AI adoption is becoming widespread among security operations teams. Prophet Security's State of AI in Security Operations 2026 survey of more than 250 CISOs, SOC leaders and practitioners found that 40% of teams use AI daily, while another 56% are testing it. Only 4% said they had no plans to adopt the technology.
For teams still evaluating AI, the key question is whether their SOC is ready to deploy agents. Readiness has two dimensions: the operational problems an AI agent could address and the technical and governance foundations required to investigate alerts accurately.
AI agents are most useful in SOCs dealing with delayed managed detection and response escalations, missing context, limited provider coverage, alert backlogs and weak visibility into investigation decisions. Internal teams may also struggle to answer quickly whether they are affected by a new vulnerability or campaign, investigate lower-severity alerts, tune noisy detections, conduct threat hunts and maintain coverage outside normal working hours.
The survey found that 60% of respondents had seen an ignored or missed alert later become a serious issue, such as a breach or service disruption. It also reported that 28% of alerts are never investigated. These pressures leave proactive hunting and detection engineering competing with day-to-day alert handling.
The SOC maturity model described by Prophet Security places alert management at the base, followed by detection coverage, threat awareness, proactive hunting and posture improvements. AI agents generally begin with alert investigation, although newer platforms can also support threat hunting and detection engineering. The time returned from routine work can then be redirected to activities higher up the model.
Technical readiness starts with access. An agent can only investigate data it can query, so relevant integrations may include the SIEM, EDR, identity provider, cloud platforms, email and important SaaS applications. It also needs readable context such as asset criticality, user roles, VIP lists, business units and known-benign activity. Monitoring the health and timeliness of logging pipelines is equally important, because missing or delayed data can make an investigation appear complete when critical evidence is absent.
A practical test is to review five recent incidents and list every query a senior analyst used to resolve them. Any query the proposed agent cannot execute identifies an access or integration gap. Data handling must also be examined: 44% of survey respondents cited data privacy and AI model training as a major obstacle. Organizations should ask vendors whether customer data is used to train models and where that data is processed.
A pilot also needs a written definition of a completed investigation. That standard should specify the evidence required for common cases, including phishing and impossible-travel alerts, and what is sufficient to classify an alert as benign. Organizations should record baseline metrics such as the percentage of alerts investigated, mean time to investigate, time to the first review and the rate at which closed alerts are reopened.
The report states that a thorough investigation takes an average of 75 minutes and that alerts often wait nearly an hour before being opened. It also says that up to 40% of organizations have disabled some detections because they lacked the capacity to review them. A documented list of those detections can help identify where additional automated investigation may restore coverage.
Governance must distinguish between investigation and response. In the survey, 57% of teams required human review before every AI decision to close an alert. For response actions, 44% used AI to recommend actions for people to perform, while 30% allowed autonomous remediation for low-risk cases. No respondent reported granting full, unsupervised autonomy.
Before a pilot, organizations should define which actions always require approval, who can expand the agent's permissions, where its findings will appear and who is responsible for reviewing mistakes. Starting with read-only investigative access and widening permissions gradually follows the same cautious approach used when introducing a new human analyst.
The proposed readiness checklist separates the need for AI from the ability to deploy it. A strong case for adoption may exist when alerts arrive late, providers lack context, investigations are delayed, detections have been disabled or difficult cases depend on only a few senior analysts. Deployment readiness, meanwhile, depends on API access, available asset and user context, written investigation standards, approval rules, log-source monitoring, baseline performance data, a record of disabled detections and a plan for using the time returned to the team.
If the SOC is not ready, the recommended preparation is largely configuration and documentation work. Teams can begin by closing the access gaps identified through the five-incident review, documenting standards for their highest-volume alert types and running an agent alongside analysts for several weeks. Analyst overrides should be recorded as information about missing context or incorrect behavior. Permissions can then be expanded one category of action at a time.
Building an internal agent is another option, but the survey indicates significant risks. Among AI users, 72% had tried building internal AI tools, and 46% of those projects were abandoned, failed to reach production or were replaced by a commercial product. Beyond a prototype, teams must maintain integrations, address API changes and rate limits, manage model deprecations, repeat evaluations and prompt-injection testing, and preserve audit trails explaining alert decisions.
Prophet Security describes its own Prophet AI platform as supporting investigation, threat hunting and detection engineering. Its SOC Analyst agent queries security tools and records the questions, queries and evidence behind decisions, while response actions remain subject to customer-defined permissions and default human approval. Its Threat Hunter component is designed to run searches for newly disclosed threats and scheduled investigations, and its Detection Engineer component uses investigation results and coverage gaps to propose and backtest detections before approval.
According to the company, findings from these functions feed one another: investigations can reveal noisy or missing detections, hunts can examine those gaps and their results can inform new detection rules. Prophet Security also states that customer data does not train its models and that its data plane can run inside a customer's own virtual private cloud.
Sources: The Hacker News.