Description

A focused course, tailored for you

The Frontier AI Risk and Regulation Operating Playbook

Translate frontier model risk policy into shipped controls, model cards, evals, and regulator-ready evidence.

The policy team wants a sign-off on the next deployment. The evals team wants two more weeks. The deployment review is on the calendar regardless. The gap that decides which side the meeting lands on is the evidence stack, not the position paper.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

Frontier AI risk work sits between three audiences that read different artefacts. The policy and standards bodies (EU AI Act GPAI rules, the US AI Safety Institute, the UK AISI, the Frontier Model Forum commitments) want clear thresholds, documented capability evals, and a credible incident response posture. The internal deployment review wants a clean model card, a current eval pass, and a named owner for each disclosed limit. The red team and safety researchers want their findings tracked to closure with reproducible seeds. When those three views are not stitched into one evidence pack per checkpoint, every deployment becomes a fresh debate. This course collapses the three views into one stack that the policy lead, the deployment reviewer, and the regulator can all read against the same checkpoint.

What you walk away with

A standing evidence pack format that the policy lead, the deployment reviewer, and an external regulator can all read against the same checkpoint.
A capability eval inventory mapped to disclosed model limits, with reproducible seeds and a clear re-run cadence.
A model card discipline that survives a slow read by a regulator, with each disclosed limit tied to a specific eval score and a named owner.
A red-team-to-incident pipeline with thresholds, paging rules, and a closure log that policy can quote.
A deployment review evidence checklist that lets a policy lead say yes without a follow-up cycle.

The 12 modules

Module 1. The frontier risk landscape and the artefacts each audience reads

Map the three audiences that consume frontier risk work: the regulators and standards bodies, the internal deployment review, and the red team and safety research community. Identify the canonical artefact each group reads against. Build a single artefact-to-audience matrix that becomes the spine for every later module. Establish the standing weekly cadence that keeps the matrix current as evals, commitments, and rules shift.

Module 2. EU AI Act GPAI obligations and systemic-risk thresholds in practice

Work through the General-Purpose AI Model obligations including the systemic-risk threshold and the Code of Practice. Translate each obligation into a concrete artefact the lab must hold: model documentation, training data summary, energy consumption reporting, systemic-risk assessment, adversarial testing log. Draft the evidence index a Commission liaison would expect to see on first request. Build the gap log against current internal practice.

Module 3. US, UK, and multilateral frontier governance and lab commitments

Cover the US AI Safety Institute, the UK AISI, the Bletchley and Seoul declarations, the Frontier Model Forum commitments, and the Hiroshima Process Code of Conduct. For each, list the specific commitments your lab has signed or implicitly accepted. Build the commitment-to-evidence ledger that names which internal team owns each, what artefact closes it, and where it is filed. The ledger becomes the spine of every external-facing report.

Module 4. Capability evaluations: the eval inventory, reproducibility, and the limits of current methods

Build the eval inventory: dangerous capability evals, alignment evals, deception evals, autonomous replication, cyber-offensive capability, CBRN uplift. For each, document the harness, the seed set, the scoring rubric, and the known limits of the method. Define the re-run cadence tied to checkpoint cadence. Write the methods appendix that an AISI evaluator can read against and reproduce.

Module 5. Systemic-risk frameworks and threshold setting

Set the thresholds that trigger systemic-risk treatment under the GPAI rules and equivalent regimes. Distinguish capability thresholds from deployment thresholds. Build the threshold-to-mitigation matrix that says: when this eval clears this score, the following deployment controls activate, the following committee is convened, the following disclosure is filed. The matrix is the artefact that lets a deployment reviewer act without re-litigating the threshold each cycle.

Module 6. Model card discipline that survives a regulator read

Build the model card as a regulator-readable artefact, not a marketing page. Each disclosed limit maps to a specific eval score, a specific known failure mode, and a named owner for the next eval pass. Each capability claim cites the evaluation harness and the score range. The card carries a change log that ties to checkpoint hashes. Draft the card review protocol that policy, evals, and deployment all sign before publication.

Module 7. Red-team-to-incident pipelines and the closure log

Wire the red team's findings into an incident pipeline with thresholds, paging rules, and a closure log. Define what severity triggers what response: a write-up only, a mitigation in the next release, a deployment hold, a disclosure to AISI partners. Build the closure log that policy can quote in external reports. Cover dual-track logging for findings that are sensitive enough to require restricted distribution.

Module 8. Pre-deployment review: the evidence pack and the sign-off ladder

Design the pre-deployment review as a one-meeting decision, not a series. Build the evidence pack: current eval results, model card delta from last checkpoint, open red-team findings, regulator-facing disclosure status, mitigation roster. Define the sign-off ladder including who can approve at each risk tier and who must escalate. Write the standing agenda that prevents re-litigation of items already closed.

Module 9. Post-deployment monitoring, real-world evals, and rapid response

Cover real-world telemetry for safety incidents, jailbreak prevalence, refusal calibration, and capability drift across fine-tunes and tools. Build the monitoring dashboard that the safety team watches and the policy team reads weekly. Define the rapid-response protocol that triggers a hot-patch, a deployment rollback, or a fresh systemic-risk assessment. Cover the public communications path when a real-world incident becomes public before the response lands.

Module 10. External regulator and AISI engagement: cadence, evidence requests, and pre-deployment information sharing

Run the standing engagement with the US AISI, the UK AISI, the EU AI Office, and other safety institutes. Cover the pre-deployment information sharing commitments, the evidence requests that arrive on short notice, and the read-room protocols for sensitive evaluation results. Build the response playbook for a regulator inquiry that lets the lab respond inside the published service level without burning a sprint.

Module 11. Disclosure, transparency reports, and the public commitment ledger

Build the public-facing transparency report and the internal commitment ledger that backs every public claim. Cover the systemic-risk assessment summary, the capability eval summary, the incident counts, the mitigation rollouts. Run the legal and communications review process that prevents over-claim or under-claim. Define the cadence: quarterly public report, monthly internal review, weekly artefact refresh.

Module 12. The operating cadence: weekly, monthly, quarterly, and per-checkpoint rhythms

Stitch every prior module into a single operating cadence. Weekly: monitoring review, commitment ledger refresh, red-team finding triage. Monthly: model card review, regulator engagement log review. Quarterly: full systemic-risk assessment, transparency report. Per-checkpoint: pre-deployment evidence pack assembly, sign-off ladder, post-deployment monitoring kick-off. Build the calendar that lives on the team wall and the dashboards that show whether each cadence is current.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

The next checkpoint is two weeks from deployment review. Modules 4, 6, and 8 give you the eval inventory, the model card, and the evidence pack that close the meeting.

An AISI evidence request arrived asking for capability eval methods and recent results. Modules 4 and 10 give you the methods appendix and the response playbook.

A red-team finding cleared an internal threshold last week. Modules 5 and 7 give you the threshold-to-mitigation matrix and the closure log entry the policy team can quote.

The transparency report is due this quarter. Modules 11 and 12 give you the public-facing report structure and the cadence that keeps the underlying artefacts current.

What you get with this course

Twelve written modules in the Art of Service learning environment.
Downloadable templates for the artefact-to-audience matrix, the eval inventory, the threshold-to-mitigation matrix, the model card review protocol, the deployment evidence pack, the commitment ledger.
Worked example evidence pack for a fictional frontier checkpoint, showing how every artefact stitches together for a single deployment review.
The hand-built implementation playbook tailored to your eval inventory, your commitment ledger, and your deployment cadence.

What you will have in hand by Day 1, Week 1, Month 1

Within 24 hours: account in the Art of Service learning environment provisioned, course modules available, downloadable templates available, hand-built implementation playbook delivered alongside course access.

Week 1: complete the artefact-to-audience matrix, the eval inventory, and the commitment ledger. These three artefacts unlock every later module.

Weeks 2 to 4: work through threshold setting, model card discipline, the red-team-to-incident pipeline, and the deployment evidence pack. End the four weeks with one checkpoint's worth of evidence assembled to the new standard.

Weeks 5 to 8: run the post-deployment monitoring, the regulator engagement cadence, and the transparency report draft. End the eight weeks with the standing operating cadence on the wall.

Before and after

Before

Every deployment review re-litigates the threshold and the evidence. Policy waits on evals. Evals waits on red team. The model card and the regulator disclosure drift apart from each other and from the actual checkpoint.

After

Each checkpoint enters review with one evidence pack the policy lead, the deployment reviewer, and the external regulator can all read against the same artefacts. The sign-off ladder runs cleanly. The transparency report writes itself from the artefacts already filed.

What happens if you do not address this

The cost of an unstitched evidence stack is not just a slow meeting. It is the moment a regulator inquiry arrives, an AISI evidence request lands, or a red-team finding becomes public before the closure log is current. At that point the lab is responding under pressure with artefacts that were never built to be read together, and policy positions are taken against findings that were never tracked to closure.

Who it is for

You are working on frontier AI risk and regulation inside a lab that ships frontier-class models. You sit at the seam between the policy and standards work outside the company and the deployment review work inside it. You write or co-write the position papers that go to regulators, you sit on the deployment review that decides whether the next checkpoint ships, and you take the call when a red-team finding crosses a threshold. The artefacts on your desk are model cards, capability eval reports, systemic-risk frameworks, and the running list of commitments the lab has made to AISIs and the Frontier Model Forum.

Who this is NOT for. Not for engineers who only train models and never see the policy room. Not for policy researchers with no deployment-review responsibility. Not for general AI ethics commentary roles. This is built for the person who has to ship the evidence pack that closes the deployment review.

How it arrives

Text-based course in the Art of Service learning environment, plus downloadable templates and worked examples for every module, plus the hand-built implementation playbook delivered alongside course access.

Time investment. Roughly six to eight hours per week for eight weeks. The course is built to run alongside live deployment review cycles, not separate from them. Each module's templates produce an artefact you would have had to produce anyway.

Why $199 is the right number

Public commentary on frontier AI risk is plentiful. Free position papers, AISI publications, and Frontier Model Forum reports cover the policy landscape. None of those produce the internal artefact stack that closes a deployment review. This course is built around the stack itself: the inventories, matrices, model card review protocols, evidence packs, and closure logs that turn the policy work into shipped controls.

FAQ

Is this aligned with the EU AI Act GPAI rules and the Code of Practice?

Yes. Module 2 maps each obligation to a concrete internal artefact. The Code of Practice signatory commitments are tracked in the commitment ledger built in Module 3.

Does this cover the US AISI and UK AISI engagement patterns?

Module 10 covers the standing engagement with the US AISI, the UK AISI, the EU AI Office, and other safety institutes, including pre-deployment information sharing and short-notice evidence requests.

Will this conflict with internal policy work already underway at a frontier lab?

No. The course is built to slot into existing policy, evals, and deployment review functions. The artefacts are designed to be the stitching layer between work that is already happening but currently lives in three different docs.

How does the hand-built implementation playbook differ from the course?

The course is the standing curriculum. The implementation playbook is the per-buyer artefact set tailored to your specific eval inventory, your specific commitment ledger, and your specific deployment cadence.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.