How AI-powered SOCs Can Boost Business for MSSPs

COMMENTARY: MSSPs face a tough situation. They need to prove the value of what they deliver to clients while protecting them against increasingly sophisticated threats, driven more and more by the use of AI. MSSPs must adapt while managing resource constraints and increasing client expectations. When they don’t reevaluate their approach to managing their security operation centers (SOCs), they often have difficulty maintaining quality, staying competitive, and increasing their margins as they scale to service customers.

This is where AI changes the game for service providers. It helps them keep pace in the rapidly evolving threat landscape, and it fundamentally transforms how they operate. AI doesn’t replace people. It frees them up to focus on what matters most. Done right, AI complements human judgment, speeding up decisions and improving outcomes. When SOC teams and AI work together, the results are more than the sum of their parts — it’s a “1 + 1 = 5” effect.

Striking the Right Balance of Efficiency and Effectiveness

SOC excellence comes from striking a sweet spot between effectiveness and efficiency to avoid situations where operations burn through resources. Traditional methods, such as hiring more analysts, only make the process more expensive and often result in an uncomfortable compromise: either reduce incident coverage to match available bandwidth (which can decrease effectiveness) or increase resources to keep up with incidents and alerts (which can reduce efficiency). That’s why a growing number of MSSPs are moving towards AI-powered SOCs.

Take DTX, for example. At first, this service provider considered adding more analysts to scale its managed SOC business, but that was counter to its goals of efficient business expansion. By adding AI to its SOC, DTX increased the accuracy, speed and efficiency of handling incidents, giving analysts more time to focus on more complex incident resolution. That shift helped DTX expand its security portfolio and increase retention with more precise, customer-specific incident response.

Improving Incident Response, Cutting Through the Noise

During security incidents, every minute counts. AI-powered SOCs increase response efficiency by streamlining the incident detection and response lifecycle. To deliver optimal results, AI models should continuously learn and refine based on the environment-specific knowledge they are ingesting, so they can detect, contain, and even remediate threats with minimal human input, speeding up response and reducing risk. In a service provider’s multi-tenant environment, this efficiency becomes a force multiplier that helps teams manage more incidents, more consistently and accurately with the same resources, directly improving margins and competitiveness.

AI also helps reduce alert fatigue. Without context, analysts can’t always tell real threats from noise, which means they must investigate all alerts equally. This can lead to missed signals, inaccurate and inconsistent handling, and significant inefficiencies. But if you combine the right mix of AI techniques with human oversight and institutional knowledge, SOC teams are able to handle multi-tier incidents — from basic to complex — on a large scale. By leveraging AI to investigate and remove alerts before they get to the analysts, teams can increase the quality, efficiency and accuracy of detections — and focus on real threats earlier and better.

Paving the Way for Proactive Defense

Some managed security businesses thrive while others constantly put out fires. It's not about speeding up the firefighting — it’s about preventing the fire in the first place. Many SOC teams equate faster response times with success, but reacting to threats after damage has already occurred is not progress. After working with countless security teams, one pattern stands out: the best SOC leaders don’t just close tickets — they build smarter systems that improve over time. They implement detections and tools that surface threats earlier in the kill chain, enabling them to contain incidents closer to initial access.

These high-performing teams shift the focus from speed to quality. They use AI to enhance analysts’ capabilities, not replace them. They break down silos between detection and response, continuously capture institutional knowledge, and refine detections rather than simply closing false positives, with the goal of reducing false positives. Ultimately, the business doesn’t measure SOC success by alert volume or response speed — it cares about proactive risk reduction. 

Driving Growth Without Sacrificing Quality

It’s hard to scale when your foundation isn’t built to last. MSSPs need infrastructure to support sustainable growth. With multi-tenancy capabilities, MSSPs can manage every client from a single platform. And AI makes it possible to tailor incident investigation and response to each customer. Build once, personalize often. That’s how you scale contracts without scaling costs.

But this isn’t a one-size-fits-all approach. Accuracy, consistency, and speed improve dramatically when AI systems continuously ingest tenant-specific institutional knowledge — from environment details to investigation behaviors and risk tolerance — and apply it across the incident lifecycle. By shadowing analysts and adapting to each customer’s environment, AI delivers precise, contextual responses at scale. Multi-tier (Tier 1, Tier 2, Tier 3) coverage ensures complex incidents are analyzed, triaged, and resolved at scale, while keeping humans in the loop.

But MSSPs can’t prove value if they can’t measure impact. AI-powered SOC platforms measure key performance indicators (KPIs) like detection effectiveness and risk reduction with data specific to each tenant and generate reports that help MSSPs win trust and renewals.

Implementing AI for SOC With a Phased Approach

Successful AI implementation doesn’t have to be an all-or-nothing approach. A gradual rollout of the technology helps SecOps teams build confidence in the technology, reduces disruption, and ensures that AI becomes a natural part of day-to-day operations.

MSSPs should start by assessing their current capabilities, testing AI in specific use cases such as impossible travel or lateral movement, and expanding over time as results come in. This is the crawl-walk-run approach security leaders want. Start gradually, prove ROI, then scale as desired.

AI systems should integrate smoothly with existing technologies like SIEM (security information and event management), EDR (endpoint detection and response), IAM (identity access management), cloud, threat intelligence, and ticketing systems. The goal is to complement the existing stack, not complicate it. Mapping incidents to specific use cases helps ensure AI delivers relevant, actionable results aligned with existing processes.

AI for SOC as a Strategic Advantage

Integrating AI into SOC operations isn’t just a tech upgrade — it’s a business necessity. MSSPs that embrace AI with a clear strategy will be best positioned to scale, stay efficient, stay competitive, and outpace modern threats. As AI shapes threats and defenses, those who wield it wisely will define the future of cybersecurity.

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