How to Evaluate AI SOC Analysts

Security operations teams face a daunting challenge: triaging and investigating an ever increasing volume of security alerts generated by a growing number of security tools. Each alert demands attention, as missing a single alert could have devastating consequences. Yet, most alerts turn out to be false positives, creating a cycle of manual, repetitive tasks that drain time and resources. 

These pressures often lead to alert fatigue, forcing security operations teams to make tough trade-offs: ignoring certain alerts, disabling detections, or simply struggling to keep up. The result is increased risk, inefficiency, and gaps in coverage that adversaries are quick to exploit.

Security Orchestration, Automation, and Response (SOAR) solutions promised to automate alert triage and investigation, but often require extensive playbook development and customization. Many organizations struggle to fully implement or maintain these complex tools, leading to patchwork automation and continued manual work.

Fighting AI with AI

Now, adversaries are raising the stakes by leveraging Artificial Intelligence (AI) to launch more sophisticated and targeted attacks, further straining already overburdened SOCs.

For example, phishing emails generated by AI mimic human writing with uncanny accuracy, making them almost indistinguishable from legitimate communications. 40% of Business Email Compromise (BEC) emails are AI-generated, according to Vipre Security Group, with losses from AI-driven BEC alone expected to reach $11.7bn by 2027.

It’s no wonder most organizations (60%) feel unprepared to properly defend themselves against this new breed of AI-powered threats. 

With human analysts struggling to keep up, SOCs face alert overload, delayed threat responses, and critical gaps in coverage that adversaries exploit. Fighting fire with fire—leveraging AI to combat AI—has shifted from a strategic advantage to an operational necessity.

AI SOC Analysts provide the ability to process vast amounts of data, detect patterns invisible to human analysts, and investigate and respond to threats in real time. Their planning, reasoning, adaptability, and scalability enable organizations to match and even outpace attackers. 

Cutting through the hype of AI SOC Analysts

Over the past year, we have seen several incumbent security vendors and emerging startups introduce AI offerings to streamline security operations. Every vendor has a compelling demo and each promises to be the silver bullet. This abundance of choice can be overwhelming, and the fear of investing in the wrong solution is real. 

Buyers grapple with questions of efficacy, integration, and ultimately, the return on investment. Does this tool really work or is it just a pretty demo? Will this tool truly alleviate the burden on my team? Will it integrate seamlessly with our existing infrastructure? And most importantly, will it deliver on its promises of maximizing our team’s efficiency while providing a strategic advantage in the face of growing number of threats?

This blog will guide you through seven key evaluation factors – (1) Coverage, (2) Accuracy, (3) Quality, (4) Workflow Integration, (5) Customizability, (6) Time to Value, and (7) Data Privacy and Security – to help you make an informed decision that empowers your SecOps team to operate more efficiently and effectively, ultimately strengthening your overall security posture.

For an introduction to AI SOC Analysts, check out our recent blog. 

Coverage

Coverage refers to the types of security alerts – identity, cloud, endpoint, email, network, data loss prevention – that the AI SOC Analyst can triage, investigate and remediate.

From a security perspective, a high coverage rate ensures that the AI SOC Analyst has been trained on a broad set of security alerts and attack vectors. A broader coverage also improves the ROI of the AI SOC Analyst and reduces the time human analysts spend on the manual and tedious tasks to triage and investigate each alert. 

Accuracy

Accuracy measures how often the AI SOC Analyst is able to come to the right determination (i.e. true positive or false positive) for an alert. 

Accuracy is critical for several reasons:

• High accuracy builds confidence in the AI system, encouraging analysts to rely on automated assessments. 
• Reducing the number of false positives a human analyst has to review prevents wasted time on benign alerts, reducing alert fatigue.
• Ensuring a low false negative rate ensures that threats are not missed.

Evaluate the AI SOC Analyst's performance under realistic SOC conditions by simulating true positives, false positives and false negatives. This approach reveals whether it can reliably investigate complex, ambiguous alerts in an operational environment. 

Quality

The depth, completeness, and explainability of the AI SOC Analyst's findings drives the quality of its investigations. 

A high quality investigation results in better accuracy and builds trust within an organization. Investigation quality depends on the ability of the AI SOC Analyst to ask all the questions that an expert analyst would ask, gather, correlate and analyze all the evidence, and piece together a clear narrative of benign or malicious behavior. You should be able to clearly understand all the steps the AI SOC Analyst took and why it arrived at its conclusions, ensuring transparency and fostering confidence in its results.

Workflow integration

How well an AI SOC Analyst integrates into your existing security tools and workflows is critical to its ROI. 

An AI SOC Analyst that quickly integrates into existing workflows allows teams to leverage AI capabilities without a steep learning curve or significant process changes and the accompanying costs of retraining and system overhauls. 

The depth and functionality of integrations with security tools in your environment matter even more. Superficial integrations that fail to align with how workflow tools are actually used within an organization can create significant operational hurdles, rendering the AI SOC Analyst ineffective from the start.

Customizability

Every organization’s security needs are unique and constantly evolving. For this reason, customizability is a critical factor in ensuring the successful adoption and long-term effectiveness of an AI SOC Analyst. 

This includes the ability to tailor the steps of its analysis, incorporate specific data sources, adjust scoring and impact assessments, etc., based on organizational context. This enables the AI SOC Analyst to continuously learn, improve, and scale with the organization, driving greater trust and efficiency across security operations.

Time to value

SOC teams are often wary of automation solutions due to past experiences with lengthy implementations and underwhelming results. An effective AI SOC Analyst should demonstrate rapid deployment and immediate impact on key metrics like alert dwell time and mean time to investigate (MTTI). 

Delays in deployment or achieving results may indicate architectural issues or a mismatch with your SOC's needs, leading to potential disruptions and hindering adoption. Additionally, be cautious of solutions that require extensive human intervention or pre-training periods, as these can significantly impact the time to value and overall effectiveness.

Data privacy and security

Many organizations are concerned about data privacy and security when using LLMs. An AI SOC Analyst must make sure that they don’t use an enterprise’s data to train or fine tune models. 

Trust is paramount when adopting AI solutions, especially in security operations where sensitive and highly confidential data is at stake. Without robust privacy safeguards, skepticism around AI adoption can grow, with organizations questioning whether their data might be exposed, misused, or shared without consent

33 questions to ask when evaluating AI SOC Analysts

Coverage

1. Use cases: Which types of security alerts - cloud, identity, endpoint, email, network, data loss prevention - is the AI SOC Analyst able to triage, investigate and remediate? 

2. Custom detections: Can the AI SOC Analyst investigate custom detections to address unique threats or organization-specific scenarios?

3. New integrations: How quickly can the AI SOC Analyst support new alert types or new security data sources to maintain consistent coverage and performance as the number of integrated tools, alerts, and data sources increases?

4. Threat hunting: Does the AI SOC Analyst support proactive threat hunting by enabling natural language queries across all available data sources? Does this facilitate hypothesis-driven searches to identify patterns or anomalies that may indicate hidden threats or suspicious behaviors?

Accuracy

5. True positive and false positive identification: How accurate is the AI SOC Analyst in  investigating alerts and identifying them as true positives and false positives? Are there any mechanisms in place to review false positives and measure their efficacy?

6. False positive reduction: How effectively does the AI SOC Analyst reduce the number of false positives that a human analyst has to triage and investigate manually?

7. Preventing hallucinations: What safeguards are in place to minimize the risk of hallucinations and bias from LLMs?

8. Decision-making with limited context: How does the AI SOC Analyst approach alerts when important contextual data is unavailable? What methods are used to prioritize, escalate, or defer these cases to maintain operational integrity?

Quality

9. Depth of investigation: What are the questions that the AI SOC Analyst answers as part of an investigation, and how thoroughly does it emulate an expert human analyst in uncovering every relevant detail?

10. Alert deduplication: How does the AI SOC Analyst identify and group duplicate alerts from the same event? Provide examples of its impact on reducing noise and streamlining investigations.

11. Incident view: How does the AI SOC Analyst group alerts from multiple security tools into an incident?

12. Follow-up questions: Does the AI SOC Analyst allow analysts to ask follow-up questions within an alert or across alerts using a natural language interface? 

13. Transparency of decision-making: How does the AI SOC Analyst present the reasoning behind its determination and severity of each alert? Are the steps and data points leading to each decision clearly outlined?

14. Measurable impact: What metrics are used to measure the effectiveness of the AI SOC Analyst and do they align with your organization SOC priorities?

15. Enhancing detection engineering: Can the AI SOC Analyst identify high-volume noisy alerts, gaps in coverage, or areas requiring tuning? Does it uncover insights to help security engineering teams improve detection strategies?

16. Remediation/response action: How does the AI SOC Analyst enable faster resolution of incidents? What specific features or outputs reduce manual response efforts and improve response times?

Integration

17. Quality of workflow integrations: How does the AI SOC Analyst ensure seamless integration with workflow tools like Slack or Microsoft Teams? Can it initiate workflows such as notifying relevant teams, escalating incidents directly from the platform, or invoking SOAR playbooks for remediation workflows? 

18. Quality of case management integrations: How does the AI SOC Analyst integrate with case management tools like Jira or ServiceNow? Does it allow analysts to create, update, and close tickets directly within the platform?

19. API capabilities: Does the AI SOC Analyst offer APIs to enable customization and seamless integration with custom tools?

Customizability 

20. Leveraging existing runbooks: Does the AI SOC Analyst provide the option to leverage an organization’s existing runbooks for its investigations?

21. Tailored investigations: Can customers tailor the AI SOC Analyst’s investigation process, including defining steps, selecting data sources, and adjusting analysis, scoring, and impact assessments, to align with their specific needs?

22. Granular feedback: Can users provide feedback at specific investigation steps or decision points?

23. Global feedback: Can feedback be applied broadly across investigations to refine the AI SOC Analyst’s analysis and decision-making?

24. Impact of Feedback: How quickly and effectively does the system incorporate user feedback to improve future investigations?

Time to value

25. Integrations with tools and platforms: Which security tools and platforms does the AI SOC Analyst integrate with out-of-the-box, and how deep are the integrations?

26. Deployment timeline: What is the average time required to deploy the AI SOC Analyst and begin processing alerts in a production environment?

27. Initial results timeline: How quickly after deployment can the AI SOC Analyst provide measurable results in terms of reducing alert dwell time, improving mean time to investigate, or streamlining triage processes.

28. Level of autonomy: How much work does a human analyst need to do alongside the AI SOC Analyst in order to triage and investigate an alert? Does the AI SOC Analyst require pre-built playbooks? Can it support multiple levels of autonomy supporting both analyst-in-the-loop and autonomous investigations?

29. Performance under testing/POV conditions: How well does the AI SOC Analyst perform during red team exercises, simulated attacks, or a proof-of-value (POV) assessment?

Data privacy and security

30. Use of customer LLMs: Does the AI SOC Analyst support the use of organization-specific LLM models?

31. Deployment options: Does the AI SOC Analyst offer the ability to run within a customer-managed environment? How can customers retain control over their data?

32. AI output accuracy: How does the AI SOC Analyst vendor ensure the AI’s output is accurate, unbiased, and explainable? 

33. Model training on customer data: Can the vendor provide explicit guarantees that customer raw data will not be used for model training without prior consent? What contractual or policy commitments are in place to prevent customer-specific data from being shared with third parties or upstream/downstream AI model providers?

By focusing on coverage, accuracy, quality, workflow integration, customizability, time-to-value, and data privacy and security, organizations can identify solutions that deliver real impact. Use these questions as a foundation for your evaluation process to ensure you choose a solution that not only strengthens your defenses but also empowers your SecOps team to work more efficiently and keep up with the growing volume of cyber threats.

At Prophet Security, we're building an AI SOC Analyst that applies human-level reasoning and analysis to triage and investigate every alert, without the need for playbooks or complex integrations. Request a demo of Prophet AI to learn how you can triage and investigate security alerts 10 times faster.