In Strike48’s 2026 survey of 100 security leaders, 84% said their tools cannot access all of their log data. That blind spot matters more once agents work investigations on their own, and more than half of those leaders worry an AI agent will act on incomplete or incorrect data.
This guide explains what agentic AI for security operations does, the four jobs agents take on in a SOC, and how to test a vendor’s claims about what its agents can see and do.
Agentic AI for security operations is software that works a security investigation on its own. Given an alert, an agent decides what evidence it needs, pulls that evidence from logs and tools, reasons about what happened, and proposes or takes the next step.
That independence is what separates agentic AI in cybersecurity from the AI tools most security teams already use. An AI assistant only answers when an analyst asks a question, and a SOAR playbook follows steps someone wrote in advance. An agent plans its own route through the investigation.
Because the agent chooses its own path, its verdict depends on two things the security team controls:
Agentic AI most often gets confused with two tools security teams already use: AI assistants and SOAR playbooks. Both automate part of an investigation, but neither decides what to do next on its own.
AI assistants are the more common source of confusion. Gartner describes them as “the precursor to agentic AI” and notes that they depend on human input: an assistant answers an analyst’s question and then waits for the next one. Gartner calls selling an assistant as an agent “agentwashing,” and buyers will see the agent label on many more products soon. Gartner expects task-specific agents to appear in up to 40% of enterprise applications by the end of 2026, an eightfold jump from 2025.
Where an assistant needs a human at every step, a SOAR playbook needs every step decided in advance. It runs the author’s steps the same way every time, making it dependable until an alert goes somewhere the author never planned for. At that point, the playbook stops or escalates to an analyst, while an agent plans a new query or hands the work to another agent. The table below compares all three approaches.
The last row is the one buyers should weigh most, because an agent cannot plan around evidence in a log source it can't access. Securing the agents themselves is a separate discipline, covered by the OWASP Top 10 for Agentic Applications for 2026. This guide deals with agents that do the security work.
Most SOCs already use AI, but few have made it part of how the SOC runs. The 2026 SANS SOC Survey Insights found that nearly four in five SOCs use AI or ML tools, but just over a third have built those tools into a defined workflow. Agents show the same gap. Gartner finds that fewer than one in five organizations have deployed AI agents, while more than 60% expect to within two years.
Vendors usually pitch agentic AI as the answer to staffing shortfalls and automation gaps, and agents can take real work off analysts, from triage through response. But security leaders rank a different problem higher. Their tools can't reach all of their own log data, and an agent can only work from the logs it can reach. So each use case below starts with the same decision about what each agent is allowed to see.
In a SOC, agents can take on four recurring jobs that follow an alert from triage through response. Each job has its own specialist agent, and before any run, the team decides which log sources the agent can query and which actions need human approval. Strike48’s Pre-Built Agent Packages follow the same split, with one agent for each job.
A triage agent reviews each incoming alert, gathers context from the logs it can reach, and decides whether the alert needs a human. Strike48's SOC Level 1 Agent does this job, and the Strike48 guide to AI SOC agents explains how teams scope it.
When triage escalates an alert, an investigation agent takes over. It groups related alerts into a single incident and works back toward the root cause. Strike48's SOC Level 2 Agent handles this stage, and the Strike48 agentic SOC guide shows how specialist agents hand off investigations.
During the investigation, Strike48's Evidence Collection Agent gathers forensic data. The platform logs every agent action to a real-time audit trail, so analysts already have a full case record when the investigation closes. The Strike48 platform overview shows how this agent layer connects to the underlying data.
Once the cause is clear, a response agent prepares actions such as endpoint isolation and remediation. These actions change the environment, so an analyst approves them before they run. Strike48’s Incident Response Agent works this way, and the post on agentic security makes the broader case for the model.
Picture an agent triaging a suspicious sign-in. It checks identity provider logs and endpoint telemetry, finds nothing unusual, and closes the alert. Meanwhile, the VPN logs show the same account connecting from a second country, and the agent can't access them.
The verdict is wrong, and the agent's report looks just as complete as a correct one. An analyst who goes looking for a log and comes up empty at least knows the investigation has a hole. An agent working from the evidence in front of it can build a coherent story without ever noticing the gap.
In Strike48's 2026 survey of 100 security leaders, 84% said their tools cannot access all of their log data for investigations, and almost two-thirds had seen an investigation stall because the data was out of reach. More than half also worry that an agent will act on incomplete or incorrect data.
Independent research points the same way. In the 2026 SANS SOC Survey, nearly a quarter of cyber leaders named weak enterprise-wide visibility as their single biggest barrier. They ranked it above staffing shortfalls and automation gaps, two problems vendors often pitch agentic AI to solve.
Most security teams already store logs across a SIEM, object storage, and a data lake, and moving everything into one platform means paying to store the same data twice. Federated search takes the other route and queries the logs where they already are.
Strike48's Flexible Data Foundation is built on federated search. Through its search-in-place connectors, agents query Amazon S3, Splunk, Elastic, and data lakes directly, without anyone migrating or copying the data. When a team wants to centralize a particular source for speed or normalization, it can bring it in with AI-assisted collectors.
Strike48 calls this pairing of complete log visibility and agents that act on what they find agentic log intelligence. Flexible Data Foundation determines what an agent can see. The agent layer and its approval gates determine what the agent may do with it.
Vendors often call their agents “autonomous,” a word that means little until a buyer pins it to a defined scale. The timing makes that harder. Gartner’s Hype Cycle for Agentic AI places agentic AI at the Peak of Inflated Expectations, the stage where claims tend to run ahead of what products deliver.
Gartner also offers the scale buyers need. Its four autonomy levels describe how much an agent may do without a human, and they apply to each action an agent takes rather than to the product as a whole.
Gartner's May 2026 release on agent governance defines four autonomy levels. Each level becomes a direct question a buyer can ask a vendor.
If "autonomous" means the product answers an analyst's questions, Gartner's definition makes it an assistant. The levels also apply action by action, so one platform might read logs at Level 1, enrich indicators at Level 4, and isolate endpoints only at Level 3.
Gartner predicts that by 2027, 40% of enterprises will demote or decommission autonomous AI agents because of governance gaps found only after production incidents. Buyers should ask vendors to state the level for each action separately.
Actions that change something in the environment, such as isolating an endpoint or running remediation, need a human gate. In the same release, Gartner warns that Level 3 approvals "can degrade under time pressure or approval fatigue," and an analyst who signs off on every query soon stops reading what they approve.
Strike48 designed its Pre-Built Agent Packages around that problem, and around the fact that language models can hallucinate. Each package runs a team of micro agents, each with one job, its own knowledge graph built with GraphRAG, and a limited set of tools. Strike48 uses Model Context Protocol (MCP) connectors to control which tools each agent can call.
Because each agent is narrow, no single agent can do much on its own. Agents investigate without step-by-step sign-off, analysts approve the critical actions, and the platform records every agent action in an audit trail. In early deployments, mean time to detection (MTTD) dropped below eight minutes, according to Strike48's January 2026 launch post, though that figure reflects those deployments and is not an industry benchmark.
A demo should test visibility first and autonomy second; skipping either leaves the buyer guessing. These five questions put both on the table.
Strike48 answers the first question with search-in-place architecture that allows agents to query logs from any source, and questions 2 through 4 with the ability to build your own agents or customize a set of pre-built agent packages. For question 5, Strike48's Workflow Orchestrator sequences the deterministic steps while agents handle the steps that call for reasoning.
An agentic SOC is a security operations center where AI agents work investigations on their own. They triage alerts, group them into incidents, collect evidence, and prepare response actions. The team decides which logs each agent can query and which of its actions need human approval.
Strike48 builds agentic AI for SOC teams through its Pre-Built Agent Packages, with specialist agents for triage, investigation, evidence collection, and incident response running on Flexible Data Foundation. When comparing vendors, ask which log stores each agent can query and which Gartner autonomy level each action runs at.
According to SANS, nearly four in five SOCs use AI or ML tools, but just over a third have built them into a defined workflow. Agents extend that use beyond answering analyst questions. They triage alerts, group them into incidents, collect evidence, and prepare response actions for human approval.
The main risks are agents acting on incomplete data and attackers manipulating agents into misusing their tools. The OWASP Top 10 for Agentic Applications for 2026 catalogs threats such as Agent Goal Hijack. Teams reduce these risks by limiting each agent's tools through MCP, logging every action, and requiring approval for consequential steps.
Human-in-the-loop means a person approves an agent's work at defined points, usually before actions with real-world consequences. Agents investigate on their own, and an analyst signs off on changes such as endpoint isolation before they happen. Governed autonomy adds regular reviews of audit logs and outcomes to confirm the approval points are in the right places.
