Save on courses or Exam now
About Instructors
-
grantBritgh- Instructor Rating - Reviews - Students - CoursesNo biography available, please complete
📘 About This Course
This is a hands-on cloud and AI security course. Across 39 lab videos in ten modules you build and defend one coherent capstone: an authorized Linux laboratory, an AWS sandbox, a CI/CD pipeline, a detection stack and a real LLM application. Roughly 30% of the time is explanation and 70% is live demonstration.
You begin with the threat landscape, a visible installation of every tool used in the course, credential and cost guardrails, and a practical threat model of the architecture. You then work through cloud shared responsibility, account baselines and organization guardrails, an Infrastructure-as-Code security baseline with OpenTofu or Terraform, and evidence-based configuration review.
Identity comes next: IAM and least privilege, federation, MFA and short-lived credentials, Zero Trust translated into identity, device, workload, network and data controls, and secrets management with workload identity. You then harden the platform itself: network segmentation and egress control, Linux and SSH hardening, container and image security, and encryption with a recovery that you actually test.
The DevSecOps modules cover protected repositories and review workflows, Semgrep, dependency and secret scanning as real quality gates, least-privileged pipelines with artifact integrity, and vulnerability triage with defensible exceptions. In detection and response you design the logging architecture, install and operate a detection stack, build rule-based and anomaly detections with false-positive control, and run a full incident cycle from triage to postmortem.
Four modules are devoted to AI and LLM security: threat modeling with the OWASP GenAI LLM Top 10, prompt injection and untrusted content, tool use, excessive agency and output validation, and RAG, data leakage, model supply chain and evaluation.
Governance is treated as engineering: NIST CSF 2.0 functions and profiles, an ISO/IEC 27001:2022 ISMS with risk treatment, the NIST AI Risk Management Framework and its Generative AI Profile, and an auditable evidence system. The course closes with privacy and retention, resilience and disaster recovery, security metrics and reporting, purple-team validation, and a capstone where you build, assess and hand over the platform with a 30-day roadmap.
Every demonstration runs in an authorized laboratory on synthetic data. Each video shows a real success case and a controlled failure that is diagnosed, repaired and retested, so you learn how the controls behave when they break. This course is a practitioner checklist, not an audit or a certification.
What You'll Learn
Build a Zero Trust architecture across identity, device, workload, network and data on a real AWS lab.
Secure an LLM application against prompt injection, excessive agency, data leakage and model supply-chain risk.
Run a DevSecOps pipeline where SAST, dependency and secret scanning actually fail the build.
Design security logging, build rule-based and anomaly detections, and run a full incident-response cycle.
Harden Linux, SSH, containers and networks, and prove that encryption and recovery really work.
Produce NIST CSF 2.0, ISO/IEC 27001:2022 and NIST AI RMF evidence that stands up to an internal audit.
🛠️ Requirements
- Comfortable on the Linux command line and with basic networking.
- An AWS account you are authorized to use, with billing and budget guardrails you control.
- A GitHub account and a dedicated repository for the lab.
- Basic Python and Git. No prior security certification is required.
👤 Who This Course is For
- Cloud and DevOps engineers who now own security outcomes.
- Security engineers and analysts moving into cloud and AI workloads.
- AI and platform engineers shipping LLM applications to production.
- Technical leads preparing for NIST CSF, ISO 27001 or AI governance work.
53618 Students applied
100 Comments
-
-
John DoeMorbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
-
John Doe
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
-
-
John Doe
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
-
John Doe
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
-
John Doe
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet