CI/CD Monitoring and Detection: Blueprint
RCCE students will learn CI/CD pipeline security including build environment hardening, artifact integrity verification, secret management in pipelines, deployment authorization controls, and pipeline audit logging. RCCE students will learn to secure continuous integration and continuous deployment pipelines against supply chain attacks, harden build environments and runners, implement secret scanning to prevent credential leakage, verify artifact integrity through code signing and attestation, configure deployment gates and approval workflows, detect unauthorized pipeline modifications, audit pipeline execution logs for suspicious activity, and respond to incidents involving compromised CI/CD infrastructure. This monitoring course teaches comprehensive detection and observability strategies for proactive security operations. Building on core knowledge, RCCE students will learn to instrument systems for security telemetry, build detection pipelines, configure alerting, and maintain monitoring coverage as environments evolve. Students gain the visibility and detection capabilities needed to catch threats early.
- Security Engineers building defensive controls
- Security Analysts and Blue Team members
- Systems Administrators with security responsibilities
- GRC and Risk Professionals supporting controls
- Professionals implementing CI/CD Monitoring and Detection: Blueprint
- Monitor and audit privilege usage; detect escalation attempts
- Explain Course Overview & Learning Objectives fundamentals
- Execute hands-on tasks for pipeline security
- Monitor and audit privilege usage; detect escalation attempts, including Secret Management.
- Design a scalable privilege management architecture with policy and enforcement
- Execute hands-on tasks for security touchpoints — covering Automated build on commit.
- Execute hands-on tasks for modern ci/cd security landscape
- Execute hands-on tasks for supply chain attacks rising — covering SolarWinds, Codecov, ua-parser-js.
- Execute hands-on tasks for pipeline as attack vector — covering SolarWinds, Codecov, ua-parser-js.
- Execute hands-on tasks for executive order 14028 mandates — covering CI/CD has privileged access to prod.
- Execute hands-on tasks for pipeline components & attack surface
- Execute hands-on tasks for source repository — covering Branch protection bypass.
| Module 01 | CI/CD Monitoring and Detection: Blueprint |
| Module 02 | Course Overview & Learning Objectives |
| Module 03 | Pipeline Security |
| Module 04 | Detection & Monitoring |
| Module 05 | CI/CD Pipeline Architecture Fundamentals |
| Module 06 | Security Touchpoints |
| Module 07 | Modern CI/CD Security Landscape |
| Module 08 | Supply Chain Attacks Rising |
| Module 09 | Pipeline as Attack Vector |
| Module 10 | Executive Order 14028 mandates |
| Module 11 | Pipeline Components & Attack Surface |
| Module 12 | Source Repository |
| Module 13 | Build Runners |
| Module 14 | Artifact Registry |
All hands-on labs run on Rocheston Rose X OS. Students practice ci/cd monitoring and detection: blueprint by implementing the controls discussed in class, with a focus on real-world deployment, monitoring, and validation.
- Lab 1: Monitor and audit privilege usage; detect escalation attempts
- Lab 2: Explain Course Overview & Learning Objectives fundamentals
- Lab 3: Execute hands-on tasks for pipeline security
- Lab 4: Monitor and audit privilege usage; detect escalation attempts
- Lab 5: Design a scalable privilege management architecture with policy and enforcement
Upon successful completion of this course, students will receive an official RCCE Course Completion Certificate for CI/CD Monitoring and Detection: Blueprint, 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