NAC Tuning and Optimization: Bootcamp Unit
RCCE students will learn network access control architectures including 802.1X authentication, RADIUS/TACACS+ integration, device posture assessment, guest networking, and network segmentation enforcement. RCCE students will learn to design and deploy NAC solutions that enforce access policies based on user identity, device health, and location, configure pre-admission and post-admission controls, implement remediation workflows for non-compliant devices, troubleshoot NAC authentication failures, detect and respond to NAC bypass attempts, integrate NAC with endpoint management platforms, and maintain NAC policies as organizational requirements evolve. This optimization course focuses on maximizing effectiveness and efficiency in production security operations. Building on core knowledge, RCCE students will learn to reduce noise, improve signal quality, tune configurations for optimal performance, and measure operational improvements. Students gain the operational maturity to transform good security programs into exceptional ones.
- Security Engineers building defensive controls
- Security Analysts and Blue Team members
- Systems Administrators with security responsibilities
- GRC and Risk Professionals supporting controls
- Professionals implementing NAC Tuning and Optimization: Bootcamp Unit
- Design a scalable privilege management architecture with policy and enforcement
- Design a scalable privilege management architecture with policy and enforcement, including 802.1X auth flows and RADIUS integration.
- Execute hands-on tasks for operational tuning — covering Reduce false positives in posture checks.
- Execute hands-on tasks for policy engineering — covering Identity-based access control design.
- Build detections and response workflows for privilege escalation, including NAC bypass attempts in real-time.
- Explain Topic Map Overview fundamentals
- Execute hands-on tasks for guest networking — covering Captive portals,.
- Execute hands-on tasks for network segmentation — covering Endpoint Integration.
- Build detections and response workflows for privilege escalation, including Quarantine, auto-fix,.
- Execute hands-on tasks for inline vs out-of-band — covering Inline: NAC in traffic path, full control.
- Design a scalable privilege management architecture with policy and enforcement, including Centralized: Single policy server cluster.
| Module 01 | Bootcamp Unit — Network Access Control Architectures |
| Module 02 | NAC Architecture Mastery |
| Module 03 | Operational Tuning |
| Module 04 | Policy Engineering |
| Module 05 | Incident Response Integration |
| Module 06 | Topic Map Overview |
| Module 07 | Guest Networking |
| Module 08 | Network Segmentation |
| Module 09 | NAC Bypass Detection |
| Module 10 | NAC Architecture Fundamentals |
| Module 11 | Inline vs Out-of-Band |
| Module 12 | Deployment Models |
| Module 13 | 802.1X Authentication Deep Dive |
| Module 14 | EAP-TLS (Certificate-Based) |
All hands-on labs run on Rocheston Rose X OS. Students practice nac tuning and optimization: bootcamp unit by implementing the controls discussed in class, with a focus on real-world deployment, monitoring, and validation.
- Lab 1: Design a scalable privilege management architecture with policy and enforcement
- Lab 2: Design a scalable privilege management architecture with policy and enforcement
- Lab 3: Execute hands-on tasks for operational tuning
- Lab 4: Execute hands-on tasks for policy engineering
- Lab 5: Build detections and response workflows for privilege escalation
Upon successful completion of this course, students will receive an official RCCE Course Completion Certificate for NAC Tuning and Optimization: Bootcamp Unit, verifiable through the Rocheston certification portal.
- 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