Implementing Risk-Based Alerting (RBA): A Real-World SOC Use Case

📌 Overview

In a typical SOC environment, analysts often face a high volume of alerts , many of which are repetitive, low priority, or false positives. This not only creates noise but also makes it difficult to identify related events that point to a single, ongoing threat. The lack of context and correlation between alerts can lead to duplicated efforts, missed connections, and slower response times.

This is the story of how one security operations team recognized the need for better context and alert prioritization, and how they successfully implemented Risk-Based Alerting (RBA) using Splunk’s Risk Analysis Framework.

🔍 The Challenge

• A surge in alerts (e.g., phishing, cloud access anomalies) made it hard to identify truly malicious activity.
• Analysts were investigating related alerts separately, unaware they were tied to the same user or threat.
• Manual triage of repetitive alerts led to burnout, alert fatigue, and duplicated work.
• Valuable time was lost investigating benign or low-impact events while more serious threats went unnoticed.

💡 The Realization

Through informal collaboration, analysts discovered they were working on different aspects of the same incident , one from the endpoint side and one from the cloud side. They realized that contextual connections between alerts were missing, which delayed response and reduced overall efficiency.

🛠️ The Solution : Risk-Based Alerting

The team consulted with their SOC architect and detection engineers and decided to implement Risk-Based Alerting (RBA) using Splunk’s Risk Analysis Framework. This allowed them to:

• Assign risk scores to users, systems, and behaviors based on cumulative findings
• Correlate multiple low-fidelity alerts into a single high-confidence alert
• Reduce false positives and prioritize threats based on severity and context
• Automatically surface related activity using Assets and Identities data

✅ Results and Benefits

After implementing RBA, the SOC team experienced significant improvements:

• Reduced alert volume by suppressing redundant, low-priority alerts