Adversarial ML & AI Security · Capstone brief

SEC-10 · Policy that survives contact

Translate one NIST AI RMF or OWASP LLM risk into a Monday-ready control a teacher in this building could follow — mapped onto language the division already has (AUP, volunteer rules, data-privacy), not a new 40-page AI policy.

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School-approved sandbox only. No production targets. Showcase materials paraphrase class behavior — do not publish payload strings.

The question

Translate one NIST AI RMF or OWASP LLM risk into a Monday-ready control a teacher in this building could follow — mapped onto language the division already has (AUP, volunteer rules, data-privacy), not a new 40-page AI policy.

Lab / materials

Expected failure modes

A generic “be ethical with AI” poster. Copying a Big Tech acceptable-use page. Controls nobody can audit (feelings, not logs or checklists).

Done looks like

One control: owner, when it applies, what “done” looks like on a Tuesday, what evidence would show it failed, and a 15-minute teacher briefing. Pair with the existing AUP section it extends. Refusal log: you did not claim the school board adopted it.

Five C's

CT: one risk → one control. CR: something a teacher can actually do. CO: a teacher (not a peer student) reads it and marks unclear steps. CM: briefing, not a treatise. CZ: unenforceable policy is theater that fails students.

Mentor role

Assistant principal, IT director, or compliance lead reviews practicality at Checkpoint 2. School-supervised.

Rubric calibration

R1: one risk ID, cited. R2: local policy citation or explicit “none.” R3: status-quo (no control) named. R4: failure evidence specified. R5: 15-minute briefing. R6: no fake adoption claim.

Two ways this goes wrong

(a) Five pages of principles, zero owner. (b) A control that requires a SOC the school does not have.


Source moved or something unclear? Send feedback on this brief. Mentors are advisory; the school supervises. These briefs do not produce verified computer-science credit.