AI Security Training in Bhutan

This instructor-led, live training in Bhutan (online or onsite) is designed for beginner to intermediate IT professionals who aim to understand and implement AI TRiSM within their organizations.

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

• Comprehend the fundamental concepts and significance of AI trust, risk, and security management.
• Identify potential risks associated with AI systems and employ mitigation strategies.
• Apply security best practices specific to AI technologies.
• Gain insight into regulatory compliance and ethical implications relevant to AI.
• Formulate strategies for effective AI governance and management.

This instructor-led training in Bhutan focuses on governance, identity management, and red-teaming for agentic AI systems. Advanced practitioners will learn to design secure deployments, implement least-privilege access, and conduct adversarial testing to mitigate real-world threats in production settings.

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

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

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

This instructor-led training in Bhutan empowers advanced professionals to secure TinyML pipelines on edge devices. The curriculum covers the implementation of privacy-preserving techniques, hardening models against adversarial threats, and applying best practices for secure data handling in constrained environments.

This instructor-led live training in Bhutan (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 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 in Bhutan (online or onsite) is designed 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.

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

• Understand and compare key privacy-preserving techniques in ML.
• Implement federated learning systems using open-source frameworks.
• Apply differential privacy for safe data sharing and model training.
• Use encryption and secure computation techniques to protect model inputs and outputs.

This instructor-led, live training in Bhutan (online or onsite) is designed for intermediate-level enterprise leaders who wish to understand how to govern and secure AI systems responsibly and in compliance with emerging global frameworks such as 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 instructor-led, live training in Bhutan (online or onsite) targets intermediate to advanced 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 instructor-led, live training in Bhutan (online or on-site) is designed for intermediate-level machine learning and cybersecurity professionals who wish to understand and mitigate emerging threats against AI models, using 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 such as adversarial attacks, inversion, and poisoning.
• Use tools like the Adversarial Robustness Toolbox (ART) to simulate attacks and test models.
• Apply practical defenses including adversarial training, noise injection, and privacy-preserving techniques.
• Design threat-aware model evaluation strategies in production environments.

This instructor-led, live training in Bhutan (online or onsite) is aimed at 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.

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 course offers a practical introduction to securing modern AI-powered applications, APIs, copilots, and autonomous agents. Participants 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 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 course teaches software developers how to securely build AI-powered applications by design. Participants learn 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 handling. The curriculum covers secure prompt design, RAG security, least-privilege access controls, guardrails, and red-team testing, enabling developers to build AI features that are secure, reliable, and resilient in real-world environments.

This instructor-led, live training in Bhutan (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 Bhutan (online or onsite) is designed for security and ML engineers who need to identify, test, and defend against attacks on ML models and applications powered by Large Language Models (LLMs).