Securing AI Applications: From Threats to Controls

AI systems introduce security challenges that are fundamentally different from anything traditional cybersecurity was built to handle. LLM applications, retrieval pipelines, vector databases, and agent based automations create new vulnerabilities that can expose sensitive data, enable unauthorized actions, and compromise entire workflows. This course gives you a complete and practical framework for securing GenAI systems in real engineering environments.You will learn how modern AI threats operate, how attackers exploit prompts, tools, and connectors, and how data can leak through embeddings, retrieval layers, or model outputs. The course walks you through every layer of the AI stack and shows you how to apply the right defenses at the right places, using a structured and repeatable security approach.What you will learnThe full AI Security Reference Architecture across model, prompt, data, tools, and monitoring layersHow GenAI attacks work, including injection, leakage, misuse, and unsafe tool executionHow to use AI firewalls, filtering engines, and policy controls for runtime protectionAI SDLC best practices for dataset security, evaluations, red teaming, and version managementData governance strategies for RAG pipelines, ACLs, encryption, filtering, and secure embeddingsIdentity and access patterns that protect AI endpoints and tool integrationsAI Security Posture Management for risk scoring, drift detection, and policy enforcementObservability and evaluation workflows that track model behavior and reliabilityWhat is includedArchitecture diagrams and control mapsModel and RAG threat modeling worksheetsGovernance templates and security policiesChecklists for AI SDLC, RAG security, and data protectionEvaluation and firewall comparison fram