SOC 3.0 – The Evolution of the SOC and How AI is Empowering Human Talent

Organizations today face relentless cyber attacks, with high-profile breaches hitting the headlines almost daily. Reflecting on a long journey in the security field, it’s clear this isn’t just a human problem—it’s a math problem. There are simply too many threats and security tasks for any SOC to manually handle in a reasonable timeframe. Yet, there is a solution. Many refer to it as SOC 3.0—an AI-augmented environment that finally lets analysts do more with less and shifts security operations from a reactive posture to a proactive force. The transformative power of SOC 3.0 will be detailed later in this article, showcasing how artificial intelligence can dramatically reduce workload and risk, delivering world-class security operations that every CISO dreams of. However, to appreciate this leap forward, it’s important to understand how the SOC evolved over time and why the steps leading up to 3.0 set the stage for a new era of security operations.

A brief history of the SOC

For decades, the Security Operations Center (SOC) has been the front line for defending organizations against cyber threats. As threats become faster and more sophisticated, the SOC must evolve. I’ve personally witnessed three distinct phases of SOC evolution. I like to refer to them 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 provide an overview of each phase, focusing on four core functions:

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

SOC 1.0: The traditional, manual SOC

Let’s take a look at how the earliest SOCs handled alert triage and remediation, detection & correlation, threat investigation and data processing.

Handling noisy alerts with manual triage & remediation

In the early days, we spent an inordinate 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 abounded.

For example, 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—”Tune these alerts!” or “Exclude this server!”—became the norm. SOC resources were invested more in managing alert fatigue than in addressing real security problems.

Remediation, too, was 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 SIEM (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. The complexity was so high that only a small subset of expert individuals in the organization could maintain these rule sets effectively, leading to bottlenecks and slow response times.

OnlyExperts for L2 & 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. For SIEMs like QRadar, administrators had to configure new database tables, data fields, and indexing rules for each new log type. This was slow, brittle, and prone to human error. Finally, many organizations used separate pipelines for shipping logs to different destinations. This was also manually configured and likely to break whenever sources changed.

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 (to some degree) key workflows.

Enriched alerts & automated playbooks

With the advent of SOAR (Security Orchestration, Automation, and Response), 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. A host name could be correlated with an asset inventory or vulnerability management database. This additional context empowered analysts to decide faster whether an alert is credible. Automated SOPs was another big 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, like isolating a host or blocking an IP.

However, the decision-making piece between enrichment and automated action remained highly manual. Analysts might have better context, but they still had to think through what to do next. And to make matters worse, the SOAR tools themselves (e.g., Torq, Tines, BlinkOps, Cortex XSOAR, Swimlane) needed extensive setup and maintenance. Expert security engineers had to create and constantly update playbooks. If a single external API changed, entire workflows could fail. Simply replacing your endpoint vendor would trigger weeks of catch up in a SOAR platform. The overhead of building and maintaining these automations is not exactly trivial.

Upgraded SIEM: Out-of-the-box detection & XDR

In SOC 2.0, detection and correlation saw key advances in out-of-the-box content. Modern SIEM platforms and XDR (Extended Detection and Response) 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. Tools like Exabeam, Securonix, Gurucul and Hunters aim to correlate data from multiple sources (endpoints, cloud workloads, network traffic, identity providers) more seamlessly. Vendors like Anvilogic or Panther Labs provide libraries of comprehensive rule sets for various sources, significantly reducing the complexity of writing queries.

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. While XDR can surface suspicious activity more efficiently, it doesn’t inherently automate the deeper forensic or threat-hunting tasks. Senior analysts remain crucial to interpret nuanced signals and tie multiple threat artifacts together.

Streamlined integrations & 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. This accelerates deployment and shortens time-to-value. Solutions like CRIBL allow organizations to define data pipelines once and route logs to the right destinations in the right format with the right enrichments. For example, a single data source might be enriched with threat intel tags and then sent to both a SIEM for security analysis and a data lake for long-term storage.

These improvements certainly help reduce the burden on the SOC, but maintaining these integrations and pipelines can still be complex. Moreover, the cost of storing and querying massive volumes of data in a cloud-based SIEM or XDR platform remains a major budget item.

In sum, SOC 2.0 delivered significant progress in automated enrichment and remediation playbooks. But the heavy lifting—critical thinking, contextual decision-making, and sophisticated threat analysis—remains manual and burdensome. SOC teams still scramble to keep up with new threats, new data sources, and the overhead of maintaining automation frameworks.

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 & remediation

Thanks to breakthroughs in AI, the SOC can now automate much of the triage and investigation process with AI. Machine learning models—trained on vast datasets of normal and malicious behavior—can automatically classify and prioritize alerts with minimal human intervention. AI models are also packed with security knowledge which helps augment human analysts’ capability to efficiently research and apply new information to their practices.

Instead of building rigid playbooks, AI dynamically generates response options. Analysts can review, modify, and execute these actions with a single click. Once a SOC team gains trust in AI-augmented responses they can let the system remediate automatically, further reducing response times.

This doesn’t eliminate human oversight, with humans-in-the-loop reviewing the AI’s triage reasoning and response recommendations, but it does drastically reduce the manual, repetitive tasks that bog down SOC analysts. Junior analysts can focus on high-level validation and sign-off, while AI handles the heavy lifting.

Adaptive detection & correlation

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

Ongoing threat intelligence feeds, behavioral analysis, and context from across the entire environment come together in near real-time. This intelligence is automatically integrated, so the SOC can adapt instantly to new threats without waiting for manual rule updates.

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 and produce contextually rich investigation paths.

Deep analysis at high speed is all in a day’s work for AI as it can correlate thousands of events and logs from distributed data sources within minutes and often within seconds, surfacing the most relevant insights to the analyst.

Finally, SOC 3.0 empowers junior analysts as even a Level 1 or 2 analyst can use these AI-driven investigations to handle incidents that would traditionally require a senior staff member. Vendors in this space include startups offering AI-based security co-pilots and automated SOC platforms that drastically shorten investigation time and MTTR.

Distributed data lakes & 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:

1. Distributed data lake
   • AI-based tools don’t necessarily rely on a single, monolithic data store. Instead, they can query data where it resides—be it a legacy SIEM, a vendor’s free-tier storage, or an S3 bucket you own.
   • This approach is critical for cost optimization. For instance, some EDR/XDR vendors like CrowdStrike or SentinelOne offer free storage for 1st party data, so it’s economical to keep that data in their native environment. Meanwhile, other logs can be stored in cheaper cloud storage solutions.
2. Flexible, on-demand queries
   • SOC 3.0 enables organizations to “bring the query to the data” rather than forcing all logs into a single expensive repository. This means you can leverage a cost-effective S3 bucket for large volumes of data, while still being able to rapidly query and enrich it in near real-time.
   • Data residency and performance concerns are also addressed by distributing the data in the most logical location—closer to the source, in compliance with local regulations, or in whichever geography is best for cost/performance trade-offs.
3. Avoiding vendor lock-in
   • In SOC 3.0, you’re not locked into a single platform’s storage model. If you can’t afford to store or analyze everything in a vendor’s SIEM, you can still choose to keep it in your own environment at a fraction of the cost—yet still query it on demand when needed.

Conclusion

From a CISO’s vantage point, SOC 3.0 isn’t just a buzzword. It’s 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. Ingesting, understanding, and triaging alerts from any security vendor or data source, Radiant ensures no real threats are missed, cuts response times from days to minutes, and enables analysts to focus on true positive incidents and proactive security. Unlike other AI solutions which are constrained to predefined security use cases, Radiant dynamically addresses all security alerts, eliminating analyst burnout and the inefficiency of switching between multiple tools. Additionally, Radiant delivers affordable, high-performance log management directly from customers’ existing storage, dramatically reducing costs and eliminating vendor lock-in associated with traditional SIEM solutions.

Learn more about the leading AI SOC platform.

About 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.

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