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AI Security AcademyDefend + EvidenceMap • Attack • Defend • Evidence

Turn AI coding speed into a paved path. Ship code you can stand behind.

The course that teaches developers and security teams to prompt, review, test, gate, and evidence AI-assisted code before it becomes production risk.

Secure promptsas review contracts
AI code reviewwith shared rubrics
CI/CD gatesthat catch risk early
Release evidencebuyers and auditors trust
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Built for developers, AppSec, product security, platform teams, SDLC leaders, and AI governance owners.

What you'll master

Go from AI coding chaos to controlled delivery

  1. Frame secure prompts

    with constraints and tests

  2. Review generated code

    for real security failures

  3. Use paved-path components

    instead of one-off invention

  4. Gate releases with evidence

    before production

Live preview

Buyer Question

Can we merge this AI-generated auth helper?

High risk
Review Framework
  • Check identity and authorization
  • Validate inputs and outputs
  • Scan secrets and dependencies
  • Require tests and release evidence
Merge Evidence
  • Prompt Constraints
  • Review Rubric
  • Test Results
  • Dependency Check
  • Gateway Policy
Delivery impactAI-assisted PR becomes reviewable

Built for your reality

Developers

Use AI coding assistants without reinventing unsafe auth, validation, logging, or dependencies.

AppSec Teams

Review AI-generated code with a shared rubric, not one-off guesswork.

Platform Engineers

Build paved paths, model gateways, and approved components developers actually use.

SDLC Leaders

Turn AI coding policy into repeatable developer behavior and release gates.

Governance Owners

Show evidence that AI-assisted delivery is controlled, reviewed, and auditable.

Speed needs a control path

This course gives teams the standards, reviews, prompts, gates, and evidence needed to make AI-assisted coding production-ready.

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

Enterprise experience

SplunkForescoutDevoCornerstoneUnumDisneyDefence& more
“The question isn't whether your developers use AI to write code. It's whether your guardrails make that usage safe enough for production.”
AI Security Academy

Why this course exists

AI changes where secure coding happens

Security no longer starts after code is generated. It starts when a developer frames the task, chooses context, grants tool access, accepts a dependency, writes a test, and decides whether the output is safe enough to merge.

The goal is not to slow anyone down. It is to give developers a paved path: secure defaults, approved components, strong prompts, review rubrics, automated checks, and release evidence that are also the easiest way to work.

Heads up

The enterprise problem

Ban AI coding and it moves into shadow workflows. The real question is whether your standards, gateways, tests, and reviews make AI-assisted code safe enough to ship.

Comparison

What changes after this course

Before — speed without a paved path

  • Every developer and model session reinvents auth, validation, and logging
  • Insecure suggestions merge because review has no AI-specific rubric
  • Secrets, dependencies, and permissions slip through unchecked
  • Shadow AI use grows because the approved path is harder

After — a paved path with evidence

  • Approved components and prompts make the secure choice the easy one
  • Reviews catch AI-specific failures with a shared rubric
  • CI/CD gates and telemetry cover AI-assisted pull requests
  • Gateways reduce shadow AI and leave an audit trail

Audience action grid

Who it's for

Developers using AI coding assistants

Secure prompting and review habits that ship faster, safely.

AppSec & product security engineers

An AI-specific review rubric and CI/CD gate design.

Platform engineers

Paved-path patterns, gateways, and approved components.

Secure SDLC & engineering managers

A program that turns AI policy into developer behavior.

AI governance teams & CISOs

Evidence that AI-assisted delivery is under control.

Checklist

What you'll be able to do

  • Explain the new secure-coding risks AI-assisted development introduces.
  • Separate model-behavior risks from application-security risks.
  • Write secure prompts that specify constraints, assumptions, criteria, and tests.
  • Review AI-generated code for auth, validation, output handling, logging, secrets, and dependencies.
  • Use shared security components instead of one-off generated implementations.
  • Define approved AI coding standards for a team or organization.
  • Apply tests, evals, and judge prompts without over-trusting model output.
  • Add CI/CD gates and telemetry for AI-assisted pull requests.
  • Use model gateways and approved tooling to reduce shadow AI.
  • Produce evidence that supports security review, audit, and buyer trust.

Program at a glance

Program at a glance

10
Modules
6
Hands-on labs
1
Operating model
6
Delivery formats

Curriculum

10 modules

  1. 01Threat Model and Workflow Boundaries
  2. 02Secure Prompting and Code Constraints
  3. 03Reviewing AI-Generated Code
  4. 04Secrets, Identity, and Permissions
  5. 05Dependencies, Packages, and Provenance
  6. 06Retrieval, Context, and Repo Assistants
  7. 07Agents, Tools, and Autonomy
  8. 08Tests, Evals, and Failure Modes
  9. 09CI/CD Gates, Telemetry, and Release
  10. 10Capstone: Operating Model

Operating principles

How the program works

Prompt quality is a security control

A prompt is a specification, a threat-model hint, a constraints document, and a review contract — not just a request for code.

Shared components beat repeated invention

Do not make every developer and every model session reinvent authentication, authorization, validation, logging, and model access controls.

Automation must be layered

Static checks, tests, dependency review, secrets scanning, model-assisted review, and human accountability together — no single judge is enough.

Evidence turns training into governance

A secure AI coding program leaves artifacts behind: standards, PR notes, test outputs, release gates, gateway logs, and review decisions.

Artifact list

What you'll walk away with

  • Approved AI coding standard
  • Secure-prompt template
  • AI-specific review rubric
  • CI/CD gate and telemetry design
  • Buyer-safe evidence summary

Hands-on practice

You'll practice

  • Rewrite an insecure coding prompt into a secure one
  • Review AI-generated code for security failures
  • Choose safe shared components over model-invented code
  • Design a model gateway policy
  • Write CI/CD checks for AI-assisted pull requests
  • Produce a buyer-safe evidence summary

Flexible delivery

Choose what fits your team

  • Self-paced course

    Work through it solo inside the Academy.

  • Enterprise workshop

    Live, hands-on for your engineering org.

  • Secure SDLC onboarding

    Make it part of how new engineers ramp.

  • Slack or Teams challenge

    A drip sequence that builds secure habits.

  • SCORM / LMS package

    Drop it into your existing training platform.

  • AIPSA Defend / Evidence module

    Plug it into the broader AIPSA program.

Framework

AIPSA alignment

Primary domains: Defend and Evidence — controlling AI-assisted development and proving it.

Also supports: Map (discovering developer AI usage) and Attack (testing AI-generated code for failure modes).

Related AIPSA products

  • AIPSA Defend Domain Package
  • AIPSA Evidence Domain Package
  • AIPSA Academy Complete
  • LMS / SCORM Setup Add-On
  • Slack / Teams Rollout Add-On
  • Enterprise Onboarding Add-On

Start the course

Turn AI coding speed into a paved path

Bring Secure Coding with GenAI to your engineering org as a self-paced course, an SDLC onboarding module, or a live workshop — and ship AI-assisted code you can stand behind.

Start this course