01 / 06PLANNED MODULE
Understand AI systems
LLM fundamentals, tokens, context, embeddings, model limitations, hallucinations, and evaluation.
PLANNED PROOF OF WORK
A baseline evaluation set and documented failure cases.
THE PROBLEM
A demo is not a secure system.
An AI feature can look impressive and still leak data, follow hostile instructions, retrieve untrusted content, or use tools in unsafe ways. You need to know how to test the system around the model, not just write better prompts.
THE APPROACH
Learn through one evolving project.
SAFE AI connects application building, repeatable testing, mitigation, and governance. Each stage produces evidence you can inspect, explain, and improve.
THE DIFFERENCE
Show the work, not just the badge.
The planned capstone records the original behaviour, the test that exposed a weakness, the control added, and the retest. Results and limitations should be documented honestly.
PLANNED CURRICULUM / 06 MODULES
From first principles
to a defensible AI system.
The sequence is designed to move from understanding to building, testing, securing, and governing. Module content will be refined as the capstone is built and tested.
02 / 06PLANNED MODULE
Build an AI application
Application flow, APIs, retrieval-augmented generation (RAG), document handling, and structured outputs.
PLANNED PROOF OF WORK
A small working AI application with a defined purpose and data flow.
03 / 06PLANNED MODULE
Observe and evaluate
Prompt and output logging, test cases, quality rubrics, error handling, and basic monitoring.
PLANNED PROOF OF WORK
A repeatable evaluation log that records expected and actual behaviour.
04 / 06PLANNED MODULE
Threat-model and red-team
Trust boundaries, prompt injection, jailbreak attempts, retrieval risks, sensitive-data exposure, and unsafe tool use.
PLANNED PROOF OF WORK
A scoped threat model with reproducible tests and severity notes.
05 / 06PLANNED MODULE
Add controls and retest
Least privilege, input and output validation, retrieval boundaries, human approval, guardrails, and regression testing.
PLANNED PROOF OF WORK
A before-and-after test record showing which controls helped and which limitations remain.
06 / 06PLANNED MODULE
Govern and present the system
AI risk assessment, control mapping, accountability, residual risk, documentation, and responsible deployment decisions.
PLANNED PROOF OF WORK
A capstone pack containing the system overview, test results, controls, and governance notes.
WHO IT IS FOR
Built for people who want to do the work.
- Security, GRC, privacy, and audit professionals moving into AI risk and security.
- Developers and technically curious learners who want to test AI applications more rigorously.
- Career upgraders who prefer a practical project over passive video completion.
WHAT YOU WILL WORK TOWARD
A capstone you can explain.
- A small AI application with a documented purpose and data flow.
- A threat model and repeatable adversarial test cases.
- Evidence of mitigations, retests, and remaining limitations.
- A risk and control mapping summary for human review.
These are planned learning outputs, not claims of completed student outcomes.
WHY CNSLT / BUILD IN PUBLIC
No invented success stories. Show the evidence.
SAFE AI is being documented as it is developed. The public journey will include experiments, failures, fixes, and the reasoning behind design decisions. The course will be promoted as ready only when the learning experience is ready.
Read the project introduction: How to build secure and trustworthy AI ↗
COURSE STATUS
In development
Join the early-access list to receive an update when the curriculum, launch timing, and pricing are ready to share.
GET EARLY ACCESS →COURSE SCOPE
What SAFE AI covers
This course focuses on the security, reliability, and governance of AI applications and their surrounding systems. Red-team exercises are intended for controlled environments and authorized testing. The curriculum does not promise that any single guardrail makes an AI system completely secure.