AI Governance, Compliance, and Security for Enterprise Leaders Training Course

This course provides a practical introduction to securing modern AI-powered applications, APIs, copilots, and autonomous agents. Participants learn how AI security differs from traditional web security, explore common AI-specific threats such as prompt injection, RAG poisoning, and agent abuse, and understand how to protect AI systems using layered defenses including WAFs, AI gateways, API security, and guardrails. Through hands-on labs and real-world examples, students gain the skills to identify AI attack patterns, secure LLM-based applications, and deploy effective runtime defenses for production environments.

This instructor-led, live training in Denmark (online or onsite) is aimed at beginner-level to intermediate-level IT professionals who wish to understand and implement AI TRiSM in their organizations.

By the end of this training, participants will be able to:

• Grasp the key concepts and importance of AI trust, risk, and security management.
• Identify and mitigate risks associated with AI systems.
• Implement security best practices for AI.
• Understand regulatory compliance and ethical considerations for AI.
• Develop strategies for effective AI governance and management.

This live, instructor-led session in Denmark (online or in-person) is designed for intermediate to advanced AI developers, architects, and product managers seeking to identify and mitigate risks in LLM-driven applications. It covers threats such as prompt injection, data leaks, and unregulated output, while teaching the implementation of security controls like input validation, human-in-the-loop supervision, and output guardrails.

Upon completion, participants will be equipped to:

• Recognize fundamental vulnerabilities within LLM-based systems.
• Implement secure design principles in LLM application architectures.
• Leverage tools like Guardrails AI and LangChain for validation, filtering, and safety measures.
• Incorporate techniques such as sandboxing, red teaming, and human-in-the-loop reviews into production-ready pipelines.

This course instructs software developers on building AI-powered applications with security as a core design principle. Participants learn to defend chatbots, copilots, RAG pipelines, and AI agents against AI-specific threats, including prompt injection, data poisoning, tool abuse, secret leakage, and insecure model output. The syllabus covers secure prompt design, RAG security, least-privilege access, guardrails, and red-team testing, enabling developers to create AI features that are secure, reliable, and resilient in production environments.

This instructor-led live training in Denmark (online or onsite) is aimed at intermediate-level AI and cybersecurity professionals who wish to understand and address the security vulnerabilities specific to AI models and systems, particularly in highly regulated industries such as finance, data governance, and consulting.

By the end of this training, participants will be able to:

• Understand the types of adversarial attacks targeting AI systems and methods to defend against them.
• Implement model hardening techniques to secure machine learning pipelines.
• Ensure data security and integrity in machine learning models.
• Navigate regulatory compliance requirements related to AI security.

This live, instructor-led training in Denmark—delivered either online or onsite—targets security engineers and compliance professionals aiming to strengthen EXO deployments, manage model access rights, and supervise AI workloads running exclusively on-premise.

This instructor-led live training in Denmark (online or onsite) is tailored for beginner-level IT security, risk, and compliance professionals seeking to master foundational AI security concepts, threat vectors, and global frameworks such as the NIST AI RMF and ISO/IEC 42001.

By the end of the training, participants will be able to:

• Understand the specific security risks associated with AI systems.
• Identify threat vectors like adversarial attacks, data poisoning, and model inversion.
• Apply foundational governance models, including the NIST AI Risk Management Framework.
• Align AI usage with emerging standards, compliance guidelines, and ethical principles.

This instructor-led, live training in Denmark (online or onsite) targets security and ML engineers focused on identifying, testing, and defending against attacks on ML models and LLM-driven applications.

Based on the latest OWASP GenAI Security Project guidance, participants will learn to identify, assess, and mitigate AI-specific threats through hands-on exercises and real-world scenarios.

This live, instructor-led training in Denmark, offered online or on-site, targets advanced professionals aiming to implement and assess techniques such as federated learning, secure multiparty computation, homomorphic encryption, and differential privacy within real-world Machine Learning pipelines.

Upon completion of this program, participants will be equipped to:

• Evaluate and contrast fundamental privacy-preserving methodologies in Machine Learning.
• Deploy federated learning systems leveraging open-source frameworks.
• Utilize differential privacy to ensure secure data exchange and robust model training.
• Leverage encryption and secure computation methods to shield model inputs and outputs.

This instructor-led, live training in Denmark (available online or onsite) is tailored for advanced security professionals and ML experts aiming to simulate AI system attacks, detect vulnerabilities, and bolster the robustness of live AI models.

By the end of the session, participants will have the ability to:

• Emulate real-world threats targeting machine learning models.
• Produce adversarial examples to assess model robustness.
• Analyze the attack surface of AI APIs and pipelines.
• Develop strategic red teaming approaches for AI deployments.

This instructor-led, live training in Denmark (online or on-site) is designed for intermediate-level engineers and security professionals. It provides the skills necessary to secure AI models deployed at the edge against various threats, including tampering, data leakage, adversarial inputs, and physical attacks.

By the end of this session, participants will have the capability to:

• Identify and assess security risks in edge AI deployments.
• Apply tamper resistance and encrypted inference techniques.
• Harden edge-deployed models and secure data pipelines.
• Implement threat mitigation strategies specific to embedded and constrained systems.

This instructor-led, live training in Denmark (online or on-site) is designed for intermediate-level machine learning and cybersecurity professionals seeking to understand and mitigate emerging threats to AI models, utilizing both conceptual frameworks and hands-on defenses like robust training and differential privacy.

By the end of this training, participants will be able to:

• Identify and classify AI-specific threats, including adversarial attacks, inversion, and poisoning.
• Employ tools such as the Adversarial Robustness Toolbox (ART) to simulate attacks and test model integrity.
• Apply practical defenses, such as adversarial training, noise injection, and privacy-preserving techniques.
• Create threat-aware model evaluation strategies suitable for production environments.