How to Evaluate an Agentic SOC Platform

Following the launch of ChatGPT in late 2022, artificial intelligence tools were quickly integrated into Security Operations Centers (SOCs). First came chatbots in SIEM and EDR platforms that summarized alerts. Later, AI SOC analysts were introduced to take over triage and investigation. That was the point where AI started doing the reasoning instead of describing it.

Attackers have their own AI tooling, which raises the bar on both speed and scale. Agentic SOC platforms have emerged to accelerate the broader security operations lifecycle, encompassing detection engineering, triage, investigation, response, and threat hunting.

Since these platforms rely on a combination of dynamic capabilities rather than a single feature, they cannot be fully evaluated through a standard demo. A comprehensive evaluation should cover ten areas: investigation quality, coverage and integrations, response and remediation, threat hunting, detection engineering, case management, learning and adaptation, transparency, privacy and security, and time to value. This guide outlines each of these areas and explains how to test them during a proof of value.

Investigation depth, quality, and accuracy

The alert investigation process is the foundation of an AI SOC, and the system’s overall effectiveness depends on its quality. There are three measurable properties to consider.

Depth: A thorough investigation asks probing questions, pursues context across every connected tool, and keeps going until nothing relevant is left unchecked, the way an experienced analyst works. Crucially, the depth and thoroughness of these investigations must remain uncompromised even under peak alert volumes, ensuring low-severity or high-volume alerts receive the same level of scrutiny rather than being closed blindly.

Clear conclusions: A production-ready platform must deliver evidence-backed verdicts, benign, malicious, or inconclusive. Evaluate whether the platform provides rigorous proof for its conclusions and explicitly acknowledges when critical telemetry is missing, ensuring transparency rather than resorting to low-confidence guessing.

Inconclusive verdicts carry real value. A platform that forces every alert into benign or malicious will be wrong at the margins, and once analysts find those errors, they stop trusting the malicious verdicts too. A useful inconclusive verdict states what the platform checked, what it could not resolve, and what evidence would settle the question, which gives an analyst somewhere to start instead of a case to redo from scratch.

Measured accuracy: Ask vendors how they measure the accuracy of their determinations continuously, not just during the initial setup. Look for established quality assurance programs that audit live investigations, and review their results. During the evaluation, run the platform alongside your analysts to directly compare their agreement rates.

What to test in practice:

• Replay closed alerts: Provide the system with ten previously resolved alerts, including some that were closed in bulk. Compare the platform’s conclusions and evidence with your analysts’ findings.
• Inject known cases: Introduce a verified true positive and a benign alert that looks similar, and observe if the system correctly handles the investigations differently based on the data.
• Read the full trail: Select a completed investigation and review every question and query the system ran from start to finish.

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Coverage: threats and technology

Coverage involves two main factors: the threats the platform can investigate, and the technology it can reach in order to do so.

On the threat side, start with alert-source breadth. This includes alerts from identity, endpoint, cloud, email, network, and Data Loss Prevention (DLP) systems, as well as any custom detections your team uses. Custom detections are especially important because they address risks specific to your organization that generic models may not recognize. You should ask vendors how quickly they can support new alert types or data sources, as your tool stack is likely to change over time.

On the technology side, focus on integration depth rather than the total number of connectors. Integrations that only retrieve basic enrichment fields will result in limited investigations, regardless of the AI’s reasoning capabilities. Telemetry also sits in more places than it used to: a SIEM, a data lake, cloud-native stores, and the tools themselves. The platform should query data where it lives instead of requiring you to centralize it first, and it should correlate results across those sources within a single investigation. Ask which sources it can query directly today, and how it handles data that sits outside your SIEM. Additionally, the integration should be bidirectional. While reading telemetry is necessary, the ability to write actions back to your systems is what ultimately reduces analyst workload. The platform should be able to automatically close benign alerts in your SIEM, update tickets in your IT Service Management (ITSM) tool, and trigger containment actions using your existing infrastructure.

Response and remediation: automated or human-in-the-loop

In 2024, Gartner identified Security Orchestration, Automation, and Response (SOAR) as an obsolete technology category, noting that its functions were being integrated into broader platforms. As a result, the response and remediation capabilities previously handled by SOAR tools are now a key part of evaluating an agentic SOC platform.

A major distinction is who maintains the underlying logic. Traditional SOAR platforms required security teams to build and update playbooks, which often became difficult to maintain. An agentic SOC platform manages its own investigative and response logic. Therefore, the evaluation should focus on how well you can govern and control its actions, rather than how easily you can build workflows.

Governance capabilities should be thoroughly tested. Response actions, such as disabling user accounts, isolating hosts, or rotating credentials, should have individual scope and permission settings rather than a single on/off switch for automation. Approval requirements should be configurable for different actions and asset types, with a human-in-the-loop requirement set as the default. From there, you can gradually expand automation as the platform demonstrates reliability. You should request to see the audit trail for automated actions and ask how the system handles rollbacks if a change needs to be reversed. Ensure you test the platform’s ability to safely execute actions in your own tools during the proof of value.

Threat hunting you can run as a program

Staffing shortages often prevent SOCs from conducting advanced threat hunting. Therefore, it is important to understand how much expertise and manual work the platform requires. A comprehensive hunting program should include five key capabilities:

• Custom hunts using natural language: Your team should be able to query the environment using everyday language, without needing to learn specific query languages or build complex data pipelines. If setting up a hunt takes several days, it is unlikely to be used frequently.
• A hunt library: The platform should offer pre-built, expert-curated hunts that your team can run, schedule, and reuse, reducing the need to write hunts from scratch.
• Timely hunts for new threats: When a new vulnerability (CVE) or attack campaign is announced, a ready-to-use hunt should be available in the library quickly. Your team should be able to run it on-demand, put it on a recurring schedule, or direct it to run autonomously when the threat is discovered in the wild. This enables your team to verify exposure within minutes without manually authoring a hunt under time constraints.
• Hunts that reach all of your data: Security telemetry now sits in several places at once: a SIEM, a data lake, cloud-native stores, and the tools themselves. A hunt is only as good as the data it can search, so ask which sources the platform can query directly, whether a single hunt can span several of them, and whether it correlates the results into one answer. A platform that requires you to centralize data first turns every hunt into a data engineering project, which is the reason most hunting programs stall.
• Actionable findings: A hunt that simply returns a list of results creates more filtering work for analysts. Suspicious findings should automatically trigger a full investigation that results in a clear verdict. Additionally, effective hunts should be convertible into permanent detection rules to provide ongoing coverage.

Detection engineering: coverage maps, new detections, and tuning

Detection engineering is an important strategic function that is often understaffed. When evaluating a platform, consider these three capabilities:

Evidence-based coverage maps: Ask how the platform builds its MITRE ATT&CK coverage map. A map based simply on available connectors only shows theoretical coverage. A more useful map is built using the actual detections that trigger in your environment, informed by real investigations. It should distinguish between detection rules that are actively working and those that have stopped matching data, as silent rules can create a false sense of security.

Authoring with historical testing: The platform should provide new detection rules written in your SIEM’s native query language, along with backtesting results against your historical data. This allows you to see what the rule would have caught and how many false positives it would have generated before you deploy it. You should also be able to deploy these rules directly into your existing SIEM rather than having to move your data to the vendor’s platform.

Investigation-driven tuning: The platform should help identify noisy detections and suggest tuning or suppression rules based on evidence gathered from past investigations. Any proposed changes should be presented as versioned updates that require your review and approval before they are implemented in production.

Case management built for security operations

Most platforms offer case management features, so you need to look for specifics that align with security operations workflows. The platform should automatically group related alerts from different tools into a single case. For example, an identity alert, an EDR detection, and an email alert linked to the same incident should be consolidated rather than appearing as separate items in the queue. The case should retain the complete timeline and evidence, use status labels that match standard SOC processes, and allow analysts to hand off cases between shifts.

Case data needs to sync bidirectionally with ticketing systems like Jira or ServiceNow to eliminate manual data entry. The platform should also be able to automatically close benign alerts in your SIEM based on rules you define. Finally, the reporting metrics should align with leadership priorities. Many teams are shifting focus from mean time to respond (MTTR) to mean time to contain (MTTC), as containment is the critical point where an attack is stopped. The platform should natively track these metrics and accurately report its own impact.

AI that learns and adapts to your environment

A platform handling daily alerts should improve over time, and it is important to understand how it learns. Ask vendors how you can provide guidance to the system, such as through custom runbooks, direct analyst feedback on investigations, and uploaded documentation like asset lists. You should also be able to provide organizational context, such as identifying critical assets, VIP users, authorized administrative behavior, and scheduled penetration testing. Over time, the platform should adjust to focus on the escalations that matter to your organization and understand your specific naming conventions.

Crucially, the system should not learn or adapt silently. Silent learning can cause model drift and affect the accuracy of the system. Every behavior it learns should be traceable to its source, reviewable by your team, and testable before it is broadly applied. If a system changes its logic invisibly, it may eventually automate errors at a large scale. Capturing explicit guidance also ensures that institutional knowledge is retained even if key analysts leave the team.

Transparency: how it thinks and what it ran

Transparency is essential for building trust in AI-generated alerts. Analysts need to easily verify the platform’s work by reviewing the specific queries it executed and comparing them to what they would have done manually. The platform should provide a complete audit trail for every investigation, showing exactly what questions it asked, what queries it ran, the data it received, and how it used that evidence to reach a conclusion. Analysts should also be able to copy any query and run it directly in the source system to verify the results.

This level of detail is necessary for both internal reviews and external audits. Brief summaries of the AI’s reasoning are insufficient. If you cannot fully reconstruct an investigation using the platform’s logs, you will not be able to defend its conclusions to stakeholders or regulators.

Privacy, security, and architecture

Evaluating an agentic SOC platform is also a vendor security review, as the system will access sensitive data. You should investigate the architecture carefully across four areas:

• Data usage: Ensure there are written guarantees that your data will not be used to train or fine-tune the vendor’s models. Confirm that their agreements with foundational LLM providers include strict zero-data-retention policies. You should also understand what data leaves your environment, in what format, and where it is stored.
• Tenancy and deployment: Look for a single-tenant architecture that isolates your data and processing from other customers. If you are in a regulated industry, ask if the platform can be deployed within your own Virtual Private Cloud (VPC) and if you can control the encryption keys.
• Agent permissions: The AI agents operate as identities within your network. Their access should be restricted following the principle of least privilege. Each action they take should require specific permissions, and every activity must be recorded in an exportable audit log. You must be able to reconstruct every action an agent takes from these logs.
• Exit terms: Clarify what assets you retain if you terminate the contract. Custom detections deployed in your SIEM, operational guidance you provided, and historical case data should remain under your control.

Time to value and operating cost

Ask vendors for a realistic timeline outlining when the platform will begin handling production investigations. Be cautious of timelines that stretch into several months or require extensive pre-training periods. A platform that integrates via APIs with your existing tools should begin delivering actionable investigations within days.

During the evaluation, establish a clear understanding of the costs. This includes the pricing model, any required onboarding services, and a return on investment (ROI) calculation based on your specific alert volumes and staffing levels. The platform’s cost should align logically with the value it provides. A vendor confident in their product should be willing to build an ROI model with you before requesting a contract signature.

How to evaluate an agentic SOC platform in a proof of value

Because most vendor demos appear polished, a structured proof of value (POV) using your own alerts is the best way to evaluate a platform. A standard POV runs for two to four weeks alongside your analysts. If you are evaluating multiple vendors, running them concurrently using the same metrics can be highly effective.

During the POV, test the platform against various alert severities, making sure to include the low-severity alerts your team typically closes in bulk, as automating these provides significant value. Track how often the platform’s determinations align with your senior analysts, check the quality of its evidence, and measure its impact on metrics like mean time to contain and the number of analyst hours saved.

Use this checklist during vendor conversations to cover the essential requirements:

• Which alert sources and custom detections can the platform investigate immediately, and what is the timeline for adding new sources?
• Can you demonstrate a complete investigation, showing every question, query, and result? Can analysts copy and run those queries themselves?
• Will the platform explicitly state when an investigation is inconclusive rather than guessing, and how frequently does this happen?
• How do you measure the accuracy of the platform’s determinations on an ongoing basis, and can we review recent quality assurance results?
• What specific response actions can the platform execute, how are permissions scoped for each, and where are the human approval gates located?
• Can our team create and schedule custom hunts using natural language? What pre-built hunts are currently in the library?
• When a new vulnerability or threat campaign is disclosed, how fast is a targeted hunt available?
• Can findings from hunts and investigations be converted into tested detection rules for our SIEM?
• How does the platform group related alerts into consolidated cases, and does it support bidirectional syncing with our ticketing system?
• How does the platform learn from our playbooks and feedback, and can we review all learned behaviors before they take effect?
• Is the system built on a single-tenant architecture, is our data excluded from model training, and what data and logic do we retain if we leave?
• What specific metrics will we track during the proof of value, and will you help calculate ROI using our organization’s data?

Conducting a thorough evaluation requires more time than a standard demo, but it highlights the operational differences between platforms before you make a commitment.