AI Security Framework
A comprehensive security framework for protecting AI/ML systems in production. Covers LLM prompt injection defense, model poisoning prevention, training data validation, RAG pipeline hardening, and AI governance controls. Aligned with OWASP Top 10 for LLM Applications and NIST AI Risk Management Framework — built from securing ML pipelines at NantHealth and enterprise AI deployments.
Framework Domains
Security controls across the entire AI/ML lifecycle.
Prompt Injection
Input sanitization patterns, system prompt hardening, output filtering, jailbreak detection, and guardrail implementation for production LLM applications.
Training Data
Data provenance tracking, poisoning detection, PII scrubbing pipelines, data lineage auditing, and compliance controls for training data governance.
MLOps Security
Model registry access controls, artifact signing, CI/CD pipeline hardening for ML workflows, experiment tracking security, and model versioning integrity.
RAG Hardening
Vector database access controls, retrieval boundary enforcement, context window manipulation prevention, and embedding model security for RAG pipelines.
Model Serving
API authentication for inference endpoints, rate limiting, model extraction prevention, adversarial input detection, and output content filtering.
AI Governance
Model cards and documentation, bias monitoring, explainability requirements, regulatory compliance (EU AI Act), and AI incident response procedures.
AI Security Framework
Free enterprise AI security framework covering LLM defense, ML pipeline protection, RAG hardening, and governance controls. Aligned with OWASP Top 10 for LLMs.
What Our Students Say
Real outcomes from cloud professionals who learned with Citadel Cloud.