Choose the path that matches your work

Learn the difference between using AI for security, securing an AI application, and protecting the model lifecycle.

Create an isolated workspace, a written scope, synthetic data, and a stop procedure before running security experiments.

Learn the small set of Python and data-handling patterns needed to explore logs, build features, and preserve an audit trail.

Build a practical mental model of supervised learning, anomaly detection, thresholds, drift, and the cost of false decisions.

Follow a request through instructions, retrieval, tools, memory, output parsing, and the final business action.

Turn a system map into abuse cases, control hypotheses, and a risk-based test plan.

Move beyond jailbreak tricks and test whether untrusted content can cross an authorization or action boundary.

Combine model-assisted review with static analysis, tests, dependency evidence, and developer context.

Use the NIST taxonomy to reason about adversarial machine learning across training, deployment, and inference.

Combine human creativity, automated probes, deterministic checks, and regression suites without mistaking volume for coverage.

Build anomaly and AI-system monitoring that produces actionable investigation paths instead of unexplained scores.

Design an agent runtime where untrusted instructions cannot silently become broad authority.

Protect models, datasets, notebooks, registries, pipelines, endpoints, secrets, and observability across the deployment lifecycle.

Connect technical controls to incident handling, responsible disclosure, risk ownership, and the EU operating context.