Stop cross-tenant leaks, trim model exposure, and gate AI-driven database access with practical controls
5 sections · 10 lessons
Trace the support-assistant request path, then distinguish user identity, application identity, authentication, and authorization so database access follows the caller rather than the app’s ambient power.
2 lessonsApply tenant_id isolation, then compare pre-filtering, read-only roles, views, and RLS to keep unauthorized rows and sensitive columns out of the application and prompt.
3 lessonsDefine allowlisted intents, parameterized queries, and basic argument validation so model-selected reads stay within predefined database access paths.
1 lessonTreat retrieved notes as data, not authority, and reduce exposure with fewer columns, fewer rows, and redaction before sending anything to the model.
2 lessonsGate state-changing actions with explicit authorization, confirmation, narrow permissions, and audit logging, then trace the full request flow and map threats to the control that stops them.
2 lessonsEvery lesson in order.
10 lessons
An LLM support assistant can leak customer data when database access, model prompts, and writes are not separated cleanly. This course shows how to secure that path with authentication, tenant isolation, least privilege, data minimization, intent allowlists, and stronger controls for state-changing actions. It is built for beginners who want a clear mental model for protecting database data in AI applications without drowning in implementation detail.
The course covers where database data can leak in an LLM support flow, how identity and authorization differ, and how tenant_id policies and row-level security scope visible data. It also covers pre-filtering, roles, views, intent allowlists, parameterized queries, prompt injection risks, data minimization, and safer write controls with audit logging.
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.