AI SOC or traditional SOC: which is the winning formula to best power your cybersecurity strategy?

Artificial Intelligence Security Operations Centers (AI SOCs) are leveraging machine learning, automation, and predictive analytics to revolutionize cybersecurity by detecting and responding to threats significantly more quickly and accurately than traditional SOCs can.

While conventional SOCs rely on workflow-based automation, manual threat hunting, and rules-based correlation, AI SOC Agents powering AI SOCs continuously learn from data patterns, adapt to evolving attack vectors, and reduce false positives.

Integrating real-time intelligence, advanced anomaly detection, and automated workflows, AI SOCs enhance threat visibility, accelerate incident response, and scale security operations with 24/7 monitoring capabilities and resource-efficient architectures, enabling organizations to outpace today’s sophisticated cyber threats.

Knowing all this, how should you approach your SOC architecture, and how does this AI-first approach contrast with traditional approaches?

I will discuss both approaches, using real-world data to help guide your decision into what’s best for your organization.

Core Differences Between Traditional SOC and AI SOC

While both traditional SOCs and AI SOCs use SIEMs, EDRs, and equivalents to detect threats, their similarity ends when it comes to what happens once the alerts are generated. SIEMs and other detection tools generate anywhere from dozens to thousands of alerts each day depending on the size of the organization.

The vast majority, over 90%, deserve no further action – being either outright false positives, or true positives with low impact. This results in traditional SOCs, powered by humans, continually getting overloaded. It’s no surprise that analysts always leave unfinished work when signing off on their shift. What is worse is that every hour analysts spend on such noisy alerts is an hour taken away from tackling real threats.

Traditional SOCs require building workflows as response templates. This creates a significant amount of busywork for engineering – both at the time of creation and then to maintain it.

An AI SOC, on the other hand, autonomously learns and doesn’t require workflows or playbooks. This results in security teams being freed up to tackle threats, not playbooks.

With manual triage in traditional SOCs, analysts spend more than 40% of their time investigating low-priority events. In some traditional SOCs, automation engineers write automation on SOARs or other low-code platforms to filter out some of the noise. But such IT automation does not survive changes in the environment, or new alert types.

AI SOC Tools are different in that they can inject automation into this chaos. They use Artificial Intelligence to deliver critical benefits, such as filtering 90% of the false positives via behavioral analysis.

AI SOCs also prioritize threats in SOC investigations using risk scores with evidence and context lake information. Finally, they can auto-resolve 60% of Tier-1 incidents in under 3 minutes.

When Traditional SOCs Shine and When They Don’t

Traditional SOCs have been around for many years. There is an ecosystem of analysts, providers, tools, and processes that were created around the traditional SOC architecture.

A traditional SOC can be the only choice in some environments, such as regulated environments with a high friction to change, or organizations that have strict internal processes to follow.

For mainstream organizations in contrast, traditional SOCs cannot handle the growing demands on security teams. A traditional SOC architecture cannot keep up with growing alert volumes, and any alerts not investigated are a risk.

It is not fast enough to manage novel attacks, as a result it pushes that onus onto the analysts. It cannot evolve fast enough to understand new alert types, or to adapt to changing tools.

Finally, traditional SOCs are challenged when it comes to threat response. While SIEMs detect threats, they don’t mitigate them. Other tools are needed to execute on blocking malicious IP addresses, quarantining machines, etc.

Fully addressing any risk often requires more than these products, it requires knowledge specific to each business that is either only in users’ heads or in unstructured notes that machines cannot automate.

AI SOC Tools Prove to be the Perfect Complement for Traditional SOC

AI SOC tools address the shortcomings of traditional SOCs. AI-driven SOC platforms combine the best of three knowledge centers.

They leverage AI models trained on large amounts of security data for aspects that are common across all SOCs.

They combine that with the AI SOC vendor’s knowledge base of latest security data. And the best AI SOCs extend that further by incorporating context provided by users to customize responses for that user.

With these techniques, they suppress noise, reduce alert fatigue, and respond to threats at machine speed.

Leveraging SIEM and AI SOC Synergy

AI SOCs do not displace the “issue finders” in your traditional SOC, such as SIEMs, EDRs, CDRs, ITDRs, XDRs, and email security solutions.

AI SOCs complement them by investigating the issues they find, filtering the false positives and issues with low impact, and responding to them.

An AI SOC is the first responder organizations need in an era of constantly growing alert volumes, so that human analysts can stay focused on the alerts that matter.

With human oversite still in the mix, and AI handling the basic SOC activities, analysts are free to focus on strategic tasks like threat hunting and playbook refinement.

Three Steps to Future-Proofing Your SOC

Organizations as a first step need to audit their existing SOC metrics for trends. This will help them identify areas of need, such as a growing gap in the number of uninvestigated alerts, slower-than-desired response times, or high rates of false positives and their sources.

Next, they should pilot AI SOC tools on alerts that are currently not being investigated to measure the benefits that can be realized, and to understand how the analyst’s role changes with an AI SOC as the first responder.

Finally, measuring ROI such as tracking MTTR, escalation rates, and storage costs over six months will help uncover if an AI SOC is right for their organization.

What is the winning formula? Let SIEM handle logs; let AI SOC handle triaging, investigation, and response; and let humans focus on the more strategic decisions.

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