Privacy-Preserving Machine Learning Training Course

This instructor-led live training (online or onsite) is targeted at intermediate-level enterprise leaders seeking to understand how to responsibly govern and secure AI systems in compliance with 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 diverges 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 like WAFs, AI gateways, API security, and guardrails. Through hands-on labs and real-world examples, students will gain the skills to identify AI attack patterns, secure LLM-based applications, and deploy effective runtime defenses in production environments.

This instructor-led live training, located in South Korea (offered online or onsite), is designed for IT professionals at beginner to intermediate levels who wish to understand and implement AI TRiSM in their organizations.

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

• Comprehend the fundamental concepts and significance of managing trust, risk, and security in AI.
• Recognize and address risks inherent to AI systems.
• Apply security best practices specifically tailored for AI technologies.
• Gain insight into regulatory compliance and ethical standards governing AI.
• Formulate strategies for effective AI governance and operational management.

This instructor-led, live training in South Korea (online or onsite) is aimed at intermediate-level to advanced-level AI developers, architects, and product managers who wish to identify and mitigate risks associated with LLM-powered applications, including prompt injection, data leakage, and unfiltered output, while incorporating security controls like 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 instructs software developers on how to securely design and build AI-powered applications. Participants will learn to protect chatbots, copilots, RAG pipelines, and AI agents against AI-specific threats like prompt injection, data poisoning, tool abuse, secret leakage, and insecure model output processing. The course 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 real-world environments.

This instructor-led, live training in South Korea (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 South Korea (online or onsite) is designed for security engineers and compliance officers who wish to strengthen EXO deployments, control model access, and govern AI workloads running entirely on-premise.

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

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

• Understand the unique security risks introduced by AI systems.
• Identify threat vectors such as adversarial attacks, data poisoning, and model inversion.
• Apply foundational governance models like the NIST AI Risk Management Framework.
• Align AI use with emerging standards, compliance guidelines, and ethical principles.

This instructor-led, live training in South Korea (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.

Aligned with the latest OWASP GenAI Security Project guidance, this course enables participants to detect, evaluate, and mitigate AI-specific threats through interactive exercises and realistic scenario-based learning.

This instructor-led, live training in South Korea (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 South Korea (online or onsite) is aimed at 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 end of this training, participants will be able 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, conducted in South Korea (online or in-person), is tailored for intermediate-level machine learning and cybersecurity professionals. The goal is to help participants understand and mitigate emerging threats against AI models through conceptual frameworks and hands-on defenses like robust training and differential privacy.

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

• Identifying and classifying AI-specific threats, including adversarial attacks, inversion, and data poisoning.
• Utilizing tools such as the Adversarial Robustness Toolbox (ART) to simulate attacks and evaluate model resilience.
• Implementing practical defense mechanisms, such as adversarial training, noise injection, and privacy-preserving strategies.
• Developing threat-aware model evaluation strategies suitable for production environments.