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From Sledgehammer to Scalpel: Rethinking Noise in the SOC

From Sledgehammer to Scalpel: Rethinking Noise in the SOC
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Low signal-to-noise ratios are slowing you down—here’s how to turn noise into clarity.

Today network architectures have grown more sophisticated and complex, spanning multi-cloud environments, diverse vendors, and distributed infrastructures. Even as cyberattacks surge, IT and SecOps leaders must contend with rising demands from new applications, workloads, and the challenges of managing a highly dispersed workforce.

In this evolving landscape, some teams still measure success by the volume of security alerts their systems generate, and this leads to an unintended consequence: overwhelming noise that obscures critical threats.

Security teams are drowning in information overload—and it’s only getting worse. Around 77% of leaders report that their attack surface has widened in the past couple of years and it’s becoming more difficult to detect attacks. They face a barrage of false alarms and alert triggers that risk swamping security teams and causing costly mistakes.

Consider the cyberattack on Target ’s Point of Sale (POS) systems, where early alerts went unnoticed. Like many security teams today, they faced the immense challenge of isolating critical signals amid a constant stream of notifications.

Most businesses are missing the point of the problem: rather than focusing on noise and alerts, we believe the key metric to focus on is the Signal to Noise Ratio (SNR). If this figure is low, you may be in trouble.

Shifting the focus from alerts to Signal-to-Noise Ratio

The Signal-to-Noise Ratio (SNR) is a concept borrowed from fields like sound engineering and imaging, where it measures the clarity of meaningful signals amidst irrelevant static. In cybersecurity, the same principle holds: signal represents genuine, high-value alerts that require immediate action, while noise is everything else that distracts teams and obscures critical threats.

When SNR is low, it means the noise is drowning out critical signals, putting your organization at risk in several ways:

  • Threat identification: Low SNR makes it challenging for teams to separate critical threats from irrelevant noise. With limited capacity, some genuine threats will inevitably slip through.
  • Response: When analysts spend too much time filtering noise, response times suffer.
  • Reviewing: Poor SNR overwhelms teams with alerts, wasting time on irrelevant ones while real threats go undetected, potentially increasing the organization’s exposure to breaches.
  • Backlogs: As alert queues grow, analysts often deprioritize low-severity alerts. These alerts, however, can mask early indicators of larger attacks, creating blind spots that leave your organization vulnerable.

Limited visibility and an inability to ingest all relevant data further weaken an organization’s security posture. Without complete data and context, teams struggle to prioritize the most important alerts, spending valuable time on low-level noise while critical threats go unnoticed.

On the other hand, a high SNR reduces noise and offers clearer signals. While this is usually beneficial, it can paradoxically increase the risk of missing critical threats if too much data is filtered out. The issue is similar to the “silence” you hear after a loud bang. In truth, it’s not silence, it's a protective measure that temporarily dampens your ability to hear.

In other words, just because you can’t hear anything doesn’t mean there’s nothing to worry about.

Redefining Noise: A New Perspective

So, how do you avoid following Target’s example?

While most businesses are understandably worried about noise, we feel that they are looking at it in the wrong way.

Noise is often seen as the enemy—a relentless distraction that overwhelms security teams. From this perspective, it becomes something to eliminate entirely. But, this approach misses a key insight: noise, when contextualized, can be a strategic advantage.

What many teams currently perceive as irrelevant noise could hold critical clues. When correlated with other indicators—such as anomalous activity patterns or unusual behaviors—this noise could help uncover vulnerabilities and prevent future attacks.

Embrace the noise! Instead of hushing the noise, the goal should be to amplify the signals that matter most and contextualize the rest.

From Noise to Context: More Data, Smarter Security

Let us consider Target’s breach again. The problem was not a lack of alerts, it was a lack of context. The system triggered alerts early, but without context, those alerts were misinterpreted and dismissed.

If those alerts had been correlated with unusual patterns or prioritized based on risk, the security team could have acted in a timely fashion and avoided the costly fallout.

This example highlights the critical role of contextualized noise. It’s not about fewer alerts—it’s about smarter ones.

Businesses must stop relying on outdated, reactive measures that lead to inefficiencies, false positives, and missed threats. They need to seek solutions that contextualize data and signals to prioritize high-fidelity alerts.

The Role of AI in Contextualizing Noise, at Scale

While contextualizing noise can unlock critical insights, achieving this at scale is beyond human capacity. Many SOCs rely on human analysts to sift through alerts—a process that’s neither scalable nor efficient in today’s threat landscape.

As attack surfaces expand and alert volumes grow, manual methods can’t keep up. This is where Artificial Intelligence (AI) reduces missed threats and accelerates responses.

By analyzing vast amounts of data in real time, AI leverages advanced algorithms to:

  • Identify connections between seemingly unrelated alerts.
  • Highlight early indicators of complex attacks.
  • Prioritize threats based on impact, enabling faster, more informed decisions.

With AI, SOCs can shift from reactive alert management to proactive threat mitigation. For Target, this could have meant correlating early alerts with behavioral anomalies, flagging the breach before it escalated.

But while AI is transformative, it’s only part of the solution. To fully operationalize contextualized noise, SOCs need an engineering-first approach—a dynamic framework that adapts to changing threat landscapes.

Moving to an Engineering-Led Approach

An engineering-first approach ensures that AI-driven insights are seamlessly integrated into detection and response workflows. It ensures that all aspects—AI, data processing, and response strategies—work together cohesively.

Nentenrich’s Adaptive Managed Detection and Response (MDR) approach, for example, is based on a continuous loop of data engineering, detection engineering, and response engineering. Designed to be adaptive rather than one-size-fits-all, it proactively adapts itself to your company’s context and needs.

This approach to real-time signal analytics promises to transform the threat detection landscape with the ability to provide real-time contextual analytics that can cover multiple dimensions of security data, analyze from specific viewpoints, and uncover patterns and connections that could easily be missed by human security teams.

This isn’t just about efficiency—it’s about resilience. An engineering-driven SOC isn’t overwhelmed by noise; it thrives on it, using every piece of data to strengthen defenses.

Conclusion: From Sledgehammer to Scalpel

To address modern cybersecurity challenges, SOC teams need to rethink their approach. Current SOC strategies are akin to a sledgehammer, with security teams playing whack-a-mole with alerts. However, much like a scalpel, an engineering led approach to analyzing all the ‘noise,’ can enable proactive threat detection and response for security teams to respond with surgical precision.

Adaptive tools, AI, and an engineering-first approach can carve a flood of alerts into a precise, steady stream of actionable insights. They can help teams detect and respond to threats faster, reducing risk and improving security posture.

In cybersecurity, noise isn’t the problem—the problem is how we’re listening to it.

 

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