AI vs. Automation in Security Operations: Determining When to Use What

As security operations teams face increasing pressure to respond faster and do more with less, two technologies are often brought into focus: AI and automation. Commonly used interchangeably, they are two distinctly different yet powerful tools in a security practitioner’s toolbox. But which should you use—and when?

Understanding the right tool for the job can be the difference between solving a security challenge efficiently or adding unnecessary risk and complexity. In this post, we’ll discuss the difference between the two practices, breaking down how to decide between AI, automation, or a combination of both—with real-world examples to illustrate each.

What’s the Difference Between AI and Automation in SecOps?

Automation focuses on streamlining repeatable tasks. Think scripts, playbooks, and workflows that handle predictable work with high speed, repeatability, consistency, and accuracy.

AI focuses on learning and adapting. Applying machine learning, natural language processing, or other techniques to analyze large amounts of data to identify patterns or make intelligent decisions—even when the path isn’t clearly defined.

These are separate but complimentary technologies. Choosing the right approach depends on the task’s complexity, data variability, and the need for judgment or pattern recognition.

Use Case 1: Automation for Repetitive Tasks using Structured Data

Example: Phishing Triage and Response

Many security teams get flooded with phishing alerts. Most follow the same pattern—check headers, scan URLs, analyze attachments, and quarantine messages. In this case, automation would be a perfect solution for these types of alerts. The logic is rule-based and repetitive. You don’t need AI to make decisions when the steps are predetermined, and the requests are structured.

A security orchestration, automation and response (SOAR) platform can utilize a playbook to:

• Automatically ingest emails marked as phishing
• Run static analysis and sandbox detonation
• Quarantine messages and remove them from inboxes
• Close low-risk alerts with documentation
• Send notifications or training assignments to the appropriate users

In this case, it’s faster, cheaper and more secure to use automation than investing a lot of time and effort into utilizing AI.

Use Case 2: AI for Unstructured, Evolving Threat Detection

Example: Detecting Insider Threats

Insider threats are notoriously difficult to catch. They often involve subtle changes in behavior or context that don’t follow fixed rules. And typically involves unstructured data, which refers to information that does not have a predefined data model or is not organized in a predefined manner. Extracting meaning from this type of data usually requires natural language processing (NLP) or machine learning, since rule-based systems struggle to parse the variability.

This is where AI shines. Behavioral analytics platforms use machine learning to:

• Establish baselines of user activity (e.g., login patterns, data access)
• Detect anomalies like unusual file downloads or access to sensitive systems at odd hours
• Prioritize alerts based on risk scoring

The patterns are complex, context-sensitive, and constantly evolving. Rule-based logic using automation alone would miss many threats—or generate too many false positives.

Use Case 3: AI + Automation for Rapid Threat Investigation

Example: Endpoint Threat Detection and Response (EDR)

Let’s say a suspicious PowerShell command is detected running on an endpoint—a classic sign of a potential compromise. You want to investigate quickly and respond accordingly if needed.

Here is how AI and automation can and should work together in a situation like this:

1. AI Detects the Anomaly
   Agentic AI analyzes the structured data (e.g., command-line activity, process trees) and unstructured data (e.g., threat intel reports) to identify unusual behavior based on known attack patterns. Agentic AI refers to AI systems capable of independently setting objectives, making decisions, and adapting to evolving threats without constant human input.
2. Automation Investigates
   A playbook runs automatically to gather structured data in the context of logs, user activity, and system metadata—speeding up the triage process.
3. AI Scores Risk and Analyst + Automation Responds
   AI assigns a risk score. If it’s high, it’s automatically flagged to the analyst who can decide whether to use automation to isolate the device, block the process, and notify the team—all within seconds and recording each step so there’s no ambiguity in the process.

In this instance, AI provides intelligent detection using both structured and unstructured data. Then an analyst is quickly provided the information necessary to make an informed decision to use automation to execute fast, consistent responses. This combination minimizes dwell time and analyst fatigue.

How to Decide Which to Use

Conclusion

Choosing between AI, automation, or a combination of both in security operations depends on the complexity of the task and the type of data involved. Automation excels at handling repeatable tasks using structured data—like log parsing, IP blocking, or ticket creation. AI shines when working with unstructured or complex data, such as natural language threat reports, behavioral patterns, or anomalies hidden in user activity.

Each technology has a place in security operations. The key is to align the technology with the problem that you are solving for—and the data that you are working with. Contact us if you’d like help determining which use cases are right for AI, automation, or a combination of both.