They provide recognized structures for governing AI risk, defining controls, and demonstrating compliance and ethical AI use in organizational settings.
The course emphasizes applying established frameworks to ensure AI risk management is consistent and defensible. The NIST AI Risk Management Framework supports a structured approach to identifying and managing AI risks through governance and lifecycle controls. The EU AI Act provides regulatory expectations for AI systems, influencing risk classification, compliance responsibilities, transparency requirements, and oversight.
Together, they help organizations define governance, implement risk controls, and produce evidence that AI systems are managed responsibly and in line with compliance obligations.
Use frameworks as your operating system: define roles, checkpoints, documentation, and metrics once—then apply them across AI projects to scale risk management.
“Frameworks turn principles into repeatable controls and evidence.”
Expert Trainer
Expert Trainer
The NIS 2 Directive aims to strengthen cybersecurity and resilience across critical infrastructure and essential services by setting clearer security and governance expectations.
Effective AI governance defines clear roles, risk tiers, approval workflows, and ethical principles. It enables responsible innovation while managing bias, privacy, transparency, and accountability risks.
CISA is a globally recognized certification for information systems auditors. It validates competence in IT auditing, governance, risk management, and information security.
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