The Agentic SOC market, which Gartner refers to as AI SOC agents, is rapidly evolving. Over the past year and a half, numerous startups have emerged, all with the goal of revolutionizing how security teams manage alert handling, research, and responses.

Their typical proposition involves implementing an AI agent to lessen your alert overload and enable your analysts to concentrate on more crucial tasks.

While this holds some truth, Gartner’s recent analysis indicates that most organizations assessing these tools are either posing the incorrect questions or not asking enough.

In a recent Gartner report, Validate the Promises of AI SOC Agents With These Key Questions, analysts Craig Lawson and Andrew Davies provide a structured assessment framework for cybersecurity leaders considering AI SOC agent deployments.

Their key takeaway: by 2028, while 70% of large SOCs are expected to test AI agents for Tier 1 and Tier 2 operations, only 15% will see significant improvements without a well-defined evaluation process. A complimentary copy of the complete report is available for download here.

This discrepancy between adoption and results suggests that the challenge for most security teams isn’t about whether to implement AI in the SOC, but rather how to distinguish genuine operational enhancements from marketing hype.

Gartner suggests assessing the following key areas, highlighting the importance of each to ensure success.

While seemingly obvious, Gartner emphasizes a nuanced approach. The initial question shouldn’t be “what can this tool do?” but rather “which current SOC functions are repetitive, time-consuming, and offer little value in improving threat detection, investigation, and response?”

A tool might exhibit impressive capabilities in a demo but address workflows your team has already streamlined. The evaluation should prioritize your operational challenges, not the vendor’s feature set.

Gartner also recommends determining the specific tasks best suited for enhancement and whether the solution is designed for particular SOC roles. A platform focused on alert triage and investigation will differ from one designed for creating if/then workflow rules.

Understanding this scope upfront prevents mismatched expectations later.

Volume metrics can be deceptive. Processing 10,000 alerts monthly means little if the investigation quality drops or true threats are missed.

Gartner stresses that evaluations should focus on enhancements in TDIR metrics and outcomes: average detection time, average response time, and reduced false positives. The report notes that qualitative results are also important.

Is the tool boosting analyst satisfaction? Is it improving performance, not just speeding it up?

The report also emphasizes that the ultimate objective should be mean time to contain (MTTC), as containment is where risk is minimized. Discussions that only focus on triage speed without addressing investigation quality and containment timelines are missing a key element.

Request benchmarks from similar environments. Determine if these benchmarks were gathered during a proof of concept or during sustained production use, as the numbers often vary.

This market is still developing. Gartner’s report highlights a market with numerous new companies using various methods and design philosophies. This diversity encourages innovation, but cybersecurity leaders must honestly assess vendor risk.

The report recommends inquiring about a solution’s initial availability, current customer base, funding, and financial prospects. It also advises accepting that acquisitions are likely and treating it as a third-party vendor management risk.

Pricing structures also require examination. Some AI SOC agents are priced based on alert volume, while others use data volume or token usage.

The cost of processing large volumes of alerts with an LLM-based system can escalate unexpectedly, and Gartner advises buyers to understand how costs perform under load.

A more subtle section of Gartner’s framework covers analyst enhancement and training. The primary concern isn’t just faster AI triage; speed is expected with AI but about whether the technology improves human expertise in the long run. 

Gartner advises asking about training resources included with the tool, whether the AI offers learning opportunities for analysts, and whether it aids with detection engineering.

This addresses an often-overlooked tension in the AI SOC market: if the AI handles all the investigative work, will junior analysts ever develop the skills to become senior analysts?

The best implementations balance this by presenting their reasoning transparently, providing analysts with a detailed investigation to review instead of a simple verdict.

For instance, Prophet Security structures its investigations to display every query, data source, and analytical step the AI took to reach a conclusion. This provides junior analysts with a blueprint for how experienced investigators approach an alert, rather than a simple yes-or-no answer to approve.

Gartner draws a distinction between “human in the loop” and “human on the loop” models. The first requires human approval for each action, while the second grants greater autonomy to the AI, with human oversight at a strategic level.

Neither model is inherently superior; the appropriate choice depends on an organization’s risk tolerance, regulatory requirements, and the AI system’s maturity.

Gartner’s framework encourages buyers to ask: What actions can the agent perform autonomously, and which require human approval? How are guardrails enforced for critical decisions? Can autonomy levels be customized based on task type or risk level?

The report also highlights the importance of fail-safe mechanisms. When an AI agent encounters ambiguity, it should escalate rather than act. This design philosophy is vital because it dictates how the system behaves at the edges, where real damage can occur.

Integration claims are easily made but challenging to verify. Gartner’s framework advises buyers to assess native integration depth across SIEM, EDR, SOAR, and identity platforms instead of accepting a logo wall at face value.

The report questions if the solution requires data centralization or can operate as a plug-and-play solution.

The distinction between a tool that needs centralized data and one that can query across multiple security data sources is operationally significant for organizations with complex or hybrid architectures.

Transparency is a key evaluation criterion. Gartner asks: How does the solution provide explanations for decisions and actions taken by the AI agent? Are there human-readable audit trails for every automated action? How is sensitive data handled, and what controls prevent model misuse or data leakage?

These are not optional for regulated industries but requirements. Explainability is essential because it affects analyst trust, even for organizations without strict compliance mandates.

An AI agent providing a verdict without showing its process puts the analyst in a difficult position, forcing them to either accept the conclusion without verification or redo the investigation.

Some vendors have adopted a “glass box” approach, documenting every query, data source, and the reasoning used to reach a conclusion.

Prophet Security’s investigation timeline allows analysts to trace each conclusion back to the underlying evidence rather than relying on a confidence score.

The report emphasizes the need for clear explainability, secure data handling, and mechanisms for human feedback that influence the system’s future behavior.

Gartner’s framework is valuable because it avoids premature endorsements in a developing market. It cautions against overreliance on marketing claims, notes that full autonomy is not feasible today, and highlights hidden costs in pricing and integration.

For security leaders evaluating AI SOC agents, the takeaway is that the technology has potential to reduce investigation burden, improve response times, and extend coverage to handle large alert volumes. Realizing this potential requires a structured, results-oriented evaluation that many buying processes lack.

Prophet Security built its agentic AI SOC platform using principles Gartner outlines: transparent investigations, integration across tools without centralizing data, and a human-on-the-loop model.

The platform is designed to enhance teams, not replace them, completing investigations quickly while providing analysts with the necessary context for confident decisions.

For organizations seeking to apply Gartner’s evaluation framework, the complete report, Validate the Promises of AI SOC Agents With These Key Questions, is available for download.

Get the report here to access all seven evaluation categories.

Sponsored and written by Prophet Security.