RCCE Course
Course #370

Conditional access Hardening Workshop

📊 Level: Advanced
⏱️ Duration: 2 Days
🏷️ Track: IAM
📋 Prerequisites: IAM fundamentals
🖥️ Mode: Online Instructor-Led
📝 Course Description

RCCE students will learn conditional access policy design and implementation including risk-based authentication, device compliance requirements, location-based restrictions, and adaptive access controls. RCCE students will learn to create conditional access policies in enterprise identity platforms, enforce MFA based on sign-in risk signals, require device compliance before granting access to sensitive resources, block access from untrusted locations and networks, implement session controls and application restrictions, troubleshoot conditional access policy conflicts, and monitor conditional access logs for policy bypass attempts and unauthorized access patterns. This hands-on hardening course focuses on reducing attack surface through practical configuration changes and security guardrails. At an expert level, RCCE students will learn to apply hardening baselines, validate configurations, and measure the security improvement achieved. Students walk away with actionable hardening checklists and the skills to maintain hardened configurations as environments evolve.

🎯 Target Audience
  • Security Engineers building defensive controls
  • Security Analysts and Blue Team members
  • Systems Administrators with security responsibilities
  • GRC and Risk Professionals supporting controls
  • Professionals implementing Conditional access Hardening Workshop
🧠 What You Will Learn
  • Execute hands-on tasks for conditional access
  • Execute hands-on tasks for hardening workshop
  • Execute hands-on tasks for learning objectives
  • Design a scalable privilege management architecture with policy and enforcement
  • Execute hands-on tasks for harden & validate
  • Monitor and audit privilege usage; detect escalation attempts, including Architect conditional, and Apply CIS/NIST hardening.
  • Execute hands-on tasks for required knowledge
  • Execute hands-on tasks for lab environment
  • Execute hands-on tasks for course scope — covering IAM fundamentals and, Cloud identity platform.
  • Execute hands-on tasks for what is conditional access?
  • Execute hands-on tasks for signal collection
📚 Course Outline
Module 01Conditional Access
Module 02Hardening Workshop
Module 03Learning Objectives
Module 04Design & Implement
Module 05Harden & Validate
Module 06Monitor & Respond
Module 07Required Knowledge
Module 08Lab Environment
Module 09Course Scope
Module 10What Is Conditional Access?
Module 11Conditional Access Architecture
Module 12Signal Collection
Module 13Policy Engine
Module 14Access Controls
🧪 Lab Details

All hands-on labs run on Rocheston Rose X OS. Students practice conditional access hardening workshop by implementing the controls discussed in class, with a focus on real-world deployment, monitoring, and validation.

  • Lab 1: Execute hands-on tasks for conditional access
  • Lab 2: Execute hands-on tasks for hardening workshop
  • Lab 3: Execute hands-on tasks for learning objectives
  • Lab 4: Design a scalable privilege management architecture with policy and enforcement
  • Lab 5: Execute hands-on tasks for harden & validate
📊 Skill Level
Advanced
Beginner Intermediate Advanced Expert
Duration
2 Days
🎓
Certificate
Completion
🖥️
Lab Platform
Rose X OS
👨‍🏫
Mode of Training
Online Instructor-Led
🔥
Platform
Zelfire
🐦‍⬛
Cyber Range
Raven
📓
Study Material
CyberNotes
🏆 Certificate

Upon successful completion of this course, students will receive an official RCCE Course Completion Certificate for Conditional access Hardening Workshop, verifiable through the Rocheston certification portal.

🔑 Student Access & Materials
  • Full access to all course materials and slide decks
  • Hands-on lab access on Rocheston Rose X OS environment
  • Access to Rocheston CyberNotes
  • Access to Rocheston Zelfire — EDR/XDR SIEM platform
  • Access to Rocheston Raven — online cyber range exercise platform
  • Access to Rocheston Vulnerability Vines AI