How to Run a Proof of Value (POV) for AI SOC Analysts

Why is a POV essential for AI SOC evaluation?

When evaluating an AI SOC analyst platform, a proof of value (POV) is your best opportunity to see if the technology lives up to its promise in your real-world environment. In a world where you are blasted by alerts and “AI Agents” are thrown around like a buzzword, it’s critical to filter out mere imposters. The key is designing a POV that doesn’t just check boxes, but actually tests the platform’s ability to triage, investigate, and explain alerts at the level your team requires.

Based on what we’ve seen from the most effective customer POVs, here’s a simple, structured way to run a high-signal evaluation that gives you a true read on how the AI SOC analyst performs head-to-head against your own team.

How do I run an effective AI SOC POV?

1. Connect 1-3 Real Alert Sources

Select a few key systems that regularly generate alerts, such as your EDR (e.g., CrowdStrike), identity provider (e.g., Okta), or SIEM. For example, with Prophet AI, connecting via API takes less than 30 minutes to get up and running with live data.

2. Let the AI Handle Live Alerts

Over the course of the POV week, allow the platform to receive and investigate real-time alerts as they come in. Don't use synthetic demos or hand-picked samples. Do use red-teaming exericses to simulate true positive threats.

3. Sample Diverse Alerts for H2H Testing

Ask your SOC manager to randomly identify 50-200 alerts across different types (e.g., impossible travel, privilege escalation, command & control, MFA abuse). Let both the human analyst and the AI investigate the same alert stream.

4. Compare Investigations Side-by-Side

Once both investigations are complete, pull the human analyst’s notes and verdict. Then compare them directly to the AI analyst’s investigation. Focus on:

• Accuracy of the determination
• Depth of context and enrichment
• Correct identification of the root cause
• Clarity and structure of the narrative
  

5. Measure Key Metrics

To quantify performance, track:

• Dwell time (how long the threat went undetected)
• Time-to-investigate (how quickly the alert was triaged and resolved)
• Analyst effort required (hands-on time or steps)
• Escalation rate (what percentage of alerts escalated to humans)
• See a full list of metrics here
  

This gives you an objective, apples-to-apples benchmark between human and AI, and a clear signal on whether the platform is ready to handle real operational load.

What are common pitfalls when running the POV?

Don’t let your POV fall short. Watch out for these mistakes:

• Relying on synthetic or curated alerts: Only real, unfiltered data will show how the platform performs in your environment. Test environments usually lack the context the AI tool can use to make decisions about the alerts, affecting the effectiveness of the test. While you should primarily rely on real data, it is also important to test edge cases. As mentioned previously, red-teaming exercises is a great way to simulate true positives in live environments.
• Ignoring integration complexity: Make sure the AI SOC analyst can connect quickly and seamlessly with your key systems and workflows. Time-to-value is important because delays in deployment usually signals an architectural issue that will continue to impact the AI's effectiveness.
• Overlooking false positives: Track how often the AI flags benign activity as threats. High false positives can increase analyst workload and impact the team's trust in the platform.
• Assuming full automation: AI is best as a force multiplier, not a complete replacement for human analysts. Evaluate how well it supports your team and how to best incorporate it in your processes.
• Neglecting explainability: If you can’t understand how the AI reached its conclusions, you will quickly lose trust in the AI and risk blind spots in your security posture.

By following these best practices, you’ll get a true sense of how the platform will work for your team and avoid costly surprises down the line.

Ready to run your own AI SOC Analyst POV?

Running a thorough POV for an AI SOC platform is your best way to cut through the hype and see how the technology truly performs in your environment. By focusing on real-world data, side-by-side comparisons, and key metrics (all while avoiding common pitfalls), you’ll gain an objective view of whether the platform can handle your operational needs and support your team. Ultimately, a well-designed POV empowers you to make smarter decisions, improve your security posture, and unlock the full potential of AI in your SOC. 

Ready to get started? Use this guide as your roadmap to a successful evaluation and checkout 11 Questions You Must Ask When Evaluating AI SOC Analysts.

Better yet, request a demo from Prophet Security to see how the leading AI SOC Analyst performs in your environment.

Frequently asked questions (FAQ)

Why is a POV important when evaluating an AI SOC analyst?

A POV is important when evaluating an AI SOC analyst because it shows how the platform performs with your real data, workflows, and alert types. It helps separate marketing claims from real-world performance and reveals whether the AI can triage, investigate, and explain alerts with the accuracy and depth your team needs.

What are the steps to run a successful AI SOC analyst POV?

To run a successful AI SOC analyst POV, connect 1–3 live alert sources, allow the AI to handle real-time alerts, and select diverse alerts for head-to-head testing. Then compare results and measure performance using key metrics like investigation time, false positives, and analyst effort.

What data sources should I connect during an AI SOC POV?

During an AI SOC POV, you should connect real, high-signal alert sources like your endpoint detection tool, identity provider, or SIEM. These sources generate relevant alerts and allow the AI to operate under the same conditions your analysts face daily.

How do you evaluate whether an AI SOC analyst improves human analyst workflows?

To evaluate whether an AI SOC analyst improves human analyst workflows, observe how it reduces manual effort, accelerates investigations, and enhances decision-making during the POV. Key indicators include fewer escalations, shorter investigation times, and improved clarity in case handoffs. The goal is to measure augmentation, not replacement.

What metrics should I use to measure the effectiveness of an AI SOC POV?

You should measure metrics such as dwell time, time to investigate, analyst effort, and escalation rate. These metrics provide a clear, objective comparison of performance and help determine whether the AI analyst meaningfully improves SOC efficiency.

What are common mistakes to avoid during an AI SOC analyst POV?

Common mistakes include using synthetic data, ignoring integration challenges, overlooking false positives, assuming the AI is fully autonomous, and not requiring explainability. These issues can distort results and reduce trust in the platform.

Why is explainability important in an AI SOC analyst POV?

Explainability is important in an AI SOC analyst POV because it ensures your team understands how the AI reached its conclusions. Without clear reasoning, analysts may mistrust the results and hesitate to act on the AI’s findings.