Agentic AI in the SOC: How It Reduces Alert Fatigue and Improves Threat Detection

Alert fatigue is a real threat, both to security teams and the organizations they protect. It occurs when security analysts are constantly overwhelmed by a flood of false positives or low-priority alerts, which often leads to burnout and the risk of overlooking true or high-priority threats.

A recent PwC paper noted that security operations center (SOC) analysts receive anywhere from 10,000 to 50,000 alerts daily, 30-40% of which go unreviewed. The paper also notes how manual triage and context gathering cause response times to be measured in hours or days. As threat actors increasingly use AI to launch more sophisticated threats at scale, traditional SOCs will be unable to defend their organizations against growing breach risks. 

To stay ahead of AI-enabled threats, modern SOCs are turning to agentic AI, which has been proven to accelerate threat analysis, significantly reduce false positives, and contain high-confidence threats at machine speed.

This Q&A with Michael DeNapoli, a Senior Solutions Architect at Pondurance, explores how agentic AI enhances threat detection for SOCs, mitigating breach risk for organizations and protecting security teams against alert fatigue.

Q: Exactly what is agentic AI, and how does it differ from generative AI?

Michael: Agentic AI differs from generative AI in one key way: generative AI mainly responds to prompts, while agentic AI is built to complete tasks. A generative model like ChatGPT takes a question, analyzes it, and returns an answer. An agentic system starts with a goal, then works through the steps on its own—breaking the task into subtasks, gathering what it needs, iterating, and refining its work without waiting for a new prompt each time.

A useful way to think about agentic AI is as a skilled worker. You assign the task, and the worker figures out how to get it done. For example, if asked to estimate a customer’s daily log ingestion rate, a generative AI can provide an answer based on the initial inputs. But, if properly trained for that task, agentic AI can dig deeper on its own. It can review server types, log sources, and prior examples until it reaches a more complete conclusion.

Q: How can agentic AI improve threat detection for SOC analysts?

Michael: Agentic AI takes on the high-volume, repeatable work that consumes most analyst time. In practice, known threats account for roughly 80% of activity. While tools like SIEM and SOAR help, they still leave a large share of alerts requiring investigation.

Agentic AI handles that remaining volume by analyzing alerts the way a SOC analyst would—reviewing historical logs, correlating events, and anticipating likely next steps—often in two or three minutes instead of the 10–20 minutes it might take a human. This dramatically increases processing speed without sacrificing quality.

Just as important, it reduces analyst fatigue. Repetitive alerts are a primary driver of burnout. By offloading that work, agentic AI frees human analysts to focus on complex, novel threats that require human ingenuity and creativity.

The result is a more balanced SOC: faster investigation at scale, stronger coverage, and better use of human expertise where it matters most.

Q: How does agentic AI enhance threat detection through contextual insights, as opposed to simply generating alerts? 

Michael: In a traditional SOC, a SIEM flags potential issues based on correlation rules—but these alerts only indicate potential threat activity, not whether a real threat exists.

Agentic AI picks up where alerts leave off. It analyzes what triggered the alert, then iteratively builds context—reviewing historical data, prior decisions, and related activity within the environment. It asks: Has this happened before? Was it benign or malicious? What additional signals should be present if this is a true threat?

By continuously enriching alerts with context, it determines whether they are false positives, contained incidents, or active threats requiring escalation. It also quantifies risk and potential impact, drawing on prior events and threat intelligence—mirroring how a human analyst investigates, but completing the process much faster.

Because multiple agents operate in parallel, this approach scales efficiently across dozens of environments at once—handling growing data volumes without overloading analysts or requiring constant headcount expansion.

Q: How does—and how will—agentic AI affect the work of a SOC analyst?

Michael: Agentic AI will fundamentally change the day-to-day work of a SOC analyst by removing much of the repetitive, low-value effort that drives alert fatigue. Today, analysts spend a significant portion of their time reviewing the same types of alerts over and over—work that is necessary, but often tedious and mentally draining.

By offloading that repetitive triage to agentic AI, analysts can focus on the alerts that truly require human attention—complex, ambiguous, or novel threats. This shift allows them to spend more time on engaging investigations without constantly interrupting that work to handle routine cases.

The impact is twofold. First, burnout is reduced because analysts are no longer overwhelmed by volume or stuck in monotonous workflows. Second, performance improves, because analysts can concentrate on high-priority threats without distraction—reducing the likelihood that something important is missed.

Importantly, this isn’t about replacing SOC analysts. It’s about enabling them to do the work humans are best at. The goal is a more sustainable, engaging role—one that helps retain skilled professionals and keeps them invested in the work over the long term.

Discover Kanati™, Agentic AI SOC from Pondurance

Pondurance Kanati is the first agentic AI SOC designed for autonomous operations within a managed detection and response (MDR) service. It replaces alert-driven, error-prone workflows with a coordinated system of AI agents that operate continuously throughout the full threat lifecycle.  

With Kanati, security teams receive lightning-fast threat analysis, a dramatic reduction in false positives, and sub-second containment for high-confidence threats. Initial performance measurements of Kanati demonstrate:

• 90% faster threat analysis with AI-powered confidence rating and containment

• 80% reduction in false positive tickets

• 10x improvement in contextual enrichment and correlation of threats

• 100% coverage of alerts resulting in all alerts investigated with full analytical rigor

Using agentic AI, Kanati helps security operations combat threats that move at machine-speed, providing fast, accurate detection, analysis and containment that can make the difference between a minor inconvenience and a successful ransomware attack or data breach.

Visit pondurance.com or email kanati@pondurance.com for more information or to request a demo.