Using AI in Security Operations: A Practical Checklist for the Modern SOC

AI is transforming how security operations centers (SOCs) work, but the hype can outpace the reality. You don’t need to build custom models or deploy cutting-edge platforms to benefit. Chances are, you already have AI-powered capabilities in your SIEM, SOAR, or security data platforms. The question is: Are they doing anything useful? 

This practical playbook lays out six key priorities to increase productivity and effectiveness while maintaining control. These are not just theoretical concepts or future predictions. They're hands-on steps your team can take today to work faster, respond smarter, and stay in control. 

1. Audit the embedded AI you already have

AI is likely already embedded in your stack — the challenge is using it effectively. Many SIEM and SOAR platforms include AI to reduce noise, correlate alerts, prioritize risks, and assist with triage. Yet these features often go underused.

Start with an audit. What’s enabled? Where is it helping? Where is it getting in the way? Focus first on low-risk, high-volume tasks like:

• Clustering duplicate alerts.
• Prioritizing incidents by severity.
• Enriching tickets with context.

When tuned properly, these embedded tools can reduce triage time, improve mean time to detect (MTTD), and free analysts from repetitive workflows that drag down productivity.

Also look at where embedded AI fits into your broader threat detection, investigation, and response (TDIR) process. AI that supports full TDIR workflows, not just siloed tasks, will deliver the clearest path to impact. 

Checklist:

• Have you reviewed the AI-powered features enabled in your tools?
• Are they actively helping reduce noise or improve time to detect?
• Are you using AI in low-risk, repetitive workflows?
• Is AI integrated into your broader TDIR workflows?

Why this matters: Without a clear audit, you might be missing easy wins. Many AI features are already part of your tech stack and can make an immediate impact with minimal disruption.

Pro tip: Test embedded AI on simple tasks to build trust and demonstrate value.

2. Redefine the responsibilities of AI and analysts

AI isn't replacing analysts; it's reshaping their roles. The key is assigning tasks appropriately. Let AI assist with triage, enrichment, or prioritization while humans supervise.

As AI takes on more tasks, the goal isn’t to remove humans from the loop. Instead, it’s to elevate their contributions by reducing repetitive work and enabling higher-value decision-making. Think of it like this: AI does the sorting, humans make the decision.

Map out your workflows. Identify what should remain human-led, what AI can assist with, and what might be ready for full automation. This clarity enhances trust, productivity, and strategic focus.

Checklist:

• Are analysts supervising AI outputs rather than handling everything manually?
• Is there clarity on task ownership between AI and humans?
• Are your analysts supported in evolving their roles?
• Are you checking for where AI is overreaching or underdelivering?

Why this matters: Clear delineation of tasks helps avoid over-reliance or under-utilization of AI. Analysts can focus on strategic oversight rather than repetitive, low-value tasks.

Pro tip: Label each step in your workflow as human-led, AI-assisted, or automated to identify opportunities for improvement and potential challenges.

3. Keep your AI accountable and reviewable

AI can be confidently wrong. That’s why traceability and human-in-the-loop oversight are essential. If a model flags a threat or suggests a remediation, your team should be able to ask: Why? Based on what? 

Generative and embedded AI systems can produce outputs that look convincing but miss the mark. A hallucinated recommendation or misprioritized threat could lead your team down the wrong path. Logging, traceability, and active feedback loops are critical.

Establish feedback loops, review logs regularly, and ensure AI doesn’t operate as a black box. Especially for sensitive actions, human verification should remain mandatory.

Checklist:

• Are AI outputs logged and reviewable?
• Do you audit outputs regularly for accuracy?
• Can analysts flag and correct AI missteps?
• Are human approvals required for high-risk actions?

Why this matters: Trust grows when systems are accountable. Analysts are more likely to embrace AI if they know it can be monitored, corrected, and improved. 

Pro tip: Embed feedback mechanisms into SOC workflows to continually refine AI behavior.

4. Protect against AI misuse, internally and externally

Yes, AI is a tool, and it’s also a new attack surface. Employees using AI tools may unintentionally leak data or trust flawed outputs. Attackers may exploit prompt injections or shadow AI tools.

As more teams adopt generative AI tools to write code, summarize logs, or generate remediation steps, the risk of misuse grows. The danger isn’t just external. Shadow AI tools used without security oversight can expose confidential data to third-party models with no accountability. 

Set guardrails: define approved tools, enforce usage policies, and monitor outputs. Log all activity and treat AI with the same scrutiny as privileged systems.

Checklist:

• Do you have clear policies on AI tool usage?
• Are employees trained on safe AI practices? 
• Are prompt injection threats being monitored?
• Are AI interactions logged and auditable?
• Are outputs validated before being shared externally?

Why this matters: Secure AI usage isn’t about stifling innovation. It’s about making the safe path the easy one, ensuring users can confidently apply AI without putting your org at risk. 

Pro tip: Monitor AI tools like you monitor critical infrastructure.

5. Manage AI’s data appetite with strong governance

AI thrives on data, and that data is often sensitive. What’s the risk? That AI systems outpace your governance policies. Generative and predictive models may require access to log files, asset inventories, user behavior records, or even customer data. Without controls, this appetite creates risk.

Map which tools access what data, and why. Layer on privacy controls, limit retention, and apply the same governance standards as any sensitive data system. 

Checklist:

• Do you know what AI tools are accessing your data?
• Are retention and access policies enforced?
• Is data access minimized? 
• Are audit trails in place for all AI workflows?
• Are compliance controls applied to AI systems?

Why this matters: Without clear governance, AI can unknowingly violate data handling or retention policies, creating new compliance and security issues.

Pro tip: Treat AI as a data pipeline: monitor inputs, outputs, and access continuously. 

6. Prepare for the rise of agentic AI

AI that acts autonomously is already here. From SOAR tools triggering remediations to AI agents acting on your behalf, the stakes are rising.

Agentic AI systems go beyond recommending actions — they can also execute them autonomously within the confines of your business and security policies. The benefits include faster containment and scalable response, but without oversight, consequences can be serious.

Define boundaries with human-in-the-loop: Which actions can AI take? What must be escalated? Who reviews the logs? Treat agentic AI like a new team member who needs rules and oversight. 

Checklist:

• Are you aware of any current autonomous AI actions?
• Are AI permissions scoped and reviewed?
• Are actions logged and approved? 
• Do your escalation paths account for AI?
• Are "rules of engagement" defined and enforced?

Why this matters: Like any new technology, AI that can act autonomously introduces a new level of operational risk. Clear rules are essential to prevent unintended changes or unsafe automations, with humans ultimately determining the level of autonomy granted to the agent.

Pro tip: Write and socialize a clear "rules of engagement" policy for AI autonomy. 

AI strengthens, not replaces, your SOC

AI is already reshaping security operations. The opportunity isn’t in adopting every new model — it’s in optimizing what you already have. Activate your embedded tools, clarify handoffs, build accountability, and prepare for what's next.

With thoughtful implementation, your team can work faster, respond smarter, and stay in control.