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AI Security AcademyAttack PillarMap • Attack • Defend • Evidence

Find AI failures before your customers do. Make safe to ship something you can prove.

The course that teaches product teams to test prompt injection, RAG leakage, agent tool abuse, guardrail failure, and sensitive-data exposure in a safe, repeatable, evidence-driven way.

Abuse casesturned into tests
RAG boundarieschecked every release
Agent controlsreviewed before launch
Release evidencenot release anxiety
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Built for QA, test automation, product security, AppSec, DevOps, SecOps, AI platform teams, product managers, and internal red teams.

What you'll master

Go from happy-path QA to release-ready AI testing

  1. Map the attack surface

    for AI product features

  2. Test instruction conflicts

    without unsafe live abuse

  3. Validate RAG and agents

    with repeatable matrices

  4. Ship with evidence

    severity and regressions

Live preview

Buyer Question

Can this assistant reveal another tenant's document through RAG?

Release blocker
Test Framework
  • Define the boundary
  • Use synthetic canaries
  • Capture safe evidence
  • Add the regression
Release Evidence
  • Attack Surface Map
  • RAG Boundary Matrix
  • Agent Tool Review
  • Guardrail Results
  • Remediation Backlog
Release impactSafe to ship becomes testable

Built for your reality

QA Engineers

Test AI features beyond happy paths with reusable abuse-case families.

Product Security

Turn AI failures into severity, remediation, and regression evidence.

Platform Teams

Wire AI release-readiness checks into pipelines, gateways, and telemetry.

Product Leaders

Define a shared bar for what safe to ship means for AI features.

Internal Red Teams

Use defensive, in-scope methods for owned AI systems and release reviews.

AI release confidence needs evidence

This course gives product teams the test families, boundary matrices, guardrail checks, severity notes, and release evidence needed before customers find the failures.

15+
Years in AI security, AppSec & enterprise
57
Public case studies
60+
Public work examples

Enterprise experience

SplunkForescoutDevoCornerstoneUnumDisneyDefence& more
“If your QA only tests the path you intended, your AI failures get discovered by users, buyers, auditors, and attackers instead.”
AI Security Academy

Why this course exists

AI fails in ways normal QA never checks

A model can follow the wrong instruction. A RAG system can retrieve the wrong document. An agent can call the wrong tool. A guardrail can pass the demo and fail the edge case — and a release can look safe simply because nobody tested the abuse path.

This course gives product teams a controlled, defensive, release-oriented way to find those failures first, turn them into repeatable tests, and ship AI features with evidence behind them.

Heads up

The enterprise problem

Every AI failure you do not test for becomes a failure your users, buyers, auditors, or attackers test for you — usually in production.

Comparison

What changes after this course

Before — one-off findings and release anxiety

  • Findings live in chat threads and disappear after the sprint
  • Guardrails are trusted because the demo passed
  • Abuse paths are discovered after launch, not before
  • Severity is argued by vibes, with no shared release-readiness bar

After — a repeatable, evidence-driven test program

  • Abuse cases become reusable test families and CI regressions
  • Guardrails are evaluated, not assumed
  • Release decisions are backed by clear evidence and severity
  • The whole team shares one language for AI release risk

Audience action grid

Who it's for

QA & test automation engineers

A structured way to test AI features beyond the happy path.

Product & application security engineers

Repeatable abuse-case libraries and CI regression suites.

DevOps, SecOps & AI platform teams

Release-readiness checks wired into the pipeline.

Product & engineering managers

A shared bar for what safe to ship actually means.

Internal red teams

Defensive, in-scope methods for owned AI systems.

Checklist

What you'll be able to do

  • Map the AI attack surface for a product feature.
  • Design prompt-injection and instruction-conflict tests.
  • Test RAG retrieval boundaries and leakage risks.
  • Review agent tools, permissions, approvals, and execution limits.
  • Find sensitive-data exposure across prompts, outputs, logs, and traces.
  • Evaluate guardrails without treating them as complete security.
  • Build reusable abuse-case libraries and prompt families.
  • Add AI red-team regression checks to CI/CD.
  • Capture evidence and severity clearly enough to drive remediation.
  • Ship a product release abuse-case test plan.

Program at a glance

Program at a glance

10
Modules
9
Hands-on labs
1
Release test plan
6
Delivery formats

Curriculum

10 modules

  1. 01AI Attack Surface for Product Teams
  2. 02Prompt Injection and Instruction Conflicts
  3. 03RAG Leakage and Retrieval Boundary Tests
  4. 04Agent Tool Abuse and Excessive Agency
  5. 05Sensitive Data Exposure and Output Handling
  6. 06Guardrail Evaluation and Regression Testing
  7. 07Test Case Libraries and Prompt Families
  8. 08CI/CD AI Red-Team Regression Suites
  9. 09Evidence, Severity, and Remediation Backlogs
  10. 10Capstone: AI Abuse-Case Test Plan

Operating principles

How the program works

Test in controlled environments

Synthetic data, approved test systems, bounded prompts, documented scope. Never attack live or third-party systems.

Tie every test to release risk

Each test connects to a product failure mode, user impact, buyer concern, or release decision — or it does not ship.

Make failures repeatable

Turn one-off findings into test cases, prompt families, CI checks, and regression suites that protect every future release.

Capture evidence for remediation

Good evidence explains what happened, why it matters, how to reproduce it safely, and what should change.

Artifact list

What you'll walk away with

  • Reusable abuse-case library
  • Prompt-family test set
  • RAG boundary test matrix
  • Agent permission review checklist
  • CI/CD regression suite design
  • Release-ready abuse-case test plan

Hands-on practice

You'll practice

  • Map a fictional AI feature's attack surface
  • Write safe prompt-injection test categories
  • Build a RAG boundary test matrix
  • Review agent tool permissions and approvals
  • Design sensitive-data exposure tests
  • Create a guardrail regression suite
  • Write severity notes and a remediation backlog
  • Assemble a release-ready abuse-case test plan

Flexible delivery

Choose what fits your team

  • Self-paced course

    Work through it solo inside the Academy.

  • QA enablement workshop

    Instructor-led for your test and release teams.

  • Product security workshop

    Hands-on for AppSec and product security.

  • Slack or Teams challenge

    A drip sequence that builds testing muscle.

  • SCORM / LMS package

    Drop it into your existing training platform.

  • AIPSA Attack module

    Plug it into the broader AIPSA program.

Framework

AIPSA alignment

Primary domain: Attack — finding AI failures before they ship.

Also supports: Map (attack surface and scope), Defend (turning failures into controls), and Evidence (capturing findings and release decisions).

Related AIPSA products

  • AIPSA Attack Domain Package
  • AIPSA Evidence Domain Package
  • AIPSA Academy Complete
  • AI Product Security Assessment
  • AI Red Team Workshop
  • LLM Attack Range
  • RAG Test Harness

Start the course

Find the failures before your customers do

Bring AI Red Teaming to your product team as a self-paced course or a hands-on workshop — and make safe to ship something you can prove.

Start this course