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UNDERSTAND → HARDEN → TEST → OBSERVE → RESPOND

Security Engineering

Build security understanding across Linux, networks, applications, DevSecOps, containers, Kubernetes and AI systems with practical engineering controls.

Typical duration2–8 days depending on target domains
DeliveryOnline · On-site · Hybrid · Security lab
Practical work5 representative labs
Outcomes
Understand threat, exposure and control concepts
Harden common infrastructure and delivery paths
Integrate security checks into engineering workflows
Diagnose security posture using evidence rather than checklists alone
Audience & prerequisites
Who it is forSystem and DevOps engineersSecurity engineersApplication teamsPlatform / cloud teamsArchitects
PrerequisitesBasic Linux and networkingAdvanced tracks assume familiarity with the target platform
Representative curriculum

Training topics, organized as engineering modules.

The exact sequence is adjusted to the audience. Foundation topics can be compressed for experienced teams; architecture and troubleshooting can be expanded for advanced programs.

01

Security Foundations

Build a threat-and-control mental model.

  • Assets and trust boundaries
  • Threats and attack surface
  • Identity and least privilege
  • Defense in depth
  • Logging and evidence
02

Linux & Network Security

Protect foundational infrastructure.

  • Host hardening
  • SSH
  • SELinux
  • Firewalling
  • Network exposure and diagnostics
03

DevSecOps

Move security into the delivery lifecycle.

  • Secrets
  • SAST / dependency / container scanning
  • Pipeline controls
  • Artifact integrity
  • Policy and remediation
04

Container & Kubernetes Security

Secure workloads and orchestration layers.

  • Image hygiene
  • Runtime privileges
  • RBAC
  • Network policy
  • Secrets and admission controls
05

Application & API Security

Understand common software-layer risks.

  • Input and output handling
  • Authentication / authorization
  • API abuse
  • Dependency risk
  • Secure design
06

AI Security

Address new risks introduced by AI applications.

  • Prompt injection
  • Data leakage
  • Tool / agent permissions
  • RAG trust boundaries
  • Model and provider risk
Hands-on work

Labs are part of the learning path.

Exercises emphasize observation, implementation, failure and diagnosis rather than command copying.

01

Review a Linux host attack surface

02

Harden SSH and service exposure

03

Add security gates to a CI pipeline

04

Review Kubernetes RBAC / pod security

05

Threat-model a RAG / agent application

Customization

Align the program to your engineering environment.

Security programs can be focused on infrastructure, DevSecOps, Kubernetes, applications, AI systems or cross-domain architecture reviews.

Linux securityNetwork securityDevSecOpsContainersKubernetesApplication securityCloud securityAI security
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