AI systems run on cryptography. That makes them your problem.
Organizations are deploying AI systems and preparing for the quantum threat as two separate programs, and the AI systems usually end up in neither scope. They belong in the migration. Model weights are signed, training data is encrypted, inference APIs run over TLS, and AI supply chains rest on code signing — every one of those mechanisms faces the same quantum-era risks as the rest of your cryptographic estate, and some of the assets behind them are among the longest-lived secrets your organization holds. This course maps the intersection and gives security teams a concrete plan for bringing AI infrastructure into a post-quantum migration program.
One scope note, stated plainly: this is a security course about protecting AI systems from the quantum threat. It does not cover quantum machine learning methods or quantum computing applied to AI; for those, see the Quantum AI & Machine Learning domain.
Who this course is for
Security architects and engineers responsible for AI systems, CISOs adding AI to their quantum risk picture, ML platform engineers who own the pipelines, and PQC migration teams who need to include AI infrastructure in scope.
Course outline
Module 1 — The cryptographic anatomy of an AI system
Every place cryptography appears in a modern AI pipeline, and which mechanisms carry quantum-era risk.
Module 2 — Long-lived secrets
Training data, model weights, and harvest-now-decrypt-later exposure. Why frontier models are among the clearest HNDL cases in any organization.
Module 3 — Integrity and provenance
Model signing, provenance chains, and trust-now-forge-later risks in model distribution.
Module 4 — Migrating AI infrastructure
Applying PQC migration methodology to AI systems: APIs, MLOps tooling, storage, and embedded deployment.
Module 5 — The AI supply chain
Third-party models, datasets, and tooling, and the cryptographic assumptions they import into your environment.
Module 6 — The combined roadmap
Sequencing AI systems within the wider PQC program: priorities, dependencies, and ownership.
Prerequisites
Security fundamentals assumed. Familiarity with PQC basics helps; our free Introduction to Quantum Security covers the essentials.
Who this course is for
Security architects and engineers responsible for AI platforms, CISOs adding AI systems to their quantum risk picture, and migration teams who need AI infrastructure inside the scope rather than beside it. Data scientists and ML engineers who own production pipelines also fit.
If your question is whether quantum computing will improve your models, Evaluating Quantum AI and Machine Learning is the other course entirely.
What you’ll be able to do afterward
- Map the cryptographic dependencies inside an AI platform
- Plan model and artifact signing for the post-quantum transition
- Assess training-data confidentiality against harvest-now-decrypt-later exposure
- Set post-quantum requirements for AI vendors and model suppliers
What you leave with
You leave with the course handbook, a PDF of the full material with the instructor notes written out in place of the slides’ bullet points, and a PDF copy of Quantum Ready, included at no extra cost. The handbook is yours to keep and yours to share inside your team. For most organizations, that shared reference is the clearest return on a training budget.
Enrollment includes 180 days of access to the online on-demand course, which matches the access window shown in the booking section below.
Where this sits in your path
Nothing is required. Post-Quantum Foundation gives the algorithm vocabulary if you want it first. Hybrid Cryptographic Implementations for the engineering, and PQC Vendor Governance for the model and platform supply chain.
Why we teach this
The course is built on the Applied Quantum PQC Migration Framework, published openly under Creative Commons at pqcframework.org and written by the practitioners who teach here. Our faculty runs migration programs inside organizations doing this work now, and the course reflects what those programs run into.