Description

A focused course, tailored for you

ML Product Security Reviews That Satisfy Enterprise Auditors

Build the threat model, attestation artefacts, and review gate that close the gap between standard AppSec checklists and the ML risk questions your enterprise customers are now asking.

Your AppSec review checklist was written for web applications. It has no row for training data provenance, no row for model inversion, no row for adversarial input surfaces. When an ML feature goes to pre-release review, you write those rows from scratch every time. Meanwhile, your enterprise customers are arriving with 40-question AI risk addenda attached to their procurement reviews, and there is no internal artefact that maps cleanly to what they are asking.

$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

Product security at an enterprise ML company sits at an intersection that standard security frameworks have not caught up with. The threat surface of an ML system is structurally different from a conventional web service: data pipeline integrity, training data lineage, feature store access controls, model serialisation vulnerabilities, inference endpoint hardening, and adversarial robustness all require dedicated review rows that AppSec checklists do not carry. At the same time, regulated enterprise customers (financial services, healthcare, government) are now attaching AI-specific security questionnaires to procurement. They want evidence of a formal ML threat model, evidence of data handling controls, and increasingly evidence of alignment with emerging AI risk frameworks like NIST AI RMF or the EU AI Act's high-risk system requirements. Without a repeatable internal review process that produces those artefacts, every pre-release is a bespoke scramble. This course gives you the methodology, the artefact templates, and the customer-facing attestation structure to close that gap permanently.

What you walk away with

Build an ML-specific threat model template covering training data lineage, feature store controls, inference endpoint hardening, model serialisation risks, and adversarial input surfaces.
Produce a pre-release ML security review gate your product teams can run semi-autonomously, reducing security bottlenecks at each release cycle.
Map your internal review artefacts to the AI risk sections of enterprise customer security questionnaires so the same work product answers both.
Understand which sections of NIST AI RMF and the EU AI Act high-risk system requirements generate the most customer scrutiny and how to evidence compliance.
Write the attestation document that satisfies a regulated enterprise customer's procurement AI risk addendum without a custom engagement for each customer.
Design a feedback loop from customer questionnaire findings back to the internal review process so new risk categories surface before they become pre-release blockers.

The 12 modules

Module 1. Why AppSec Checklists Break on ML Systems

Maps the structural differences between a conventional web service threat surface and an ML system threat surface. Covers the five categories of ML-specific risk that standard OWASP-derived checklists omit: training data integrity, model supply chain, feature store access, inference endpoint, and adversarial robustness. Produces the gap analysis document you take to your security leadership to justify a dedicated ML review track.

Module 2. Training Data Lineage as a Security Control

Builds the data provenance review section of your ML threat model. Covers what to verify at each stage of the training pipeline: data source authentication, transformation audit logs, label integrity for supervised systems, and retention controls. Produces a data lineage attestation template that maps to the data handling sections of enterprise customer AI risk questionnaires, particularly those referencing ISO 42001 or NIST AI RMF data governance requirements.

Module 3. Model Serialisation and Supply Chain Risks

Addresses the model artefact itself as an attack surface. Covers serialisation format vulnerabilities in common ML frameworks, dependency chain risks in model registries, signing and integrity verification for model artefacts, and access controls on model storage. Produces the model supply chain section of your pre-release checklist, including the questions to ask your ML platform team before a model artefact moves to production.

Module 4. Feature Store and Training Infrastructure Access Controls

Reviews the access control surface specific to ML infrastructure: feature stores, experiment tracking systems, model registries, and training job schedulers. Covers least-privilege design for ML roles, separation of training and inference environments, and audit log requirements for regulated workloads. Produces the infrastructure access review section of the ML security gate, including the checklist rows that map to SOC 2 and ISO 27001 controls your customers cite in procurement.

Module 5. Inference Endpoint Hardening

Covers the production inference surface: authentication and authorisation on prediction APIs, input validation specific to ML payloads, rate limiting and abuse patterns unique to inference endpoints, output filtering for generative systems, and monitoring for anomalous inference patterns. Produces the inference endpoint hardening checklist your product teams run at pre-release, with pass/fail criteria that your security gate can enforce without requiring a bespoke review for each feature.

Module 6. Adversarial Input and Prompt Injection Review

Builds the adversarial robustness section of the ML threat model, covering traditional adversarial examples for discriminative models and prompt injection surfaces for natural language interfaces. Explains what a pre-release adversarial review looks like in practice: what to test, what evidence to retain, and how to scope the review proportionately to the risk level of the feature. Produces the adversarial input review artefact and the decision tree for when a full red-team exercise is required versus a structured checklist review.

Module 7. NIST AI RMF: Which Sections Generate Enterprise Customer Questions

Maps the NIST AI RMF govern, map, measure, and manage functions to the sections of enterprise customer AI risk questionnaires that generate the most follow-up. Identifies the five NIST AI RMF practices that regulated financial services and healthcare customers reference most frequently in procurement AI addenda. Produces the NIST AI RMF alignment section of your attestation document, with evidence pointers that draw from the artefacts built in modules 1 to 6.

Module 8. EU AI Act High-Risk System Requirements for SaaS Vendors

Covers the EU AI Act Annex III categories that enterprise SaaS ML features most commonly trigger: employment and work management, access to essential private services, and safety components in products. Explains the technical documentation, conformity assessment, and human oversight obligations that apply to a SaaS vendor whose ML feature is embedded in a customer's high-risk workflow. Produces the EU AI Act section of your customer-facing attestation document and the internal artefact checklist that supports it.

Module 9. Building the Enterprise Customer Attestation Document

Assembles the customer-facing attestation artefact from the internal review outputs built in modules 1 to 8. Covers the structure that satisfies the AI risk sections of procurement questionnaires from regulated enterprises in financial services, healthcare, and government sectors. Explains which internal artefacts to reference versus reproduce, how to handle confidentiality constraints, and how to version the attestation document as your ML systems evolve. Produces a reusable attestation template your customer-facing teams can adapt per customer without a new security engagement.

Module 10. Designing the Repeatable ML Security Review Gate

Builds the operational process: how to structure the ML security review gate so product teams run the first pass themselves, what the security team reviews versus delegates, how to set severity thresholds that distinguish blocking from advisory findings, and how to track findings to closure. Covers tooling integrations that embed the gate in an existing CI/CD pipeline. Produces the ML security gate process document your engineering leadership and product security team operate from.

Module 11. Feedback Loop: From Customer Questionnaire Findings to Internal Controls

Covers the mechanism for routing new risk categories surfaced in customer procurement reviews back into the internal ML threat model and review gate. Explains how to identify a pattern across multiple customer questionnaires, how to decide whether it warrants a new review row, and how to communicate the change to product teams. Produces the feedback loop governance document that ensures your review gate evolves with customer requirements rather than lagging them by a release cycle.

Module 12. The 90-Day Implementation Roadmap

Sequences the artefacts from modules 1 to 11 into a 90-day delivery plan for a product security practitioner building the ML review function from scratch or restructuring an ad hoc process. Covers stakeholder alignment across engineering, product, legal, and customer success. Establishes the minimum viable gate for the first three releases under the new process and the success metrics that show the function is working. Produces the roadmap document and 30-day check-in template.

How this addresses your situation

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

Your pre-release checklist has no ML-specific rows and you are writing them by hand for each feature: start with modules 1, 2, 3.

Enterprise customers are arriving with AI risk questionnaires attached to procurement and your internal artefacts do not map to them: start with modules 7, 8, 9.

You need a repeatable review gate that product teams can run without security being a bottleneck every release cycle: modules 5, 6, 10 cover the gate design.

You want a systematic feedback loop so new customer requirements surface before they become pre-release blockers: module 11 is the mechanism, module 12 sequences the build.

What you get with this course

12 written modules covering ML threat modelling, regulatory attestation frameworks, and review gate design
Downloadable ML threat model template covering all five ML-specific risk categories
Pre-release ML security review gate checklist with pass/fail criteria
Customer-facing attestation document template mapped to NIST AI RMF and EU AI Act high-risk system requirements
Enterprise customer AI risk questionnaire response guide for financial services, healthcare, and government procurement contexts
90-day implementation roadmap with stakeholder alignment guide and 30-day check-in template
Hand-built implementation playbook delivered alongside course access, tailored to ML and product security roles at enterprise SaaS companies

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

Course access and the tailored implementation playbook are both provisioned within 24 hours of purchase.

The implementation playbook is built for ML and product security roles at enterprise SaaS companies, not a generic template.

Modules are self-paced. Most practitioners complete the course across two to three weeks while running their current review workload.

Before and after

Before

Each ML feature pre-release involves writing new threat model rows from scratch, fielding customer AI risk questionnaires without a consistent artefact to point to, and spending security review cycles on scope that product teams could handle with a structured checklist.

After

A repeatable ML security review gate that product teams run semi-autonomously, a customer attestation document that maps to the AI risk sections of enterprise procurement questionnaires, and a feedback loop that routes new customer requirements into the internal review process before they become release blockers.

What happens if you do not address this

Enterprise customers in regulated sectors are increasing the AI-specific sections of their procurement security questionnaires. Without a formal ML threat model and attestation process, each new customer engagement requires a bespoke security review, slowing sales cycles and creating inconsistent representations of your security posture across customers. The cost is not just review time. It is the risk of a customer questionnaire finding that surfaces a gap you knew existed but had not formalised, at the worst possible moment in the sales process.

Who it is for

Machine learning and product security practitioners at enterprise SaaS or cloud companies who own pre-release security reviews for ML features, respond to enterprise customer security questionnaires involving AI risk, and need to build a repeatable ML threat modelling and attestation process that works across multiple product teams without security becoming a release bottleneck.

Who this is NOT for. Security analysts whose scope is limited to conventional application security with no ML system exposure. Academic ML researchers with no enterprise customer accountability. Compliance officers looking for a checkbox survey rather than an operational review methodology.

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. Approximately 6 to 8 hours of reading across 12 modules. Artefact build time (threat model, attestation document, review gate) is additional and depends on your existing process maturity. The 90-day roadmap in module 12 sequences the build across a realistic workload.

Why $199 is the right number

Standard AppSec certifications (CSSLP, GWEB) cover web application security thoroughly and do not address ML-specific threat surfaces, regulatory attestation for AI systems, or enterprise customer AI risk questionnaire response. NIST AI RMF training courses cover the framework in the abstract but do not produce the operational artefacts a product security practitioner needs at the pre-release gate. This course is the operational layer that sits between framework literacy and the actual pre-release checklist rows.

FAQ

Does this course assume I already have an ML background?

It assumes you work in a product security role that includes ML systems in scope. You do not need to understand model training mathematics. You need to understand what an ML system does operationally and where it touches your threat surface. Module 1 covers the structural differences that matter for security review without requiring ML expertise.

Which customer questionnaire frameworks does the attestation document map to?

The attestation template in module 9 maps to NIST AI RMF govern and manage functions, EU AI Act Annex III high-risk system documentation requirements, and the AI security sections of SOC 2 Type II and ISO 27001 certification questionnaires that regulated enterprise customers most commonly cite in procurement.

Is this relevant if my company is not yet subject to the EU AI Act?

Yes. The EU AI Act sections are relevant because your enterprise customers in the EU are subject to it and will ask you to evidence compliance as a vendor. Module 8 covers exactly this vendor-side obligation, separate from whether your own company is a regulated entity under the Act.

How do I handle the attestation document when our ML systems change between customer reviews?

Module 9 covers versioning the attestation document and the internal artefact traceability that supports it. The goal is a living document tied to specific system versions, not a static one-time statement.

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.