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.