Secure a support copilot’s refund, retrieval, and script paths before they turn into leakage or abuse
4 sections · 9 lessons
Trace where identity, spend, retrieval, tool calls, and sandboxing need enforcement in a fintech support copilot, then bind callers to tenants and end users with narrowed delegated access.
2 lessonsApply shared quotas and in-flight caps, then authorize RAG retrieval at the backend call site while treating retrieved chunks as untrusted input and validating outputs against schema rules.
2 lessonsConstrain the refund tool with scope, ownership, approval, and idempotency rules, then sandbox model-written analysis code and harden MCP servers against secret exposure and privilege escalation.
2 lessonsBuild the logging, encryption, retention, and kill-switch layer, then verify the full control set with a table-driven regression suite and a final end-to-end refund-path review.
3 lessonsA fintech support copilot has to know who is asking, what they are allowed to see, and which actions it may trigger without crossing a line. This course builds that judgment for Python backend engineers working with RAG, refund tools, and model-generated analysis code, using concrete controls instead of vague AI safety talk. By the end, the backend request path, delegated access, quotas, retrieval checks, tool gates, sandboxing, logging, and kill switches fit together as one security model.
The course covers identity binding, tenant-scoped delegation, shared quota accounting, retrieval authorization, injection screening, tool argument checks, approval gates, idempotency, and URL policy. It also covers script sandboxing, MCP server scoping, audit logging, encryption, key and retention constraints, kill switches, and a regression suite for adversarial refusal cases.
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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.