Integrating data enrichment tools, such as threat intelligence and AI/ML, enhances threat detection accuracy, giving security teams more context for risk assessment.

Creating AI/ML-driven SOC environments with SIEM/XDR

Security Information and Event Management (SIEM) and Extended Detection and Response (XDR) are designed to collect, analyze, and provide automated responses to security events across an organization's IT infrastructure. SIEM correlates and aggregates log data, while XDR enhances detection and response across endpoints, networks, and clouds for improved threat management.

Creating SOC environments used to be considered a difficult task requiring the collective effort of several seasoned security professionals, but with a modern SIEM/XDR platform like Wazuh, that perception is changing. Wazuh, as a SIEM/XDR solution, simplifies the process of setting up a SOC due to its open source nature, ease of usability and extensive documentation on the practical implementations of the security solution. It utilizes such as malware detection, file integrity monitoring, vulnerability detection, security configuration assessment, and log management.

The sections below analyze how Wazuh can help build a SOC environment driven by artificial intelligence/machine learning.

Integrating Wazuh with present-day AI/LLM

Large Language Models (LLMs) are artificial intelligence trained and designed to understand and generate human-like text-like translations and produce coherent and relevant responses. Integrating LLMs into cybersecurity systems has opened up new possibilities for enhancing the quality and depth of log analysis. LLMs, such as those used in OpenAI ChatGPT, have gained popularity for their ability to understand and process human language, making them ideal for security operations.

Wazuh, as a SIEM/XDR platform, already offers extensive capabilities for detecting and analyzing security threats. However, by integrating it with LLMs, we can automate and enhance the interpretation of alerts, providing valuable context for faster and more informed decision-making.

The blog post Nmap and ChatGPT security auditing with Wazuh explains how LLMs can be integrated into security platforms like Wazuh. Another example is combining Wazuh with YARA for malware detection and using an LLM to enrich YARA scan results. This enriched data can be viewed using the Wazuh dashboard.

Anomaly detection in SOC environments

Anomaly detection involves identifying irregularities or deviations from an expected baseline within a system or user activity. These anomalies are usually detected using various forms of security telemetry, such as network traffic, user behavior, and system resource utilization. .

The OpenSearch anomaly detection Plugin is one tool you can utilize. Wazuh integration with the OpenSearch anomaly detection plugin leverages the Random Cut Forest (RCF) algorithm to detect anomalies in data collected by Wazuh. It offers insight through visualizations, displaying key metrics like anomaly grade, confidence levels, and frequency of anomalies. It helps detect unusual behavior across an organization's IT infrastructure and allows near real-time detection from logs and data ingested by Wazuh.

The blog post on enhancing IT security with an anomaly detection shows how Wazuh integration with the OpenSearch anomaly detection plugin can help identify patterns from failed logins that can indicate an attack. This feature aids the investigation process by allowing you to determine the source IP and agent IP with the most anomalies.

Integrating AI/ML into SOC environments helps to match the growing complexity of threats. The Wazuh and its ability to integrate with AI/ML platforms provide a solution for enhancing security operations by providing real-time threat detection and data enrichment.

Wazuh has a growing of users and professionals who tackle challenges and share insight on improving their organization's security posture. You can also visit its to learn more about the product.

Contributed by Wazuh.