9 October 2026, 01:12 PM
AI security requires more than knowing how AI models work. Security professionals increasingly need to understand how LLM applications are built, where their attack surfaces exist, how AI agents interact with tools and data, and how those systems can be tested and protected.
A good training course should give learners enough technical context to understand AI systems and enough practical work to apply security concepts to real scenarios. Here are five AI security training courses worth considering in 2027.
1. Certified AI Security Expert (MSec-CAIS) – Modern Security
Certified AI Security Expert (MSec-CAIS) is a hands-on AI security course from Modern Security that covers the process of building, breaking, and defending AI applications.
The course starts with AI, ML, GenAI, and LLM fundamentals before moving into embeddings, vector databases, RAG, AI agents, MCP, LangChain, and LangSmith. Learners work with LLM APIs and build applications through practical labs rather than studying the concepts only in theory.
The security portion covers prompt injection, indirect prompt injection, sensitive information disclosure, agent attacks, MCP attacks, model backdoors, and AI supply-chain security. It also includes model signing, AIBOMs, dependency pinning, model scanning, and end-to-end AI security pipelines.
On the defensive side, students work with LLM guardrails, MCP gateways, prompt-injection defenses, and AI security testing with Garak. The course contains 40 lessons and is designed for security engineers, developers, penetration testers, red teamers, and technical leaders. No previous AI or LLM experience is required.
2. SANS SEC411: AI Security Principles and Practices
SANS SEC411 focuses on GenAI and LLM defense for cybersecurity professionals. The course covers AI security fundamentals, attack-surface analysis, the OWASP Top 10 for LLMs, prompt injection, jailbreaking, RAG manipulation, MCP security, context hijacking, and agentic workflows.
The training includes hands-on labs with more than 100 practical tasks and is designed for cybersecurity professionals entering AI and LLM defense. SANS also states that no prior AI experience is required.
This course is a good option for security practitioners who already understand cybersecurity and want to add AI-specific defensive skills.
3. OffSec AI-300: Advanced AI Red Teaming
OffSec's AI-300 is aimed at learners who want to approach AI security from an offensive perspective. The course covers attacks against generative AI applications, LLMs, RAG pipelines, AI agents, machine learning systems, and supporting infrastructure.
The training uses practical labs and realistic AI environments. It is intended primarily for experienced cybersecurity professionals, penetration testers, red teamers, and security engineers.
For professionals interested specifically in AI red teaming and offensive security testing, AI-300 provides a more specialised path than a general AI security course.
4. SANS SEC545: GenAI and LLM Application Security
SANS SEC545 focuses on securing applications built around generative AI and large language models. It is associated with the GIAC AI Platform Security (GAIPS) certification, which covers areas such as AI application architecture, agentic systems, model integrations, APIs, MLOps, MLSecOps, and AI risk management.
This makes SEC545 relevant for professionals who work close to AI application development and need to understand how security controls fit into the application lifecycle.
5. SANS SEC598: AI and Security Automation for Red, Blue, and Purple Teams
SEC598 takes a different approach by focusing on the use of AI for security operations. The course covers AI-assisted work across red, blue, and purple team activities and includes hands-on training.
SANS lists SEC598 as a six-day course with 36 CPEs and hands-on learning. A 2027 SANS event listing also shows the course paired with the GIAC AI Security Automation Engineer credential.
It is worth considering for security professionals who want to learn how AI can be incorporated into offensive and defensive security workflows rather than focusing solely on securing AI applications.
Choosing an AI Security Training Course
The right course depends on what you want to do with the skills.
MSec-CAIS is a broad hands-on option covering AI application development, offensive testing, threat modelling, and defensive techniques. SEC411 is focused on GenAI and LLM defense, while AI-300 is more suitable for professionals interested in AI red teaming. SEC545 focuses on GenAI and LLM application security, and SEC598 is aimed at using AI across security operations.
Before enrolling in a 2027 course, check the current syllabus, prerequisites, lab access, course format, and whether the material matches the type of AI security work you want to perform.
A good training course should give learners enough technical context to understand AI systems and enough practical work to apply security concepts to real scenarios. Here are five AI security training courses worth considering in 2027.
1. Certified AI Security Expert (MSec-CAIS) – Modern Security
Certified AI Security Expert (MSec-CAIS) is a hands-on AI security course from Modern Security that covers the process of building, breaking, and defending AI applications.
The course starts with AI, ML, GenAI, and LLM fundamentals before moving into embeddings, vector databases, RAG, AI agents, MCP, LangChain, and LangSmith. Learners work with LLM APIs and build applications through practical labs rather than studying the concepts only in theory.
The security portion covers prompt injection, indirect prompt injection, sensitive information disclosure, agent attacks, MCP attacks, model backdoors, and AI supply-chain security. It also includes model signing, AIBOMs, dependency pinning, model scanning, and end-to-end AI security pipelines.
On the defensive side, students work with LLM guardrails, MCP gateways, prompt-injection defenses, and AI security testing with Garak. The course contains 40 lessons and is designed for security engineers, developers, penetration testers, red teamers, and technical leaders. No previous AI or LLM experience is required.
2. SANS SEC411: AI Security Principles and Practices
SANS SEC411 focuses on GenAI and LLM defense for cybersecurity professionals. The course covers AI security fundamentals, attack-surface analysis, the OWASP Top 10 for LLMs, prompt injection, jailbreaking, RAG manipulation, MCP security, context hijacking, and agentic workflows.
The training includes hands-on labs with more than 100 practical tasks and is designed for cybersecurity professionals entering AI and LLM defense. SANS also states that no prior AI experience is required.
This course is a good option for security practitioners who already understand cybersecurity and want to add AI-specific defensive skills.
3. OffSec AI-300: Advanced AI Red Teaming
OffSec's AI-300 is aimed at learners who want to approach AI security from an offensive perspective. The course covers attacks against generative AI applications, LLMs, RAG pipelines, AI agents, machine learning systems, and supporting infrastructure.
The training uses practical labs and realistic AI environments. It is intended primarily for experienced cybersecurity professionals, penetration testers, red teamers, and security engineers.
For professionals interested specifically in AI red teaming and offensive security testing, AI-300 provides a more specialised path than a general AI security course.
4. SANS SEC545: GenAI and LLM Application Security
SANS SEC545 focuses on securing applications built around generative AI and large language models. It is associated with the GIAC AI Platform Security (GAIPS) certification, which covers areas such as AI application architecture, agentic systems, model integrations, APIs, MLOps, MLSecOps, and AI risk management.
This makes SEC545 relevant for professionals who work close to AI application development and need to understand how security controls fit into the application lifecycle.
5. SANS SEC598: AI and Security Automation for Red, Blue, and Purple Teams
SEC598 takes a different approach by focusing on the use of AI for security operations. The course covers AI-assisted work across red, blue, and purple team activities and includes hands-on training.
SANS lists SEC598 as a six-day course with 36 CPEs and hands-on learning. A 2027 SANS event listing also shows the course paired with the GIAC AI Security Automation Engineer credential.
It is worth considering for security professionals who want to learn how AI can be incorporated into offensive and defensive security workflows rather than focusing solely on securing AI applications.
Choosing an AI Security Training Course
The right course depends on what you want to do with the skills.
MSec-CAIS is a broad hands-on option covering AI application development, offensive testing, threat modelling, and defensive techniques. SEC411 is focused on GenAI and LLM defense, while AI-300 is more suitable for professionals interested in AI red teaming. SEC545 focuses on GenAI and LLM application security, and SEC598 is aimed at using AI across security operations.
Before enrolling in a 2027 course, check the current syllabus, prerequisites, lab access, course format, and whether the material matches the type of AI security work you want to perform.