11 Questions You Must Ask When Evaluating AI SOC Analysts

Why evaluate AI SOC Analysts?

As the pressure on security operations teams continues to mount – with increasing alert volumes, changing threats, and a persistent talent shortage – many organizations are rethinking how their SOC operates. If you're exploring AI-driven SOC platforms, you're likely looking for a way to increase efficiency, reduce response times, and finally give your analysts the breathing room to focus on high-impact work.

Whether you're replacing legacy tools, augmenting your team with AI, or starting fresh with an AI-first SOC strategy, evaluating the right platform is critical. But with the explosion of vendors claiming AI capabilities, knowing what to look for and what to ask, can make or break your selection process.

In this post, we'll cover 11 key questions to ask both yourself and the vendors you're considering in order to create a shortlist of top AI SOC vendors. These questions will help you cut through the noise and focus on the capabilities that truly matter. By the end, you’ll know how to best evaluate solutions such as Prophet AI.

What are the 11 key questions to ask?

1. Is the AI analyst's investigation deterministic or non-deterministic, and what does that mean for consistency and trust?

• What to look for: Whether the AI produces the same result given the same input (deterministic), or if outputs can vary (non-deterministic, e.g., LLM-based). Ask how consistency is ensured in either case.
• Why it matters: Consistent investigations make outcomes auditable, repeatable, and safer to automate.
• Impact: A well-controlled non-deterministic system boosts analyst trust, reduces rework, and enables scalable automation when it comes to investigating various alerts. None-the-less, the AI SOC analyst should provide a clear classification of alerts.

2. Does the platform work autonomously or as a copilot?

• What to look for: Clarity on whether the AI makes decisions independently (triage, investigations, response), offers recommendations, or both, and whether this is configurable.
• Why it matters: You need to align the platform’s autonomy with your team’s maturity, risk tolerance, and workload.
• Impact: A good fit here leads to faster outcomes without sacrificing control. Too much or too little autonomy could create operational risk or limited ROI.

3. What types of data and tools does the platform integrate with out-of-the-box, and how long does it take to operationalize?

• What to look for: Prebuilt integrations (e.g., with CrowdStrike, Okta, Splunk), ease of setup (API, agentless, no-code), and time to first signal.
• Why it matters: Delayed integrations or custom work drain time and create friction during POV and rollout.
• Impact: The faster you can connect high-quality data, the sooner the platform can demonstrate value and improve coverage.

4. How accurate are the AI investigations at identifying true positives-and how is accuracy measured?

• What to look for: Ask the vendor how they measure and validate true positive rates across different alert types. Look for benchmarks, confidence scoring, and how often the AI agrees with or improves upon human analyst determinations.
• Why it matters: Accuracy is foundational. If the AI misclassifies threats, it introduces risk. If it correctly identifies true positives more consistently than your team, it can meaningfully improve SOC metrics and reduce risk.
• Impact: High true positive accuracy means fewer missed threats, better signal-to-noise ratio, and more confidence in moving toward autonomous or semi-autonomous triage.

5. How explainable are the platform’s decisions and actions?

• What to look for: Whether the AI shows its reasoning, evidence, and decision paths in a way a human can verify and understand.
• Why it matters: Security analysts must trust AI outputs to act on them, especially in incident response or escalation.
• Impact: High explainability builds trust, accelerates AI onboarding, and reduces second-guessing of AI-driven conclusions.

6. How does the platform handle false positives and learning over time?

• What to look for: Feedback loops, analyst input capture, automated tuning, or retraining mechanisms to reduce repetitive noise.
• Why it matters: False positives kill productivity and erode trust in any detection or response system.
• Impact: A platform that improves with usage reduces alert fatigue, increases signal-to-noise ratio, and lightens analyst workload.

7. What is the total cost of ownership (TCO), including setup and maintenance?

• What to look for: Beyond licensing, ask about onboarding effort, tuning needs, staffing, professional services, and ongoing upkeep.
• Why it matters: Hidden costs can negate perceived ROI or make a platform unusable for small teams.
• Impact: Understanding TCO upfront ensures a realistic business case and better long-term operational planning.

8. What security, compliance, and data privacy controls are in place?

• What to look for: Encryption, data locality, tenant isolation, RBAC, logging, audit trails, and relevant certifications (SOC 2, ISO 27001, etc.).
• Why it matters: AI platforms often handle sensitive telemetry and incident data-especially in regulated industries.
• Impact: Strong controls reduce risk exposure, accelerate procurement approval, and ensure alignment with compliance requirements.

9. What is the process for incorporating our internal threat intelligence and proprietary context into the platform’s analysis?

• What to look for: Ability to ingest custom IOCs, business context, VIP lists, internal risk models, and threat feeds.
• Why it matters: AI needs context to make accurate, environment-aware decisions-not just generic threat logic.
• Impact: The more the platform understands your business, the more relevant and high-fidelity its investigations become.

10. Can the AI analyst autonomously recommend or execute response actions, and if so, how is oversight and control maintained by my team?

• What to look for: Scope of autonomous response, approval workflows, rollback options, and governance mechanisms.
  Why it matters: Autonomy can accelerate response times, but needs safe boundaries to prevent mistakes or overreach.
• Impact: Proper controls enable confidence in automation and reduce response times without introducing new risk.

11. What ensures that investigations are up-to-date and best-in-class over time?

• What to look for: Model updates, new playbooks, threat coverage expansions, vendor intelligence pipelines, and expert oversight.
• Why it matters: Threats evolve daily and your AI platform needs to evolve with them or it risks becoming obsolete.
• Impact: A platform with a strong update and curation process ensures you're always covered against emerging attack techniques and with latest triage and investigation best practices.

See the long-list of considerations here.

Conclusion

Choosing an AI SOC platform isn’t just about flashy demos or bold claims, it’s about finding a system that fits your team, your data, and your security mission. By asking the right questions and running a structured, outcomes-driven POV, you’ll cut through the noise and get a clear picture of whether the platform can truly deliver value.

Ultimately, the best AI analyst isn’t just fast or autonomous, it’s one your team can trust, tune, and grow with over time. Go in with a plan, test with real alerts, and hold vendors accountable to your standards.

Go ahead and put Prophet to the test by scheduling a demo today!

Frequently Asked Questions

1. What is an AI SOC analyst and how is it different from traditional SOC tooling?

‍An AI SOC analyst is software that uses large language models to triage and investigate alerts, replacing static rules and playbooks. Many SOCs measure analyst throughput per shift and see clear increases once the AI is active.

2. Why does deterministic versus non deterministic investigation matter when selecting an AI SOC platform?‍

Deterministic investigation returns the same result every time from the same data, while non deterministic approaches can vary. Consistency metrics such as repeatability rate show whether variance is under control.

3. How do I measure the accuracy of an AI SOC analyst during a proof of value?‍

Measure true positives, false positives and confidence scores during a two week proof of value. Aim for a true positive agreement rate above ninety percent with human analysts.

4. What hidden costs should be included in total cost of ownership for an AI SOC platform?‍

Total cost of ownership covers license, deployment, integrations, cloud compute, analyst validation time and future maintenance. Build a three year cost per alert model to expose hidden expenses.

5. How much autonomy should an AI SOC analyst have, and how is oversight maintained?‍

Set autonomy to match risk tolerance, from advisory mode through one click actions to full containment with rollback controls. Track the percentage of alerts processed without human touch to quantify success.

6. Which security and compliance controls are table stakes for an AI SOC platform?‍

Essential controls include tenant isolation, encryption in transit and at rest, role based access, detailed audit logs and SOC 2 or ISO certificates. Verify that every data access event appears in immutable logs.

7. How can I run a realistic proof of value for Prophet AI or any AI SOC vendor?‍

Run a proof of value by connecting live data, defining success metrics and comparing AI output with analyst work for at least ten business days. Use daily dashboards to watch mean time to investigate trend downward.

8. How do I quantify the efficiency gains delivered by an AI SOC analyst?‍

Quantify efficiency gains by baselining dwell time, mean time to investigate, mean time to resolve and alerts per analyst, then comparing results after deployment. Dashboards should show percentage changes for each metric day by day.