AI-enabled incident triage hands autonomous agents the investigation of a security alert. The agents classify the alert, pull evidence from the relevant log sources, and reach a verdict before an analyst opens the case. The analyst reviews finished work and approves any action with real-world consequences, such as isolating an endpoint. A tool that only sorts or scores alerts leaves every investigation with the analyst.
AI-enabled incident triage is a SOC workflow where software agents investigate alerts from start to finish. Vendors use the "AI triage" label loosely, though. A copilot ranks alerts and leaves the investigation to the analyst. A triage agent runs the investigation and gives the analyst a verdict.
Most teams have the copilot version. According to a survey of 282 security leaders, more than half of teams use AI copilots in production. Those teams still leave 40% of alerts uninvestigated.
Agentic triage covers the first two tiers of SOC work. We split that work between our SOC Level 1 and Level 2 Agents. The Level 1 Agent sorts real threats from false positives. The Level 2 Agent investigates the real threats and adds threat intelligence and user history to each case.
Triage runs in six stages, each with one owner. Agents handle the first five stages, from intake to verdict. An analyst owns the last stage, where findings turn into network action.
When one model handles a broad prompt like "investigate this alert," it has too much room to make things up (called hallucination). Instead, we split the job into small tasks. A coordinator agent hands each task to a specialist, such as checking IPs against threat feeds, and combines the results.
Each specialist works from a knowledge graph of the organization's assets and users, which tells it what normal looks like in that environment. MCP connectors limit which tools it can call. The catch is upkeep, since the security team has to keep that graph current.
In Strike48’s May 2026 survey of 100 security leaders, 84% said their tools can't reach all their log data. Nearly two-thirds had at least one investigation stall because they couldn't reach the data. An agent can't find an attacker in logs nobody collects.
CrowdStrike researchers found in their 2025 Global Threat Report that 79% of detections were malware-free. These attacks rely on stolen credentials and legitimate admin tools, which leave little trace in the logs most SIEMs prioritize. If a team never collects the logs that would show them, no alert fires.
Cost drives much of the gap. Ingestion-based SIEMs charge by the gigabyte, so teams drop expensive sources like cloud audit trails. Four in five leaders we surveyed cited the cost of keeping data searchable as a major concern.
We built our data foundation on search-in-place connectors. They query logs where they already live, in S3, Splunk, or a data lake, so teams don't have to move or drop a source to cover it.
In our early deployments, mean time to detection fell below eight minutes. Agents also caught active phishing campaigns that legacy SIEM tools had missed. Because the agents run around the clock, analysts don't start the day with a backlog.
Gurucul's 2025 research found that nearly three-quarters of SOC teams call their triage automation a success. Yet only 9% of security leaders fully trust AI decisions. The table measures detection results, so leaders can judge agents on outcomes rather than how much they automate.
Gartner analysts expect organizations to cancel more than two in five agentic AI projects by the end of 2027, citing unclear value and weak risk controls. When we asked leaders what worries them about agents, three fears came up. Agents might act harmfully, make up answers, or work from incomplete data. Each fear points to a question for the vendor.
An agent can only judge what it can see. If some logs are missing, its verdicts cover only part of the environment, and they still sound confident. Buyers should ask which sources the system can query today and which ones need a migration first. Our Flexible Data Foundation reaches logs in S3, Splunk, and Elastic without moving them.
Reviewers can't trust a verdict they can't check. Every finding should link back to the specific log entries the agent used. We log every agent action, including each tool call, so analysts and auditors can follow the full chain of evidence.
Hallucination worries more than two-thirds of the leaders we surveyed. A good answer names a specific control, such as narrow agent tasks, a knowledge graph of the environment, or limits on tool access. "Advanced AI" is not a control, so buyers should press the vendor for details.
Agents should investigate on their own, since that is where the analyst hours go. Locking accounts or isolating endpoints should still need a person's sign-off. Security teams should place approval gates at key points before those actions.
Teams should roll out triage in stages and give agents more freedom only as results support it. Granting full autonomy on day one lets untested agents take real actions.
Most SOC teams already know they can't keep up. Alerts arrive faster than analysts can clear them, and two in five never get investigated. Log sources the budget couldn't cover stay dark, and CrowdStrike puts average adversary breakout time at 48 minutes. An attacker who moves that fast is deep in the network before anyone reaches the alert. Another copilot won't fix that, because an analyst still has to work every case.
Our SOC agents investigate alerts across logs where they already live, and analysts approve the actions that matter.
What is an AI triage system?
An AI triage system uses agents to classify, investigate, and escalate security alerts. Agents gather evidence from relevant logs and reach a verdict before an analyst reviews the case. The analyst gets a finished investigation instead of a raw alert.
What is AI incident response?
AI incident response uses agents throughout the incident, from triage and investigation to containment. Triage comes first, where agents work out what happened. Response actions such as account lockouts come next, and in a governed setup a human approves each one before it runs.
How does AI triage differ from traditional SIEM alert management?
A SIEM collects logs and raises alerts, then leaves the analyst to work out what happened. AI-enabled triage does that work first. The analyst receives a finding that covers what happened, why it matters, the evidence, and the recommended action.
Why do most AI triage implementations fail to reduce analyst workload?
Two problems keep the work on the analyst. Many "AI triage" tools are copilots, so analysts still investigate every alert, just with better tooling. Others run on partial log coverage, so cases involving unseen sources go back to analysts to check by hand.
How do AI agents prevent hallucinations during security investigations?
Developers give each agent a narrow job, a knowledge graph of the environment, and limited tool access. A small, well-defined task leaves the agent little room to invent an answer. These controls lower the risk without removing it, which is why a human still approves every high-impact action.
What metrics indicate AI-enabled triage is working?
Security teams track MTTD, the Tier 1 false positive rate, alerts investigated per analyst, and the alert-to-incident ratio. Together they show whether agents are investigating and correlating or just sorting alerts. The table above lists targets for each.
These articles cover related topics in more depth.
