The AI Security Engineer's Model-Risk Evidence

Description

What is the The AI Security Engineer's Model-Risk course about?

Turn prompt-injection findings, jailbreak red-team logs, and model-access reviews into the artefacts security partners and counsel actually sign off on. Your red-team work proves the model can be jailbroken. The artefact that proves you handled it correctly is the bottleneck, not the testing itself. Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course?

What do you take away from the The AI Security Engineer's Model-Risk course?

A reusable prompt-injection harness with logged inputs, outputs, severity, and remediation owner per finding. A jailbreak severity rubric your security partner and counsel team have pre-agreed. A model-access review template the access-review board accepts on first pass. A third-party model dependency log that survives external review. A one-page model card that holds up to security partner, counsel, and external auditor review.

What you get with this course?

Twelve written modules with structured templates and worked examples. Python prompt-injection harness with JSON log schema. Jailbreak severity rubric document. Model-access review template. Third-party model dependency log template. One-page model card template. Logging architecture specification template. EU AI Act conformity evidence mapping table. Model-specific incident response runbook. Annual red-team cycle calendar template. Hand-built implementation playbook tailored to your model surface, delivered alongside.

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

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it. Modules 1 through 4 cover the harness and severity rubric, completable in the first week at a steady pace. Modules 5 through 8 cover the access review, dependency log, model card, and logging architecture. Modules 9 through 12 cover AI Act mapping.

What does the The AI Security Engineer's Model-Risk cover on before and after?

Red-team findings live in notebooks and Slack threads. Each new model card section is rewritten from scratch. Access reviews come back from the board with five clarification questions. Counsel asks what the AI Act conformity record looks like and nobody has a one-paragraph answer. Every finding flows through a logged harness into a templated red-team report. The model card pulls fields, not.

What happens if you do not address this?

The first time an external regulator, a security partner team, or a customer security review asks for the model-risk evidence record, the answer cannot be reconstructed. The model card is rewritten under pressure. The access-review history is incomplete. The third-party model dependency log does not exist. The cost of building this evidence chain after the fact, with deadlines attached, is several multiples.

Who it is for?

An AI security engineer, AI red-team lead, or model-risk reviewer at a frontier-model lab, a large platform with internal-model deployments, or a cloud provider hosting third-party models. You run prompt-injection tests, jailbreak harnesses, model-access reviews, and third-party model evaluations. You are comfortable in code. The gap is the documentation chain between a finding and a defensible sign-off.

More answers: what you get with every course, refund policy, all help answers.

A focused course, tailored for you

The AI Security Engineer's Model-Risk Evidence Playbook

Turn prompt-injection findings, jailbreak red-team logs, and model-access reviews into the artefacts security partners and counsel actually sign off on.

Your red-team work proves the model can be jailbroken. The artefact that proves you handled it correctly is the bottleneck, not the testing itself.

$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

AI security engineers at frontier-model and large-platform shops carry a specific friction. The testing skill is high. The reporting chain is not. A jailbreak surface is documented in a notebook with screenshots, a Slack thread captures the severity call, a retest happens in a different environment, and the final sign-off lives in someone's head. When a security partner reviews the model card, when counsel asks for the conformity evidence, when an external auditor asks for the model-access log, the record has to be reconstructed under time pressure. This course is the artefact chain that prevents that reconstruction.

What you walk away with

A reusable prompt-injection harness with logged inputs, outputs, severity, and remediation owner per finding.
A jailbreak severity rubric your security partner and counsel team have pre-agreed.
A model-access review template the access-review board accepts on first pass.
A third-party model dependency log that survives external review.
A one-page model card that holds up to security partner, counsel, and external auditor review.

The 12 modules

Module 1. The Evidence Chain From Finding to Sign-Off

Maps the lifecycle of a single AI security finding from the moment it appears in a notebook to the moment it is signed off in a model card. Names every handoff, every artefact, every approver. Identifies the three points where the chain typically breaks for AI security engineers and the artefact that closes each break. Output is your own evidence-chain diagram for your current model surface.

Module 2. Building the Prompt-Injection Harness That Logs What Matters

Builds a prompt-injection test harness whose output is a structured log, not a notebook. Covers the input taxonomy, the response capture, the severity field, the reproducibility field, and the link back to the model version. Includes the harness in Python with a JSON log schema and a worked example for a text-generation surface and a tool-use surface. You ship a harness you actually run.

Module 3. Jailbreak Severity Rubric That Security Partners Accept

Develops a severity rubric for jailbreak findings that maps to the categories the security partner team and counsel team review. Covers prompt-only jailbreaks, multi-turn jailbreaks, tool-misuse jailbreaks, and data-exfiltration jailbreaks. The rubric is the artefact that prevents per-finding negotiation. Output is a rubric document you can attach to every red-team report.

Module 4. Red-Team Report Template the Model Card Inherits From

Builds the red-team report template that becomes the source of truth for the model card section on adversarial robustness. Covers the scope statement, the harness reference, the finding table, the severity calls, the remediation owners, and the retest evidence. The template is structured so the model card author copies fields rather than rewrites them.

Module 5. Model-Access Review Template the Access Board Approves First Pass

Builds the access-review template for granting model access to internal teams, third-party integrators, and research partners. Covers the requested scope, the data exposure analysis, the rate-limit recommendation, the logging requirement, and the revocation trigger. Template is designed so the access-review board can decide in one pass rather than asking for clarifications.

Module 6. Third-Party Model Dependency Log

Builds the dependency log for every third-party model your stack consumes. Covers the model provider, the model version, the access mechanism, the data flow, the retention terms, and the kill-switch path. The log is the artefact a security partner asks for when a third-party model has a publicly disclosed vulnerability. Output is a populated log for your current dependencies.

Module 7. Model Card That Survives Counsel Review

Constructs a one-page model card with sections that counsel has pre-approved language for: intended use, known limitations, adversarial robustness, data provenance summary, evaluation summary, and revision history. Each section pulls from a templated source in your evidence chain. The model card becomes a roll-up, not a fresh write.

Module 8. Logging Architecture for Model Inputs and Outputs at Defensible Granularity

Specifies the logging architecture your security and counsel teams need to defend post-incident review. Covers what to log, what not to log, retention windows, access control, redaction patterns for sensitive inputs, and the field schema that maps to the prompt-injection harness. Output is a logging spec your platform team can implement.

Module 9. EU AI Act Conformity Evidence the Counsel Team Will Accept

Maps the AI Act high-risk system conformity requirements to the artefacts you already produce in modules 2 through 8. Covers the risk management system documentation, the data governance summary, the technical documentation file, the record-keeping evidence, and the human oversight description. The output is a mapping table counsel uses to answer regulator questions without reinventing evidence.

Module 10. Incident Response Runbook for Model-Specific Incidents

Develops the incident response runbook for model-specific incidents: prompt-injection in production, jailbreak observed by a customer, third-party model vulnerability disclosure, training data exposure, and model-output safety incident. Each scenario gets a containment step, a notification step, a forensics capture step, and an artefact-update step that feeds back into the model card.

Module 11. Annual Red-Team Cycle and External Review Readiness

Builds the annual red-team cycle calendar with internal-team rotations, external red-team engagement, scope expansion criteria, and the external review pack that an independent reviewer receives. The pack is the consolidated output of every prior module: harness, rubric, red-team reports, access-review records, dependency log, model cards, logging spec, AI Act mapping, and incident records.

Module 12. Your First Model-Surface Implementation Plan

Walks the recipient through producing a four-week implementation plan for one model surface in their current scope. Each week names the artefact to ship, the team to involve, the sign-off to obtain, and the review meeting to hold. Output is a plan you can take to your manager and the security partner team in the same week the course is delivered.

How this addresses your situation

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

A notebook of jailbreak screenshots that needs to become a defensible record before the next model card ships.

An access-review board asking why model access requests come in without the data-exposure analysis pre-filled.

A counsel team asking what AI Act conformity evidence looks like for the model you ship next quarter.

An external red-team engagement starting in six weeks with no consolidated review pack to hand over.

What you get with this course

Twelve written modules with structured templates and worked examples.
Python prompt-injection harness with JSON log schema.
Jailbreak severity rubric document.
Model-access review template.
Third-party model dependency log template.
One-page model card template.
Logging architecture specification template.
EU AI Act conformity evidence mapping table.
Model-specific incident response runbook.
Annual red-team cycle calendar template.
Hand-built implementation playbook tailored to your model surface, delivered alongside course access.
Thirty-day money-back guarantee.

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

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

Modules 1 through 4 cover the harness and severity rubric, completable in the first week at a steady pace.

Modules 5 through 8 cover the access review, dependency log, model card, and logging architecture.

Modules 9 through 12 cover AI Act mapping, incident runbook, annual cycle, and your four-week implementation plan.

Before and after

Before

After

Every finding flows through a logged harness into a templated red-team report. The model card pulls fields, not prose. Access reviews approve first pass. Counsel receives an AI Act mapping table that names the artefact for each requirement. The external red-team engagement receives a consolidated review pack the same day it is requested.

What happens if you do not address this

Who it is for

Who this is NOT for. Application security engineers whose work is web and API surface, not model behaviour. Compliance generalists who do not run red-team or model-access work themselves. Anyone looking for a generic AI policy overview rather than the per-finding evidence chain.

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. Eight to twelve hours of focused reading and template completion across the twelve modules. Most engineers finish the first four modules in the first week and pace the rest over the following month.

Why $199 is the right number

Public AI security writing covers the testing techniques but not the evidence chain. Vendor model-card examples show the output but not the artefact pipeline that produced it. Consulting engagements at this depth start at five figures and produce a slide deck rather than a working template pack. This course delivers the templates and the playbook directly.

FAQ

Do I need a specific model stack to use this?

No. The templates and harness are framework-agnostic. Worked examples cover a text-generation surface and a tool-use surface. The implementation playbook is hand-built for your specific stack.

Is this aligned to a specific regulation?

The AI Act mapping module covers EU AI Act conformity. The harness, rubric, and model card design choices also align to NIST AI RMF and ISO 42001 evidence expectations, so the same artefact chain answers multiple regimes.

What does the implementation playbook contain?

It is hand-built within 24 hours of purchase. It maps your current model surface, your access-review process, and your existing logging stack to the templates in the course, and names the order of artefacts to ship first.

Is there a refund?

Yes. Thirty-day money-back guarantee, no questions.

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.