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AI For Incident Responders
This course will teach you how to use AI copilots safely and responsibly in real-world investigations. You will learn how to combine human judgment with AI speed, apply governance frameworks, identify AI-specific threats, and measure tangible improvements in performance and accuracy.
Course Content
Overview
Incident response teams are under constant pressure to move faster and manage more data than ever before. The number of alerts continues to grow, while the time available to investigate each one shrinks. Analysts spend hours reviewing telemetry and logs only to discover that many alerts are false positives or duplicates. The result is alert fatigue, inconsistent documentation, and delayed containment.
Artificial intelligence is now helping change that story. Modern SOC platforms integrate AI copilots that can summarize incidents, generate natural-language queries, and propose containment actions in seconds. Tools such as Microsoft Copilot for Security, Google SecOps Duet or Gemini, Splunk AI Assistant, and Elastic AI Assistant are already reshaping how analysts triage, investigate, and report.
This course will teach you how to use AI copilots safely and responsibly in real-world investigations. You will learn how to combine human judgment with AI speed, apply governance frameworks, identify AI-specific threats, and measure tangible improvements in performance and accuracy.
Course Objectives
By the end of this course, you will be able to:
- Explain How AI Supports Each Phase of the Incident Response Lifecycle: Describe how AI copilots assist in detection, analysis, containment, and recovery through summarization, reasoning, and natural-language query generation. Why it matters: Understanding where AI fits helps analysts apply it strategically and maintain human oversight.
- Write and Apply Structured Prompts: Use effective prompt patterns that generate summaries, investigative queries, and recommended next steps. Why it matters: Structured prompting transforms AI from a chatbot into a reliable investigative partner.
- Apply Governance and Risk Management Frameworks: Integrate principles from the NIST AI Risk Management Framework (AI RMF) and CISA Secure by Design guidance. Why it matters: These frameworks ensure that AI use remains transparent, secure, and fully accountable.
- Recognize and Mitigate AI-Specific Threats: Identify and address risks such as prompt injection, model evasion, data poisoning, and model extraction. Why it matters: Defending AI systems requires awareness of new adversarial tactics that target automated reasoning.
- Build and Measure AI-Enhanced Playbooks: Standardize AI prompt snippets, log model outputs, and track improvements using MTTA, MTTR, and false-positive reduction metrics. Why it matters: Consistent prompts and measurable outcomes ensure both efficiency and compliance.
- Strengthen Analyst Skills for AI-Augmented Workflows: Develop data literacy, governance awareness, and collaboration skills across SOC and data science teams. Why it matters: Analysts who understand both AI and governance principles will lead the next generation of SOC operations.
Create Goal
Working towards a new skill? Set a goal to complete this course. We'll help you track your progress and keep yourself accountable.
