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Why alert volume becomes an investigation problem
A security operations center (SOC) can receive more signals than its analysts have time to investigate. The challenge is not simply having many alerts: it is deciding which ones merit attention, assembling enough context to assess them, and acting before a real threat causes harm. When routine work and false positives consume capacity, meaningful signals can wait or go uninvestigated.
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SANS Institute’s 2026 First Look, AI-Human Collaboration in Modern SOCs, illustrates the scale with a hypothetical mid-sized organization receiving 4,500 alerts a day: spending 10 minutes on each would require nearly 94 full-time analysts working without breaks. That is a workload illustration, not a staffing recommendation.
The same SANS First Look summarizes findings from the SANS 2025 SOC Survey: two-thirds of SOC teams could not keep pace with alert volume, and nearly half of alerts went uninvestigated. It also reports that analysts spend 80% to 90% of their time chasing false positives from traditional SIEM and SOAR tools. These are report-specific findings; the available summary does not establish that the figures apply to every SOC.
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What can agentic capabilities do about alert noise?
“Filtering” need not mean deleting alerts. The research literature distinguishes three ways to reduce alert fatigue: automate suitable routine work, augment human decisions with additional information, and support collaboration between analysts and AI on complex or novel cases. These are useful categories for assessing a workflow, not a guarantee that a particular system supports each one. The distinction is described in the 2025 ACM Computing Surveys article “Alert Fatigue in Security Operations Centres: Research Challenges and Opportunities”.
| Approach | Role in the workflow | What to verify |
|---|---|---|
| Automation | Handle defined, routine tasks at scale, such as applying an established screening or routing process. | Which tasks are in scope, what happens when the system is uncertain, and whether the result can be reviewed. |
| Augmentation | Add information that helps an analyst assess an alert and make a decision. | Whether the supporting context is relevant, traceable and available to the analyst at decision time. |
| Human-AI collaboration | Assist with investigation when a case is complex, unusual or needs several reasoning steps. | Where the analyst takes over, how the system escalates uncertainty, and which actions require approval. |
These approaches can make alerts easier to triage or investigate, but the cited taxonomy does not establish that a specific agent will classify threats accurately or reduce a SOC’s workload. A system that summarizes an alert is not necessarily one that should close it; a system that can propose a response is not necessarily authorized to carry it out.
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Where an agent fits in an alert investigation
Assess an agent against the actual sequence of work rather than the label “agentic.” For each stage, identify what the system is expected to do, what evidence it needs, and who remains accountable for the outcome.
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- Screen and prioritize: Determine whether the system is meant to rank alerts, group related signals, or route cases. Ask what the ranking means operationally and how analysts can challenge it.
- Gather context: Establish which telemetry and identity or environment information the task depends on, whether the system can access it, and how it indicates missing or stale context. These are evaluation questions, not capabilities established for any particular product by the sources cited here.
- Support investigation: Check whether the system presents a useful account of its findings and the evidence behind them, rather than only a conclusion. Define how an analyst can request more information or take over.
- Recommend or take action: Separate suggestions from executed response actions. Set explicit boundaries for what can happen without approval, how exceptions reach a human, and how an action can be reviewed afterward.
The appropriate division of work depends on the task and its consequences. Routine, bounded steps may be candidates for automation; ambiguous or high-impact decisions call for stronger analyst involvement. Treat that division as an operating-model choice to test, not as a promise implied by an AI feature.
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Why the agent itself needs security and governance
An agent that can access security data or participate in operational workflows introduces a security and governance concern of its own. NIST’s 2026 analysis of responses to its request for information on AI-agent security says respondents broadly agreed that fundamental cybersecurity practices remain relevant, but need adaptation for agents. That supports a deliberate review of how an agent is deployed and controlled; it does not, on the evidence summarized here, establish a detailed set of technical controls.
On April 30, 2026, the NSA announced that it had joined the Australian Signals Directorate’s Australian Cyber Security Centre and other organizations in releasing guidance titled Careful Adoption of Agentic AI Services. Read the joint-guidance announcement as a primary reference when planning adoption. Do not assume a tool is safe to deploy simply because it is used in a security operation: assess the agent’s own access, oversight and failure handling in the context of the intended workflow.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate an agent for a SOC
There is no standardized product scorecard established by the sources cited here. Use the following questions to structure an evaluation around the work the team needs done:
- Workflow fit: Is the system screening, adding context, assisting investigation or taking response actions? Is that the bottleneck the SOC needs to address?
- Data and context: Can it access the telemetry and relevant identity or environment information its assigned task requires? How does it handle gaps?
- Human control: Can analysts review, override or escalate its work? Are approval boundaries clear, particularly for actions with operational consequences?
- Auditability: Can the team inspect what the system did and the information it relied on? The sources support asking about auditability, but do not establish a specific product’s logging or explanation features.
- Security and governance: What needs to change in the organization’s existing security practices to account for the agent and its role?
- Evidence of outcomes: What is measured, under what conditions, and by whom? Distinguish vendor claims, survey findings, participant perceptions and independent evaluations.
Before deployment, define a limited workflow and a human review path, then decide what evidence would justify expanding the system’s role. Measure operational results against a baseline that matters to the SOC—such as investigation time, alert disposition quality or the share of cases receiving timely review—rather than treating a high volume of system activity as proof of value. These are evaluation measures to choose for the pilot, not outcomes demonstrated by the sources cited here.
What the available benchmark evidence does—and does not—show
The Cloud Security Alliance’s 2025 Beyond the Hype: A Benchmark Study of AI Agents in the SOC reports that 94% of participants viewed AI in cybersecurity more positively after hands-on evaluation. That figure measures a change in participant perception, not detection accuracy, investigation quality or reduced workload. It is a reason to distinguish hands-on experience from assumptions about AI—not a performance guarantee.
Another SANS publication, the 2026 SOC Report, describes a benchmark of 444 practitioners and 69 cyber leaders. It reports that 74% of cyber leaders apply threat intelligence to security operations and threat hunting, while 26% use it in budget and spending decisions. Those figures provide context on security-operations practices; they do not show that agentic AI reduces alert noise.
For a SOC considering agentic capabilities, the defensible case is therefore specific: test whether a defined system helps with a defined workflow, verify that analysts retain appropriate oversight, and judge results by measured operational outcomes. The sources cited here explain why the problem matters and how to frame the evaluation; they do not establish that agents universally solve alert overload.
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