Secure AI Workflow
A sample project narrative for an evaluated AI workflow with explicit trust boundaries and human review.
Sample content: Replace this page with a project you can accurately discuss. No implementation or outcome here is presented as personal experience.
Problem framing
An AI feature is a system, not a prompt. Document the user goal, acceptable failure modes, protected data, and what must remain under human control.
Trust boundaries
Use a short threat model to show where untrusted input enters, which tools can act, and where authorization is enforced. Useful topics may include:
- prompt injection and untrusted retrieved content;
- least-privilege tool access;
- sensitive-data handling and retention;
- auditable decisions and human approval;
- model, prompt, and evaluation versioning.
Evaluation strategy
Define a representative test set before optimizing. Track task success alongside safety failures, abstention quality, cost, and latency. Add examples only from data you are permitted to share.
Outcome
Replace this section with measured results, caveats, and your specific contribution. If a metric moved, describe the baseline and measurement window.