Product Managers
Turn AI risk into clear requirements, acceptance criteria, and launch decisions.
AI Security AcademyMap PillarMap • Attack • Defend • Evidence
Turn AI risk into product requirements. Ship AI features buyers can trust.
The course that teaches product teams to define data boundaries, abuse cases, evals, release gates, and buyer-ready evidence before AI risk becomes launch friction.
Risk mappedbefore engineering starts
Abuse casesas backlog inputs
Launch gateswith clear ownership
Buyer evidenceready at release
Built for product managers, product owners, AI product leads, founders, PMO leaders, engineering managers, design leaders, and governance partners.
What you'll master
Go from vague AI scope to launch-ready product decisions
Define the feature risk
inside the brief
Set data boundaries
users, tenants, and tools
Write abuse-case requirements
with acceptance criteria
Ship with launch evidence
buyers can review
Live preview
Buyer Question
Can this AI feature use customer data and trigger agentic actions?
Launch-criticalProduct Decision Framework
- Define data boundaries
- Write abuse cases
- Set eval acceptance criteria
- Assign launch owners
Launch Evidence
- AI Feature Brief
- Data Boundary Map
- Eval Summary
- Risk Decision Record
- Buyer Evidence Pack
Launch impactTrust is defined before release
Built for your reality
PMO Leaders
Create one launch-readiness bar across product, engineering, security, and legal.
AI Product Leads
Scope AI features with data boundaries, evals, and buyer trust built in.
Founders
Launch AI features buyers can understand, review, and trust.
Governance Partners
Move review faster with product artifacts that make risk explicit.
Launch readiness is a product artifact
This course gives product teams the briefs, boundaries, abuse cases, eval criteria, release gates, and evidence needed to make secure AI features shippable.
15+
Years in AI security, AppSec & enterprise
57
Public case studies
60+
Public work examples
Enterprise experience
SplunkForescoutDevoCornerstoneUnumDisneyDefence& more
“If AI risk never makes it into the requirements, it comes back later as launch delay, buyer friction, and production failure.”
Why this course exists
Product decisions define AI risk
Product decisions set what data the AI sees, what users expect, what actions agents can take, which failure modes matter, what evidence buyers receive, and what counts as launch-ready.
This course helps product teams scope AI features that are useful, testable, governed, and trusted — by making risk a first-class part of the brief, the backlog, and the launch gate.
Heads up
The enterprise problem
Risk that is not captured in requirements returns as launch delay, buyer friction, security-review churn, or a production incident — at the worst possible time.
Comparison
What changes after this course
Before — risk is someone else's problem
- AI risk surfaces late, in security review or a buyer questionnaire
- Done means the UI works, not that behavior was tested
- Data boundaries and tenant expectations are assumed, not defined
- Launch readiness is a debate, not a checklist
After — risk is built into the product
- Risk becomes requirements, abuse cases, and acceptance criteria
- Evals and release gates define what launch-ready means
- Data boundaries and user expectations are explicit
- Launches ship with buyer-ready evidence attached
Audience action grid
Who it's for
Product managers & owners
A way to turn AI risk into clear, testable requirements.
Technical program & PMO leaders
A shared launch-readiness bar across teams.
AI product leads & founders
Scoping patterns for features buyers will trust.
Engineering & design managers
Common language with security, legal, and privacy.
Governance & legal/privacy partners
Product artifacts that make review faster.
Checklist
What you'll be able to do
- Explain what changes when products use AI.
- Distinguish LLM, RAG, agent, eval, guardrail, and model-gateway concepts.
- Translate AI risk into product requirements.
- Define data boundaries and tenant expectations.
- Write abuse-case stories and acceptance criteria.
- Specify evals and release gates for AI features.
- Prioritize security stories in backlogs and roadmaps.
- Prepare buyer-ready evidence for launch.
- Coordinate security, legal, privacy, engineering, and go-to-market owners.
- Build a secure AI feature launch plan.
Program at a glance
Program at a glance
10
Modules
8
Hands-on labs
1
Launch plan
6
Delivery formats
Curriculum
10 modules
- 01What Changes When Products Use AI
- 02AI Feature Vocabulary for Product Teams
- 03Risk Framing and Product Decisions
- 04Data Boundaries, Tenancy, and User Expectations
- 05Abuse Cases as Product Requirements
- 06Evals, Acceptance Criteria, and Release Gates
- 07Security Stories, Backlogs, and Roadmaps
- 08Buyer Evidence and Launch Readiness
- 09Working with Security, Legal, and Engineering
- 10Capstone: AI Feature Launch Plan
Operating principles
How the program works
Risk becomes requirements
If the risk matters, it shows up in the brief, the acceptance criteria, the test plan, or the release gate — not a side conversation.
Boundaries beat vibes
Define data boundaries, user expectations, tenant scope, tool permissions, and failure behavior explicitly.
Evals are acceptance criteria
For AI features, done includes behavior tests — not only a completed UI.
Launch needs evidence
Enterprise launches need artifacts that explain controls, limitations, review status, and residual risk.
Artifact list
What you'll walk away with
- AI feature brief template
- Data-boundary and tenant-expectation map
- Abuse-case-to-acceptance-criteria pattern
- Launch-readiness gate checklist
- Buyer evidence package
- 30-day AI feature launch plan
Hands-on practice
You'll practice
- Draft an AI feature brief
- Map data boundaries for a feature
- Write abuse-case user stories
- Define eval acceptance criteria
- Create launch-readiness gates
- Build a buyer evidence checklist
- Define escalation owners
- Build a 30-day AI launch plan
Flexible delivery
Choose what fits your team
Self-paced course
Work through it solo inside the Academy.
Product leadership workshop
Instructor-led for your product org.
PMO enablement program
Roll it out across program teams.
Slack or Teams challenge
A drip sequence that builds shared language.
SCORM / LMS package
Drop it into your existing training platform.
AIPSA Map module
Plug it into the broader AIPSA program.
Framework
AIPSA alignment
Primary domain: Map — locating AI risk in product decisions.
Also supports: Evidence (buyer-ready launch artifacts) and Defend (turning risk into secure requirements).
Related AIPSA products
- AIPSA Map Domain Package
- AIPSA Evidence Domain Package
- AIPSA Academy Complete
- AI Product Security Assessment
- AI Governance Workshop
- AI Feature Launch Readiness Review
Start the course
Ship AI features buyers can trust
Bring AI Product Management for Secure AI Features to your product org as a self-paced course or a leadership workshop — and make launch-readiness something you can prove.
Enterprise AI Security Training
AI Product Management for Secure AI Features
Define, scope, and ship AI products buyers can trust.
Introductory~5 hours10 modulesSelf-paced
AI product risk starts in product decisions, not in engineering. This course teaches product teams to turn AI risk into requirements, abuse cases, acceptance criteria, release gates, and buyer-ready evidence — so features ship useful, governed, and trusted.
Quote
“If AI risk never makes it into the requirements, it comes back later as launch delay, buyer friction, and production failure.”
Why this course exists
Product decisions define AI risk
Product decisions set what data the AI sees, what users expect, what actions agents can take, which failure modes matter, what evidence buyers receive, and what counts as launch-ready.
This course helps product teams scope AI features that are useful, testable, governed, and trusted — by making risk a first-class part of the brief, the backlog, and the launch gate.
Heads up
The enterprise problem
Risk that isn't captured in requirements returns as launch delay, buyer friction, security-review churn, or a production incident — at the worst possible time.
Comparison
What changes after this course
Comparison item
Before — risk is someone else's problem
- AI risk surfaces late, in security review or a buyer questionnaire
- "Done" means the UI works, not that behavior was tested
- Data boundaries and tenant expectations are assumed, not defined
- Launch readiness is a debate, not a checklist
Comparison item
After — risk is built into the product
- Risk becomes requirements, abuse cases, and acceptance criteria
- Evals and release gates define what "launch-ready" means
- Data boundaries and user expectations are explicit
- Launches ship with buyer-ready evidence attached
Audience Action Grid
Who it's for
| You are | What this course gives you |
|---|---|
| Product managers & owners | A way to turn AI risk into clear, testable requirements |
| Technical program & PMO leaders | A shared launch-readiness bar across teams |
| AI product leads & founders | Scoping patterns for features buyers will trust |
| Engineering & design managers | Common language with security, legal, and privacy |
| Governance & legal/privacy partners | Product artifacts that make review faster |
Checklist
What you'll be able to do
✓Explain what changes when products use AI.
✓Distinguish LLM, RAG, agent, eval, guardrail, and model-gateway concepts.
✓Translate AI risk into product requirements.
✓Define data boundaries and tenant expectations.
✓Write abuse-case stories and acceptance criteria.
✓Specify evals and release gates for AI features.
✓Prioritize security stories in backlogs and roadmaps.
✓Prepare buyer-ready evidence for launch.
✓Coordinate security, legal, privacy, engineering, and go-to-market owners.
✓Build a secure AI feature launch plan.
Metrics
Program at a glance
Modules
10
Hands-on labs
8
Capstone
1 launch plan
Delivery formats
6
Curriculum
Curriculum
10 modules
01
What Changes When Products Use AI
02
AI Feature Vocabulary for Product Teams
03
Risk Framing and Product Decisions
04
Data Boundaries, Tenancy, and User Expectations
05
Abuse Cases as Product Requirements
06
Evals, Acceptance Criteria, and Release Gates
07
Security Stories, Backlogs, and Roadmaps
08
Buyer Evidence and Launch Readiness
09
Working with Security, Legal, and Engineering
10
Capstone: AI Feature Launch Plan
Operating principles
How the program works
Principle
Risk becomes requirements
If the risk matters, it shows up in the brief, the acceptance criteria, the test plan, or the release gate — not a side conversation.
Principle
Boundaries beat vibes
Define data boundaries, user expectations, tenant scope, tool permissions, and failure behavior explicitly.
Principle
Evals are acceptance criteria
For AI features, "done" includes behavior tests — not only a completed UI.
Principle
Launch needs evidence
Enterprise launches need artifacts that explain controls, limitations, review status, and residual risk.
Artifact List
What you'll walk away with
- An AI feature brief template that captures risk up front.
- A data-boundary and tenant-expectation map.
- An abuse-case-to-acceptance-criteria pattern for your backlog.
- A launch-readiness gate checklist with evals.
- A buyer evidence package and a 30-day AI feature launch plan.
Checklist
Hands-on practice
✓Draft an AI feature brief.
✓Map data boundaries for a feature.
✓Write abuse-case user stories.
✓Define eval acceptance criteria.
✓Create launch-readiness gates.
✓Build a buyer evidence checklist.
✓Define escalation owners.
✓Build a 30-day AI launch plan.
Definition List
Flexible delivery
- Self-paced course
- Work through it solo inside the Academy.
- Product leadership workshop
- Instructor-led for your product org.
- PMO enablement program
- Roll it out across program teams.
- Slack or Teams challenge
- A drip sequence that builds shared language.
- SCORM / LMS package
- Drop it into your existing training platform.
- AIPSA Map module
- Plug it into the broader AIPSA program.
Recommended learning path
Recommended learning path
Start with Modules 1–3 to understand how AI changes product decisions and risk framing.
Move through Modules 4–6 to define boundaries, abuse cases, evals, and acceptance criteria.
Finish with Modules 7–10 to manage backlogs, launch readiness, cross-functional owners, and your final AI feature launch plan.
Framework
AIPSA alignment
Primary domain: Map — locating AI risk in product decisions. Also supports: Evidence (buyer-ready launch artifacts) and Defend (turning risk into secure requirements).
Related Paths
Related AIPSA products
- AIPSA Map Domain Package
- AIPSA Evidence Domain Package
- AIPSA Academy Complete
- AI Product Security Assessment
- AI Governance Workshop
- AI Feature Launch Readiness Review
Note
Responsible scope
This course uses fictional products and public-safe artifacts. It does not provide legal advice or promise risk-free AI, and it does not replace security, privacy, legal, or engineering review.
Start the course
Ship AI features buyers can trust
Bring AI Product Management for Secure AI Features to your product org as a self-paced course or a leadership workshop — and make launch-readiness something you can prove.