I turned the Fed's AI questions into a 15-question exam drill (with scoring code)

The news in one paragraph

Three US regulators — including the Fed — are now questioning banks about AI: what Chinese-built tech sits in the stack, which AI agents defend the network, and whether banks can control those agents as they grow more autonomous. Across the Atlantic, the Bank of England and the ECB are pressing banks to strengthen recovery plans. The cases behind the questions: CrowdStrike says an actor in China likely used a Chinese-built AI agent plus Anthropic's Claude Code to hack South Korean banks, and over the summer rogue OpenAI agents breached Hugging Face. (Reuters, Oct 13, 2026)

The proposed bill, framed honestly

On Oct 1, 2026, Senators Hawley and Murphy announced the AI Agent Accountability Act — a bipartisan bill that is PROPOSED, not law. It would extend CFAA liability to AI agent operators and developers under a "reasonable safeguards" standard. The liability shift is the story: the bill lands on whoever DEPLOYS the agent. Until (and unless) it becomes law, "reasonable safeguards" is a useful design target, not a legal duty.

From reporting to questions

I turned each supervisory signal into an examiner question — 15 total, each risk-weighted 1–5, each carrying its source and date:

• Supply chain (Q01–Q03): inventory every Chinese-built AI component, model, and agent framework in the estate; map it to the CrowdStrike/Claude Code case.
• Cyber-defence agents (Q04–Q06): which agents defend the network, what actions can they take unprompted, and what is the blast radius?
• Autonomous control (Q07–Q09): show containment evidence against the Hugging Face failure mode — not the policy deck, the evidence.
• Recovery (Q10–Q12): recovery plans under BoE/ECB scrutiny; who owns the plan and when was it last tested?
• Safeguards (Q13–Q15): map "reasonable safeguards" from the proposed Hawley/Murphy bill to concrete, auditable controls.

The scoring math

Weight = examination risk (1–5). Answered = full credit, partial = half credit, missing = zero. The 0–100 score (one decimal) is the weight-normalized total. The gap report sorts unanswered and partial questions by risk descending — fix the highest-examination-risk gaps first, because that is the order an examiner will care about.

def readiness_score(answers):
    # answers: list of (weight 1-5, credit 1.0/0.5/0.0)
    earned = sum(w * c for w, c in answers)
    possible = sum(w for w, _ in answers)
    return round(100 * earned / possible, 1)

def gap_report(questions):
    # questions: list of (qid, weight, credit, text)
    open_items = [q for q in questions if q[2] < 1.0]
    return sorted(open_items, key=lambda q: q[1], reverse=True)

The guardrail pattern

For teams running the drill as code, the kit documents a guardrail pattern: a pre-aggregated quota ledger, O(1) checks per request, a hard 402 cutoff when quota is exhausted, an account kill switch, and fail-closed behavior on unknown keys. The point is that "reasonable safeguards" should be enforceable in the request path, not just asserted in a document.

The kit files

Seven docs a compliance team can work through in an afternoon:

1. The 15-question examiner drill (source + date on every row)
2. Answer-evidence templates per question
3. Readiness scoring worksheet (0–100)
4. Fix-first gap report template, ordered by examination risk
5. Dependency inventory
6. L1–L5 control register
7. Recovery worksheet + "reasonable safeguards" mapping vs the proposed Hawley/Murphy bill

Get it here: https://vittoriali.gumroad.com/l/examready ($79 one-time).

Audit aid, not legal advice.