Today’s organizations are under constant threat from cyber attacks, with high-profile breaches making headlines almost daily. After reflecting on my extensive experience in the security field, it’s clear that this issue is not only a human problem, but also a math problem. The sheer volume of threats and security tasks is too much for any Security Operations Center (SOC) to handle manually within a reasonable timeframe. However, there is a solution – SOC 3.0, an AI-augmented environment that enables analysts to do more with less and shifts security operations from a reactive to a proactive stance. The transformative power of SOC 3.0 will be explored in this article, demonstrating how artificial intelligence can significantly reduce workload and risk, providing world-class security operations that every Chief Information Security Officer (CISO) desires. To appreciate this leap forward, it’s essential to understand the evolution of the SOC over time and how the steps leading up to 3.0 have set the stage for a new era of security operations.

A Brief History of the SOC

The Security Operations Center (SOC) has been the frontline defense against cyber threats for decades. As threats have become more sophisticated, the SOC has had to evolve. I’ve witnessed three distinct phases of SOC evolution, which I refer to as SOC 1.0 (Traditional SOC), SOC 2.0 (the current, partly automated SOC), and SOC 3.0 (the AI-powered, modern SOC).

In this article, I’ll provide an overview of each phase, focusing on four core functions:

• Alert triage and remediation
• Detection and correlation
• Threat investigation
• Data processing

SOC 1.0: The Traditional, Manual SOC

Let’s examine how the earliest SOCs handled alert triage and remediation, detection and correlation, threat investigation, and data processing.

Handling Noisy Alerts with Manual Triage and Remediation

In the early days, we spent a significant amount of time on simple triage. Security engineers would build or configure alerts, and the SOC team would then struggle under a never-ending flood of noise. False positives were abundant.

For instance, if an alert fired every time a test server connected to a non-production domain, the SOC quickly realized it was harmless noise. We’d exclude low-severity or known test infrastructure from logging or alerting. This back-and-forth process became the norm, with SOC resources invested more in managing alert fatigue than in addressing real security problems.

Remediation was also entirely manual. Most organizations had a Standard Operating Procedure (SOP) stored in a wiki or SharePoint. After an alert was deemed valid, an analyst would walk through the SOP:

• “Identify the affected system”
• “Isolate the host”
• “Reset credentials”
• “Collect logs for forensics”, and so on.

These SOPs lived primarily in static documents, requiring manual intervention at every step. The main tools in this process were the Security Information and Event Management (SIEM) system (often a platform like QRadar, ArcSight, or Splunk) combined with collaboration platforms like SharePoint for knowledge documentation.

Early SIEM and Correlation Challenges

During the SOC 1.0 phase, detection and correlation mostly meant manually written queries and rules. SIEMs required advanced expertise to build correlation searches. SOC engineers or SIEM specialists wrote complex query logic to connect the dots between logs, events, and known Indicators of Compromise (IOCs). A single missed OR or an incorrect join in a search query could lead to countless false negatives or false positives.

Only Experts for L2 and L3 Threat Investigation

Threat investigations required highly skilled (and expensive) security analysts. Because everything was manual, each suspicious event demanded that a senior analyst perform log deep dives, run queries, and piece together the story from multiple data sources. There was no real scalability; each team could only handle a certain volume of alerts. Junior analysts were often stuck at Level 1 triage, escalating most incidents to more senior staff due to a lack of efficient tools and processes.

Manual Pipelines for Data Processing

With big data came big problems, such as manual data ingestion and parsing. Each log source needed its own integration, with specific parsing rules and indexing configuration. If you changed vendors or added new solutions, you’d spend months or even multiple quarters on integration.

In short, SOC 1.0 was marked by high costs, heavy manual effort, and a focus on “keeping the lights on” rather than on true security innovation.

SOC 2.0: The Current, Partly Automated SOC

The challenges of SOC 1.0 spurred innovation. The industry responded with platforms and approaches that automated key workflows to some degree.

Enriched Alerts and Automated Playbooks

With the advent of Security Orchestration, Automation, and Response (SOAR), alerts in the SIEM could be enriched automatically. An IP address in an alert, for example, could be checked against threat intelligence feeds and geolocation services.

Automated SOPs were another significant improvement. SOAR tools allowed analysts to codify some of their repetitive tasks and run “playbooks” automatically. Instead of referencing a wiki page step by step, the SOC could rely on automated scripts to perform parts of the remediation.

Upgraded SIEM: Out-of-the-Box Detection and XDR

In SOC 2.0, detection and correlation saw key advances in out-of-the-box content. Modern SIEM platforms and Extended Detection and Response (XDR) solutions offer libraries of pre-built detection rules tailored to common threats, saving time for SOC analysts who previously had to write everything from scratch.

Incremental Improvements in Threat Investigation

Despite XDR advances, the actual threat investigation workflow remains very similar to SOC 1.0. Tools are better integrated, and more data is available at a glance, but the analysis process still relies on manual correlation and the expertise of seasoned analysts.

Streamlined Integrations and Data Cost Control

Data processing in SOC 2.0 has also improved with more integrations and better control over multiple data pipelines. For example, SIEMs like Microsoft Sentinel offer automatic parsing and built-in schemas for popular data sources.

In sum, SOC 2.0 delivered significant progress in automated enrichment and remediation playbooks. However, the heavy lifting – critical thinking, contextual decision-making, and sophisticated threat analysis – remains manual and burdensome.

SOC 3.0: The AI-Powered, Modern SOC

Enter SOC 3.0, where artificial intelligence and distributed data lakes promise a quantum leap in operational efficiency and threat detection.

AI-Driven Triage and Remediation

Thanks to breakthroughs in AI, the SOC can now automate much of the triage and investigation process. Machine learning models can automatically classify and prioritize alerts with minimal human intervention.

Adaptive Detection and Correlation

The SIEM layer in SOC 3.0 is far more automated, with AI/ML models creating and maintaining correlation rules. The system continuously learns from real-world data, adjusting rules to reduce false positives and detect novel attack patterns.

Automated Deep-Dive Threat Investigations

Arguably the most transformative change is in how AI enables near-instantaneous investigations with no need to codify. Instead of writing a detailed manual or script for investigating each type of threat, AI engines process and query large volumes of data, producing contextually rich investigation paths.

Distributed Data Lakes and Optimized Spend

While the volume of data required to fuel AI-driven security grows, SOC 3.0 relies on a more intelligent approach to data storage and querying.

Conclusion

From a CISO’s perspective, SOC 3.0 is the natural next step in modern cybersecurity, enabling teams to handle more threats at lower cost, with better accuracy and speed. While AI won’t replace the need for human expertise, it will fundamentally shift the SOC’s operating model, allowing security professionals to do more with less, focus on strategic initiatives, and maintain a stronger security posture against today’s rapidly evolving threat landscape.

About Radiant Security

Radiant Security provides an AI-powered SOC platform designed for SMB and enterprise security teams looking to fully handle 100% of the alerts they receive from multiple tools and sensors.

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About the Author: Shahar Ben Hador spent nearly a decade at Imperva, becoming their first CISO. He went on to be CIO and then VP Product at Exabeam. Seeing how security teams were drowning in alerts while real threats slipped through drove him to build Radiant Security as co-founder and CEO.