Security and Privacy in TinyML Applications Training Course

This instructor-led live training in Norway (online or onsite) is designed for intermediate-level enterprise leaders who want to learn how to responsibly govern and secure AI systems in compliance with emerging global frameworks like the EU AI Act, GDPR, ISO/IEC 42001, and the U.S. Executive Order on AI.

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

• Understand the legal, ethical, and regulatory risks of using AI across departments.
• Interpret and apply major AI governance frameworks (EU AI Act, NIST AI RMF, ISO/IEC 42001).
• Establish security, auditing, and oversight policies for AI deployment in the enterprise.
• Develop procurement and usage guidelines for third-party and in-house AI systems.

This course offers a practical introduction to securing modern AI-powered applications, APIs, copilots, and autonomous agents. Participants will 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 Norway (online or onsite) is designed for beginner to intermediate IT professionals aiming to understand and implement AI TRiSM within their organizations.

Upon completion of this training, participants will be able to:

• Understand the core concepts and significance of AI trust, risk, and security management.
• Identify and mitigate risks linked to AI systems.
• Apply security best practices specific to AI.
• Navigate regulatory compliance and ethical issues related to AI.
• Formulate strategies for effective AI governance and management.

This instructor-led, live training in Norway (online or onsite) targets intermediate to advanced AI developers, architects, and product managers looking to identify and mitigate risks associated with LLM-powered applications. Key risks addressed include prompt injection, data leakage, and unfiltered output. The course emphasizes incorporating security controls such as input validation, human-in-the-loop oversight, and output guardrails.

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

• Understand the core vulnerabilities of LLM-based systems.
• Apply secure design principles to LLM app architecture.
• Use tools such as Guardrails AI and LangChain for validation, filtering, and safety.
• Integrate techniques like sandboxing, red teaming, and human-in-the-loop review into production-grade pipelines.

This course teaches software developers how to build AI-powered applications securely by design. Participants learn how to protect chatbots, copilots, RAG pipelines, and AI agents against AI-specific threats such as prompt injection, data poisoning, tool abuse, secret leakage, and insecure model output. The course covers secure prompt design, RAG security, least-privilege access, guardrails, and red-team testing, helping developers build AI features that are secure, reliable, and resilient in real-world environments.

This instructor-led, live training in Norway (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 instructor-led, live training in Norway (online or onsite) is aimed at security engineers and compliance officers who wish to harden EXO deployments, control model access, and govern AI workloads running entirely on-premise.

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

Upon completion of this training, participants will be able to:

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

This instructor-led, live training in Norway (online or onsite) is aimed at security and ML engineers who need to identify, test, and defend against attacks on ML models and LLM-powered 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 instructor-led, live training in Norway (online or onsite) is intended for advanced professionals who wish to implement and evaluate techniques such as federated learning, secure multiparty computation, homomorphic encryption, and differential privacy in real-world machine learning pipelines.

Upon completion of this training, participants will be capable of:

• Grasping and comparing essential privacy-preserving methods in ML.
• Building federated learning systems using open-source frameworks.
• Employing differential privacy to ensure safe data sharing and model training.
• Leveraging encryption and secure computation methods to safeguard model inputs and outputs.

This instructor-led live training in Norway (online or onsite) is aimed at advanced-level security professionals and ML specialists who wish to simulate attacks on AI systems, uncover vulnerabilities, and enhance the robustness of deployed AI models.

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

• Simulate real-world threats to machine learning models.
• Generate adversarial examples to test model robustness.
• Assess the attack surface of AI APIs and pipelines.
• Design red teaming strategies for AI deployment environments.

This instructor-led, live training in Norway (online or onsite) is designed for intermediate-level engineers and security professionals who wish to secure AI models deployed at the edge against threats such as tampering, data leakage, adversarial inputs, and physical attacks.

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

• Identify and assess security risks associated with edge AI deployments.
• Apply techniques for tamper resistance and encrypted inference.
• Harden models deployed at the edge and secure data pipelines.
• Implement threat mitigation strategies tailored to embedded and constrained systems.