Securing an LLM Feature, End to End
Review a fictional support assistant from its first data-flow map to a release decision and incident response. Build an access policy, output contract, tool state machine and evaluation report, then defend what the evidence allows you to ship.
A staff member can be allowed to read two customers' tickets while being forbidden to include one customer's details in a reply to the other. That small distinction drives this course. You will secure a fictional help desk assistant by following what its data and tools can actually reach.
The case stays connected across eight lessons in four modules. First, draw the boundaries and build an evidence-based risk register. Then implement a small access-policy model and review every destination of a model reply. Next, design bounded tools and evaluate both policy enforcement and model behavior. Finish by making a release decision from incomplete evidence and rehearsing a disclosure incident.
You leave with a review packet: a boundary map, permission table, risk register, output contract, tool state machine, evaluation report and release memo. The final tabletop adds containment and restart criteria. These are practical artifacts to adapt to your own authorized work; the examples use fictional customers and synthetic data.
The exercises vary by task. Predict an access decision before running the offline example. Change a permission while a draft is queued. Review a streaming output path. Explain what a zero-event evaluation can establish. Choose a limited pilot or hold the release, then defend that decision against a worked rubric. The feedback explains which evidence matters and when a different answer would be reasonable.
The first three lessons are free. The remaining five apply the same case to output controls, tool execution, evaluation, the release project and incident response. Each lesson includes links to the concepts that explain its mechanisms and questions that test the reasoning under changed constraints.
Use the course in sequence when learning the method. If you arrive at one lesson from search, its case assumptions and orientation let you work through that task on its own. Completion comes from producing and reviewing the artifacts, not from memorizing a list of attack names.
When you finish, you can
- Map data authors, acting identities and recipients across the assistant's stores and services
- Prioritize reachable failures using evidence and explicit assumptions
- Authorize retrieval for the actor, task and recipient, including revocation and cached copies
- Specify output rules for rendering, streaming, saved content and message delivery
- Design tool approval, atomic budget reservations and retry reconciliation
- Report evaluation results with detector limitations, uncertainty and utility evidence
- Produce a release memo with enforced scope, blockers, owners and acceptance criteria
- Rehearse containment, evidence handling and restart checks for a disclosure incident
BEFORE YOU START · You can follow an API request through a web application and distinguish signing in from checking permission. The offline examples use basic Python. Start with the Security fundamentals course if identity, processes or network requests are unfamiliar.
Syllabus
Map the boundaries and prioritize the evidence
Distinguish data authors, actors and recipients, then turn the support case into a risk register with explicit assumptions and refusal tests.
Enforce evidence access and output policy
Build an actor-and-recipient access model, account for revocation and copies, and specify separate rules for display, streaming and delivery.
Bound actions and evaluate their effects
Design current authorization, exact-action approval, cumulative reservations and retries, then produce a test report with observable outcomes and honest limits.
Decide what can ship, then rehearse a failure
Use the course artifacts to make a bounded release decision, then practise containment, evidence handling and restart criteria in a fictional incident.