This AI Cybersecurity Course at igmGuru moves you from core AI risk concepts to hands-on defense engineering. You'll red-team machine learning models, secure LLM applications against prompt injection and data leakage, map controls to NIST AI RMF and ISO/IEC 42001, and build monitoring for agentic AI systems. With mentor-led sessions, real datasets, and scenario-based labs, you'll leave able to assess, secure, and govern AI systems in production, not just recite frameworks.
This course is built to work for both newcomers and experienced security professionals. If you're new to the field, our AI Cybersecurity for Beginners primer in Module 1 covers core AI/ML concepts and foundational security terminology before you move into certification-level content. To get the most from the rest of the course, you should ideally have:
No prior AI development experience is required. If you understand basic security concepts, our labs will bring you up to speed on the AI-specific risks layered on top.
This AI Cybersecurity Online Training program is designed for professionals who need to secure the AI systems their organizations are rapidly deploying. It's a strong fit if you are:
AI security has moved from a niche specialty to a core expectation for security teams, and employers are actively hiring to close the gap between AI expertise and cybersecurity expertise. Typical roles you can pursue after this training include:
Professionals choose igmGuru's AI Cybersecurity Online Course to build defensible, framework-aligned skills fast. Here's what you get:
This AI Cybersecurity Training prepares you for the fast-growing landscape of recognized AI security credentials, including the Certified AI Security Specialist (CAISS) and ISACA's Advanced in AI Security Management (AAISM), which validates expertise across AI Governance, AI Risk Management, and AI Technologies and Controls (note: AAISM requires an active CISM or CISSP as a prerequisite). igmGuru's modules and labs are mapped to the frameworks these exams are built on - NIST AI RMF, ISO/IEC 42001, and the OWASP Top 10 for LLM Applications - so you build exam-ready knowledge alongside practical, job-ready skills.
On completing the training, you'll also receive an igmGuru Course Completion Certificate recognizing your hands-on AI security assessment and red-teaming project work.
AI cybersecurity focuses on securing AI and machine learning systems themselves, not just the infrastructure around them. It covers risks like adversarial attacks, prompt injection, and data poisoning that don't exist in traditional software, alongside using AI as a defensive tool for faster threat detection.
No. The course starts with an AI Cybersecurity for Beginners primer covering core AI/ML concepts, then builds toward advanced adversarial testing and governance skills, so a security background matters more than an AI or data science background.
The course maps to the frameworks behind leading credentials such as CAISS and ISACA's AAISM, along with NIST AI RMF and ISO/IEC 42001-aligned assessments. Note that AAISM specifically requires an active CISM or CISSP before you can sit the exam.
The course runs 45 hours total, combining live instructor-led sessions with hands-on labs and a capstone project. Most learners complete it in 6 to 8 weeks on a part-time schedule.
Yes. Module 3 is dedicated to LLM and generative AI security, covering prompt injection, jailbreaking, and the OWASP Top 10 for LLM Applications in depth.
Yes. Security spending is rising sharply as organizations defend against AI-enhanced attacks while also securing their own AI deployments, and most professionals today are trained in either cybersecurity or AI, not both - exactly the gap this course closes.
Yes. Classes run on flexible weekday and weekend batches, all sessions are recorded for lifetime access, and labs are self-paced so you can practice around your schedule.
Graduates typically pursue roles such as AI Security Engineer, AI/ML Security Analyst, SOC Analyst focused on AI threat detection, or AI Governance and Risk Compliance Specialist.
Yes. You'll run adversarial attacks against sample models, red-team an LLM application, and complete a capstone AI security assessment using MITRE ATLAS methodology.
