Overview
Understand and apply the OWASP Top 10 security risks for large language model applications, Detect and mitigate vulnerabilities like prompt injection and insecure output handling, Design and implement secure architectures for LLM-powered systems, Build and document an LLM security risk register and mitigation plan
This course is ideal for AI developers, security engineers, MLOps professionals, product managers, and anyone responsible for designing or securing systems powered by large language models. It’s also suitable for technology leaders who want to understand LLM risks and align with best practices.
No prior AI security experience required. Basic familiarity with AI applications, software development, or cybersecurity concepts is helpful but not mandatory.
Are you working with large language models (LLMs) or generative AI systems and want to ensure they are secure, resilient, and trustworthy? This OWASP Top 10 for LLM Applications – 2025 Edition course is designed to equip developers, security engineers, MLOps professionals, and AI product managers with the knowledge and tools to identify, mitigate, and prevent the most critical security risks associated with LLM-powered systems. Aligned with the latest OWASP recommendations, this course covers real-world threats that go far beyond conventional application security—focusing on issues like prompt injection, insecure output handling, model denial of service, excessive agency, overreliance, model theft, and more.
Throughout this course, you’ll learn how to apply secure design principles to LLM applications, including practical methods for isolating user input, filtering and validating outputs, securing third-party plugin integrations, and protecting proprietary model IP. We’ll guide you through creating a comprehensive risk register and mitigation plan using downloadable templates, ensuring that your LLM solution aligns with industry best practices for AI security. You’ll also explore how to design human-in-the-loop (HITL) workflows, implement effective monitoring and anomaly detection strategies, and conduct red teaming exercises that simulate real-world adversaries targeting your LLM systems.
Whether you're developing customer support chatbots, AI coding assistants, healthcare bots, or legal advisory systems, this course will help you build safer, more accountable AI products. With a case study based on GenAssist AI—a fictional enterprise LLM platform—you’ll see how to apply OWASP principles end-to-end in realistic scenarios. By the end of the course, you will be able to document and defend your LLM security architecture with confidence.
Join us to master the OWASP Top 10 for LLMs and future-proof your generative AI projects!
Dr. Amar Massoud
PhD in computer science and IT manager with 35 years technical experience in various fields including IT Security, IT Governance, IT Service Management , Software Development, Project Management, Business Analysis and Software Architecture. I hold 80+ IT certifications such as :
ITIL 4 Master, ITIL 3 Expert
ISO 27001 Auditor, ComptIA Security+, GSEC, CEH, ECSA, CISM, CISSP, CISA
PGMP, MSP
PMP, PMI-ACP, Prince2 Practitioner, Praxis, Scrum Master
COBIT 2019 Implementor, COBIT 5 Assessor/Implementer
TOGAF certified
Lean Specialist, VSM Specialist
PMI RMP, ISO 31000 Risk Manager, ISO 22301 Lead Auditor
PMI-PBA, CBAP
Lean Six Sigma Black Belt, ISO 9001 Implementer
Azure Administrator, Azure DevOps Expert, AWS Practitioner
And many more.