Secure hosted and self-managed GenAI systems against prompt injection, data leakage, and weak cloud controls
4 sections · 11 lessons
Define hosted and self-managed GenAI ownership, then build a first-pass threat model for LLM and agent systems and examine how prompt injection works and where it stops.
3 lessonsDesign RAG retrieval with permission mirroring and isolation, then apply short-lived workload identity and scoped tool authorization to reduce exposure and overreach.
3 lessonsPlan secure deployment for managed model APIs and self-hosted weights, select monitoring evidence for GenAI security failures, and run response steps for suspected data leakage.
3 lessonsMap governance duties to concrete engineering controls and evidence, then consolidate a reusable secure GenAI blueprint with verification checkpoints.
2 lessonsEvery lesson in order.
11 lessons
GenAI systems in the cloud create new trust boundaries around models, agents, tools, and data. This course builds a practical security view of hosted and self-managed deployments, from threat modeling and prompt injection to identity, authorization, monitoring, incident response, and governance. It is aimed at practitioners who need to secure production GenAI workloads without losing sight of cloud ownership, evidence, and residual risk. By the end, the reader can reason about security controls for LLM and agent applications across the full deployment lifecycle.
The course covers GenAI trust boundaries, LLM and agent threat modeling, prompt injection, RAG permission mirroring, and vector-data isolation. It also addresses workload identity, short-lived credentials, tool authorization, secure deployment for managed APIs and self-hosted weights, runtime monitoring, incident response for data exposure, and governance mapped to engineering controls.
From project managers and operators to founders, analysts, and teams adopting AI, Fenzo is for people who want to get better at the work they already do.
Anything from using AI for project planning and analysis to improving workflows, decision-making, communication, and delivery. If you can describe the skill or task, Fenzo can build a course around it.
Yes. Tell Fenzo what you do, what you’re trying to accomplish, and where you’re stuck, and Fenzo will shape the course around your role, your goals, and the work you actually have to do.
No. Fenzo adapts to your starting point, so you can begin with the fundamentals or jump straight into more advanced work.
Yes. Fenzo for Business puts the whole team on one plan — shared seats, courses you can assign, and reporting on who has finished what.
See Fenzo for BusinessYou start by describing your goal or taking a short skill benchmark. Fenzo uses that context to identify gaps and shape the learning experience around them.
Yes, Fenzo works in your mobile browser.
Something to operate in every section.
Three findings from decades of learning research shaped how every Fenzo lesson is built.
Average pass rate · Freeman et al., PNAS 2014
What the science says
Students in lecture-only classes are 1.5× more likely to fail.
How Fenzo does it
Every Fenzo lesson is interactive. No passive reading or watching, ever.
You work through itAI does it for you
Skill retention over time · with vs. without practice
What the science says
Offloading thinking to AI erodes the very skills you’re trying to build.
How Fenzo does it
Fenzo makes you do the thinking — guided, but never done for you.
Retrieval practiceRe-reading
Long-term recall · retrieval vs. re-reading
What the science says
Recalling information beats re-reading it for building durable understanding.
How Fenzo does it
Lessons weave in quizzes and recall prompts, so it sticks.