How to implement an AI Software Bill of Materials (AI-SBOM) that captures the full component graph of a deployed AI system — base models, adapters, training datasets, and dependencies — and how to use it to manage supply chain risk and regulatory compliance.
A structured methodology for red teaming large language model applications — covering attack taxonomy, scoping, tooling (Garak, PyRIT, PromptBench), and how to translate findings into actionable security improvements.
A practical framework for implementing prompt injection detection and containment at the API gateway layer — covering input sanitisation, context isolation, output filtering, and anomaly-based detection for production LLM deployments.
How to apply zero trust principles to machine learning infrastructure — covering training pipeline access controls, model registry security, inference endpoint hardening, and secrets management for AI workloads.
Practical design patterns for building a prompt injection and jailbreak detection layer in front of production LLM deployments — covering rule-based filters, semantic classifiers, canary tokens, and output validation.