Your AI Needs the Noise: Why Filtering Kills the Agentic SOC

To deliver on the promise of an autonomous, agentic SOC, modern security organizations must achieve three high-level corporate priorities: significantly reducing Mean Time to Respond (MTTR), enabling faster threat investigation times, and improving overall uptime and service reliability.

But achieving these metrics through automation is running straight into a legacy operational roadblock: strict “noise reduction”.

For decades, cybersecurity leaders have assumed that the key to security efficiency is stripping away technical clutter to prevent human analyst burnout. Although noise reduction remains a pragmatic tool for human-centric triage, it introduces a critical data deficit that actively prevents AI from reaching the high-fidelity reasoning needed to secure the modern enterprise.

Noise Reduction for Humans is Data Starvation for AI Agents

To avoid overwhelming human analysts, many security tools are designed to pre-filter and summarize raw data, removing technical attributes that might give context and sequence. External factors, like storage, cost, and tool performance limitations, also compel organizations to filter data into “highlights,” which can inadvertently remove vital, contextual clues to the ‘who, what, and how’ information needed to definitively identify or respond to a threat.  

This data reduction creates a critical reasoning gap for AI because, unlike a human whose experience can help them occasionally to fill in or leap across holes in data, an AI agent is restricted to the evidence that it receives. Without access to data omitted by filters simply because it is ‘rarely’ useful, AI is prevented from using its massive analytical power to chase down every clue, forcing its logic into probabilistic guesses and “hallucinations” of the “most likely” scenario.

Incomplete data doesn’t just produce missed threats — it forces expensive human analysts back into the loop to fact-check every AI suggestion, effectively negating the ROI of an automation project and potentially increasing the MTTR.

The AI Traffic Explosion, Lateral Movement, and Protocol Complexity

This data deficit turns dangerous as enterprises adopt agentic AI. The resulting explosion of AI traffic introduces hidden complexities that legacy, filtered architectures simply cannot parse, fracturing enterprise visibility in two distinct ways.

The Growing Lateral Movement Haystack

First, internal AI agents generate an unprecedented volume of internal traffic on top of the existing automated workflows and corporate application activity constantly communicating east-west across the infrastructure. This creates an even bigger haystack of activity for cyber attacks to hide in. And because malicious lateral movement looks remarkably similar to automated application traffic, sophisticated threat actors are increasingly hiding their behavior inside this haystack.

But it remains critical to ensure that increasing data accessibility for threat detection and resolution does not impact network stability or performance.  The impact of failing to address this issue goes far beyond simple measurements of user productivity.  In industries such as financial services, for example, a fraction of a second delay in data transmission can disrupt multi-million dollar transactions.

The Chaos of AI ‘Flexibility’

Second, AI has expanded and evolved to be easily accessible from any device, anywhere, with increasing flexibility for organizations to more easily integrate AI within their existing architecture.  This presents security teams with an even greater need to monitor a wide variety of protocols — each with its own communication patterns, authentication behaviors, encryption options, and data formats that it must simultaneously support and secure.

In high-stakes environments like healthcare, for example, a rotating cast of independent specialists, surgeons, anesthesiologists, and others may not be ‘employees’ of a particular facility, but still need to use their preferred devices, cloud services, and AI tools on the network. In such environments, it becomes incumbent on the NOC and SOC to monitor a staggering number of east-west and north-south protocols to truly ensure the security and performance of the network resources that can often mean the difference between life and death.

If your security AI does not understand the distinct, lateral protocols of these varied applications, it will default to false alerts, miss genuine anomalies, or disrupt critical services.

Strengthen AI Agent Reasoning with Network Truth

To shift AI from best-guess to evidence-based decisions, security teams must ensure their AI agents have access to all the data that might be needed to power more definitive decision making and clear response guidance.  The filtering of data and dependence on metadata that once helped reduce the noise for human analysts, must be entirely reconsidered to unleash the full power of AI.

And because the future patterns of enterprise AI traffic remain uncertain, security teams need an observability platform that can see everything, unfiltered, and uninterpreted. While common network monitoring and security tools often limit their focus to a handful of common north-south protocols, true network observability demands the continuous decoding of a vast spectrum of protocols — especially the critical lateral, east-west protocols where threats and unmonitored AI traffic blend together.

By anchoring an agentic SOC in this complete layer of network truth, organizations can eliminate enterprise blind spots, accelerate investigation times, and empower their AI models to make definitive, evidence-based security decisions that reduce breaches, minimize dwell time, and speed response.

Learn how the agentic SOC becomes truly autonomous when you feed your AI agents network truth.