AI-Improved Security Operations Center

Security Operations Centers process large volumes of alerts, including false positives and duplicates. This creates alert fatigue, slows triage, and limits the time available for high-impact incidents. This case study introduces an AI-improved SOC workflow that supports analysts while keeping them in control. The pipeline combines SIEM alert normalization, machine-learning-based alert prioritization, cyber threat intelligence, asset-criticality scoring, retrieval-grounded triage notes, SOAR-style playbook routing, and analyst feedback. The workflow helps students understand how AI can reduce manual Tier-1 triage effort, improve response time, and support safer decision-making without replacing human judgment.