Free Udemy Course __ AI Security Fundamentals: Risks, Frameworks & Tools

Master AI threat modeling, SDLC integration, and compliance for enterprise-grade systems

4.5 (1,000+ students enrolled) English
Other Machine Learning
AI Security Fundamentals: Risks, Frameworks & Tools

What You'll Learn

  • Identify modern GenAI risks and understand how attackers target LLM and RAG pipelines
  • Apply a layered AI security design to strengthen every component of an AI application
  • Create detailed AI threat models and link each threat to concrete control measures
  • Configure AI firewalls and runtime guardrails to manage prompts, responses, and tool actions
  • Embed security practices into AI development workflows, including dataset checks and eval automation
  • Implement robust identity, authorization, and scoped access for AI endpoints and integrations
  • Enforce data governance for RAG systems through access rules, tagging, and secure retrieval patterns
  • Use SPM platforms to maintain visibility over models, datasets, connectors, and policy violations
  • Build observability pipelines to track prompts, responses, decisions, and model quality metrics
  • Assemble a unified AI security strategy and translate it into clear 30, 60, and 90 day actions

Requirements

  • No prior knowledge required
  • A computer with internet access
  • Eagerness to learn

Who This Course is For

  • Developers integrating AI capabilities into existing or new products
  • Machine learning engineers maintaining model workflows and RAG systems
  • System and cloud architects designing secure AI infrastructures
  • Security analysts and DevSecOps teams responsible for safeguarding AI services
  • Team leads and decision makers who oversee AI initiatives and compliance requirements

Your Instructor

Experienced Instructor

Professional Educator

An experienced instructor with expertise in this field and a passion for teaching.

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