Certified AI Security Expert (MSec-CAIS) Course Reviews & Provider Details

Modern Security is an AI Security & Cybersecurity Academy focused on practical, engineering-first security education for security professionals, developers, penetration testers, cybersecurity practitioners, and technical leaders. Modern Security was founded to bridge the gap between modern engineering practices and cybersecurity, with a particular focus on AI, LLMs, cloud-native systems, and emerging security challenges. Training is available online and on-site for professionals and teams worldwide. Modern Security's flagship Certified AI Security Expert (MSec-CAIS) is a hands-on AI security certification designed to teach learners how to build, assess, attack, test, and defend AI and LLM-based applications. The course contains 40 lessons and combines video instruction with practical labs and real-world security scenarios. No prior AI or LLM experience is required, making the program suitable for learners who are new to AI security as well as experienced security practitioners looking to expand their skills. The program begins with the foundations of AI, machine learning, generative AI, and large language models, before moving into practical development and security concepts. Learners work with LLM APIs, embeddings, vector databases, Retrieval-Augmented Generation (RAG), LangChain, LangSmith, agentic systems, tools and function calls, and Model Context Protocol (MCP). Hands-on projects include building an internal security chatbot using RAG, developing an AI security tool, and building a web security scanner agent that uses an LLM to orchestrate security tools. The offensive security portion focuses on real-world AI and LLM attack techniques. Topics include prompt injection, indirect prompt injection, sensitive information disclosure, authorization issues, MCP attacks, agentic architecture attacks, AI application threat modeling, and model backdoors. Learners also explore AI supply-chain risks, including Hugging Face models, MLFlow, model backdoors, model signing with Sigstore, AI Bill of Materials (AIBOM), dependency pinning, model scanning, and end-to-end AI supply-chain security. The defensive portion covers practical techniques for securing AI applications, including LLM guardrails, MCP gateways, observability and detection, prompt sanitization, robust prompt design, model monitoring, input/output controls, and architecture-level security improvements. Learners revisit the attacks covered during the course and work through defensive approaches at the application and architecture layers. The course also includes LLM security testing with Garak, including testing standalone LLMs, REST APIs, and guardrails. The certification is designed for security engineers, developers, penetration testers and red teamers, cybersecurity professionals, and technical leaders who want to understand how modern AI systems work and how to secure them. Participants receive a certificate of completion and can complete the program at their own pace. Modern Security's broader training areas include AI and LLM security, threat modeling and secure design, cloud-native security, secure software development lifecycle (SSDLC), red and blue team collaboration, adversarial testing and model abuse, API and microservices security, and practical security engineering. The academy's approach emphasizes hands-on learning, real-world labs, and actionable security practices rather than theory alone. Modern Security's AI security training is taught by practicing security professionals with experience in product security, red teaming, application security, security research, and AI security. The MSec-CAIS instructor, Harish Ramadoss, has experience in product security, red teaming, and security research, including previous work with Trustwave SpiderLabs and Rippling. He has also presented security research and training at conferences including Black Hat, DEF CON, and HITB. https://modernsecurity.io