For cybersecurity professionals deploying AI across investigation, detection, response, governance, and team operations
Claude for Security Professionals
From AI fundamentals to a governed, production AI security capability.
Ten modules covering the complete AI adoption lifecycle for security teams. Investigation methodology, detection engineering, IR documentation, automation, compliance, governance, adversarial AI threats, and organizational deployment — all environment-agnostic, all producing deployable assets.
Overview
AI is transforming cybersecurity. Most security professionals know this but do not know how to operationalize it. This course takes you from "AI is useful" to "I have a documented, governed, production AI capability integrated into my security program."
Ten modules across four phases: foundations, operational skills, governance, and strategic deployment. Every module produces deployable assets — prompt libraries, templates, scripts, frameworks, and playbooks. The course is environment-agnostic: examples use Microsoft 365 and Sentinel, but the methodology applies to any SIEM, EDR, or security platform.
Prerequisite: Familiarity with Claude fundamentals. Complete the Claude Field Guide first, or jump straight in if you already use AI tools daily.
Audience profile
SOC analysts and investigators who want AI-powered workflows for investigation, detection, and documentation across any environment. Detection engineers who want to build, test, and document rules faster — in KQL, SPL, or Sigma. Security managers and team leads who need an AI governance framework and deployment playbook. CISOs and security directors who need the strategic perspective: what AI means for their program, how to govern it, and how to measure ROI.
Prerequisites: Working knowledge of cybersecurity (investigation, detection, incident response, compliance, or governance). Basic AI literacy (the Field Guide or equivalent). No specific platform or vendor experience required.
Course syllabus
The strategic context
What AI offers security teams, where the genuine capabilities are, and what the authoritative literature says. Sets the intellectual foundation for everything that follows.
AI-powered security operations
Four modules covering the core operational skills: investigation, detection engineering, IR documentation, and security automation. Environment-agnostic methodology with deployable assets.
Compliance, governance, and adversarial AI
Three modules covering the governance layer: compliance automation, organizational AI governance, and defending against AI-powered threats.
Organizational adoption and the future
Two modules covering team deployment and the strategic roadmap: how to roll AI out across your security team and where AI in security operations is heading.
What you leave with
Deployable assets in every module
AI capabilities assessment and tool evaluation framework
A structured framework for evaluating AI tools against your operational requirements. Capabilities matrix mapping AI strengths to security functions. Vendor assessment criteria. The strategic foundation for every adoption decision.
Investigation prompt library (20+ prompts, 6 incident types)
Environment-agnostic investigation prompts covering endpoint compromise, email-based attacks, identity compromise, insider threat, cloud incidents, and ransomware. Tested, documented, and adaptable to any SIEM or EDR platform.
Detection engineering template and testing framework
A repeatable process for converting any threat advisory into a deployed detection rule — in KQL, SPL, or Sigma. Includes rule generation prompts, MITRE ATT&CK mapping, test plans with false positive estimation, and documentation templates.
IR report template pack and communication templates
CISO-ready IR report structure with Claude prompt templates for every section. Plus: executive briefing, board presentation, regulatory notification, employee communication, and PIR templates. The complete incident communications toolkit.
AI governance framework — deployed, not documented
Shadow AI detection rules, data classification matrix, vendor assessment scorecards, acceptable use policy, AI incident response procedures, and board reporting templates. Running in your environment, not sitting in a SharePoint folder.
Organizational deployment playbook and AI roadmap
Team onboarding plan, role-specific configurations, ROI measurement framework, CISO business case, and a 12-month AI capability roadmap. Everything you need to move AI from personal tool to organizational capability.
Study guide
How to approach this course
Time commitment
Plan for roughly 20–25 hours of estimated study time across all 11 modules. Each module is 2-3 hours and produces a complete deployable asset. Most people complete the course over 5-8 weeks alongside their day job. Modules can be completed independently — start with whichever matches your most immediate need.
What you need
An AI assistant account with project/workspace capability (Claude Pro/Team, or equivalent). Access to a security operations environment for the investigation and detection modules — any SIEM and EDR platform works. The course uses Microsoft 365 and Sentinel for examples but the methodology is platform-agnostic.
Prerequisite
Complete the Claude Field Guide (Foundation + Security tracks) or have equivalent AI tool experience. Working knowledge of security operations — this course does not teach security fundamentals. If you can write a structured prompt and understand the verification discipline, you are ready.
Environment-agnostic
This course is not tied to Microsoft 365. The investigation methodology, detection engineering process, governance frameworks, and automation patterns apply to any security environment. Examples use KQL and Sentinel for illustration, but every technique translates to Splunk, CrowdStrike, Elastic, or any other platform.
See for yourself
Every module produces deployable assets — prompt libraries, investigation templates, governance frameworks, and automation scripts. See the course depth for yourself.