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
All training programs
UNDERSTAND → HARDEN → TEST → OBSERVE → RESPOND
Security Engineering
Build security understanding across Linux, networks, applications, DevSecOps, containers, Kubernetes and AI systems with practical engineering controls.
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 for— System and DevOps engineers— Security engineers— Application teams— Platform / cloud teams— Architects
Prerequisites— Basic Linux and networking— Advanced 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.
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
CONNECTED PATHS
Turn the learning path into production context.
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Move AI, LLM, RAG and agentic applications from prototype behavior toward measurable, secure and operable production systems.
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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