Get AICERTS AI+ Security Level 1 Certified

AI+ Security Level 1 (AT-2101) is the entry credential in the AICERTS AI security track. It validates that you can identify, mitigate, and prevent AI-related security threats, and that you understand the safety practices and compliance expectations now attaching to AI systems.

IT professionals, cybersecurity practitioners early in their careers, AI developers, and compliance officers who need to understand where AI systems break and who is accountable when they do. It assumes general IT literacy rather than machine learning expertise.

How to detect vulnerabilities specific to AI systems, how to secure models against the common attack classes, how to apply ethical AI guidelines in practice, and how to fold AI security into workflows your organization already runs rather than bolting on a separate process.

Yes. The proctored exam tests AI threat types, risk mitigation strategies, and AI security practices. Your $200 exam voucher is included with enrollment, and you test online through remote proctoring rather than waiting for a test center slot.

Five days of live instruction, online or in person, with hands-on exercises throughout rather than a lecture series.

No. The course introduces how AI systems work at the level you need to secure them. What helps far more is existing security fundamentals, because much of the material is about applying familiar thinking, threat modeling and access control and data handling, to a new class of system.

Level 1 establishes the threat landscape and the defensive basics. Level 2 is hands-on offense and defense: MLSecOps, red teaming AI systems, and AI incident response. Level 3 moves to enterprise architecture, multi-model governance, and regulatory strategy. Start here unless you already work in security and want the practitioner depth of Level 2.

Because the failure modes are genuinely new. Prompt injection, training data poisoning, model extraction, and the supply chain around pretrained models do not map cleanly onto existing security controls, and a team that treats a model as ordinary software will miss all of them. That said, this augments a security foundation rather than replacing one.

Especially then. The expensive mistakes get made in the first deployments, when nobody has decided who owns model risk, what data goes in, or how output gets validated. Learning the threat landscape before you have three ungoverned systems in production is considerably cheaper than learning it afterward.