Generative AI Privacy & Security OWASP & NIST for Devs

Master Generative AI data privacy with OWASP, NIST AI RMF, secure RAG, LLMOps, threat modeling and privacy testing.
What you'll learn

---

Neutralize the OWASP Top 10 vulnerabilities specific to Large Language Models before deployment.
Build Python-based middleware to automatically redact PII before it reaches external AI APIs.
Defend AI agentic workflows and chains against direct and indirect prompt injection attacks.
Implement Zero-Trust architecture and strict Role-Based Access Controls in RAG systems.
Requirements

---

Basic understanding of Python or JavaScript and familiarity with REST APIs. No prior cybersecurity expertise or advanced machine learning knowledge is required-we build the security layers from scratch.
Description

---

"This course contains the use of artificial intelligence."
Build GenAI applications that can demonstrate how sensitive information is controlled-not merely promise that it is.
Generative AI introduces new data flows across prompts, models, RAG systems, vector stores, tools, APIs, caches, and telemetry. This developer-focused course converts privacy and AI-risk guidance into technical architecture, implementation controls, tests, and review evidence.
Who this course is for

---

Backend Developers, Full-Stack Engineers, and AI Integration Specialists tasked with integrating LLMs into enterprise applications who need to guarantee data privacy and pass strict security compliance audits.
Homepage

---