OWASP Top 10 for Large Language Model (LLM) Applications
in Artificial IntelligenceWhat you will learn?
Identify, analyze, and mitigate vulnerabilities outlined in the OWASP Top 10 for LLM Applications.
Implement secure-by-design practices across data pipelines, prompts, outputs, and runtime components.
Apply defense-in-depth strategies and integrate LLM security into the SDLC.
Conduct effective LLM security testing, including red teaming, adversarial evaluations, and monitoring.
Target Audience
Software Developers implementing LLM-enabled features or AI-driven functionality.
Security Engineers and Application Security Professionals responsible for AI system protection.
DevOps/MLOps Engineers supporting model deployment, infrastructure, and operational security.
Technical Product Managers guiding secure AI product development and compliance.
About this course
As organizations rapidly adopt Large Language Models (LLMs) across products, workflows, and customer-facing applications, securing these systems has become a mission-critical priority. Securing the AI Revolution: Defense-in-Depth for Large Language Models is a comprehensive, practitioner-focused course designed to equip developers, architects, and security professionals with the skills to identify, assess, and mitigate the unique risks associated with LLM-powered applications.
This course goes beyond high-level theory. It provides a structured, hands-on exploration of the OWASP Top 10 for LLM Applications—an essential framework for understanding the most significant vulnerabilities emerging in the modern AI landscape. Through real-world scenarios, guided whiteboard solutions, and defense-driven design principles, you will learn how to secure LLM inputs, outputs, data pipelines, supply chains, vector databases, and runtime operations.
Participants will gain the ability to recognize threats such as prompt injection, sensitive data leakage, model poisoning, excessive agency, and unbounded resource consumption. You will also learn how to integrate LLM security into the Secure Development Lifecycle (SDLC), implement defense-in-depth strategies, and apply best practices for red teaming, adversarial testing, monitoring, and incident response.
By the end of the course, you will be equipped not only to understand LLM security risks but to proactively prevent them. Whether you are building new AI features or securing existing applications, this course will help you transform your approach—from simply deploying LLMs to confidently engineering safe, reliable, and resilient AI systems.
Requirements
Basic understanding of software development or application security concepts.
Familiarity with AI/ML systems or experience working with LLM-based tools is helpful but not required.
