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

This course offers a practical introduction to securing modern AI-driven applications, APIs, copilots, and autonomous agents. Participants explore how AI security diverges from traditional web security, examine specific threats like prompt injection, RAG poisoning, and agent abuse, and learn to protect AI systems using layered defenses such as WAFs, AI gateways, API security, and guardrails. Through hands-on labs and real-world scenarios, students develop 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 Thailand (online or onsite) is tailored for IT professionals from beginner to intermediate levels who aim to understand and implement AI TRiSM in their respective organizations.

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

• Comprehend the core principles and significance of managing trust, risk, and security in AI.
• Recognize and reduce risks inherent in AI systems.
• Apply established security best practices to AI deployments.
• Navigate regulatory compliance and ethical implications surrounding AI.
• Formulate strategies for robust AI governance and operational management.

This live, instructor-led training in Thailand (online or onsite) is designed for intermediate to advanced AI developers, architects, and product managers aiming to identify and mitigate risks associated with LLM-powered applications. It addresses threats such as prompt injection, data leakage, and unfiltered output, while guiding the integration of security controls like input validation, human-in-the-loop oversight, and output guardrails.

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

• Understand the core vulnerabilities of LLM-based systems.
• Apply secure design principles to LLM app architecture.
• Utilize 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 with inherent security. Participants learn to protect chatbots, copilots, RAG pipelines, and AI agents from AI-specific threats, including prompt injection, data poisoning, tool abuse, secret leakage, and insecure model outputs. 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 production environments.

This instructor-led, live training in Thailand (online or onsite) targets intermediate AI and cybersecurity professionals aiming to master the security vulnerabilities associated with AI models and systems, especially within highly regulated sectors like finance, data governance, and consulting.

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

• Recognize adversarial attack vectors against AI systems and implement corresponding defense mechanisms.
• Utilize model hardening techniques to protect machine learning pipelines.
• Maintain data security and integrity within machine learning frameworks.
• Comply with regulatory requirements pertaining to AI security.

This live, instructor-led training in Thailand (delivered online or on-site) targets security engineers and compliance officers seeking to fortify EXO deployments, manage model access, and govern on-premise AI workloads.

This live, instructor-led training in Thailand (available online or on-site) is tailored for entry-level professionals in IT security, risk, and compliance. It aims to foster a deep understanding of core AI security concepts, threat vectors, and global standards like the NIST AI RMF and ISO/IEC 42001.

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

• Understand the unique security challenges posed by AI systems.
• Identify specific threats, such as adversarial attacks, data poisoning, and model inversion.
• Apply core governance models, including the NIST AI Risk Management Framework.
• Align AI deployments with emerging standards, regulatory guidelines, and ethical norms.

This live, instructor-led course in Thailand (online or onsite) is designed for security and ML engineers who need to identify, test, and defend ML models and LLM-powered applications against emerging threats.

Aligned with the latest OWASP GenAI Security Project guidance, this training enables participants to identify, assess, and mitigate AI-specific threats through practical exercises and real-world scenario analysis.

This instructor-led, live training in Thailand (online or onsite) is targeted at advanced professionals aiming to implement and evaluate techniques including federated learning, secure multiparty computation, homomorphic encryption, and differential privacy in practical machine learning pipelines.

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

• Understand and distinguish between 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, conducted in Thailand (either online or onsite), is designed for senior security professionals and ML experts who aim to simulate attacks on AI systems, detect vulnerabilities, and improve the robustness of deployed AI models.

By the conclusion of this training, participants will be capable of:

• Replicating real-world threats to machine learning models.
• Producing adversarial examples to challenge model robustness.
• Analyzing the attack surface of AI APIs and pipelines.
• Crafting red teaming strategies for AI deployment contexts.

This instructor-led live training, conducted in Thailand (either online or on-site), targets intermediate-level engineers and security specialists looking to secure edge-deployed AI models against threats such as tampering, data exfiltration, adversarial inputs, and physical breaches.

By the end of the program, attendees will possess the ability to:

• Detect and evaluate security risks associated with edge AI deployments.
• Implement tamper-resistant features and encrypted inference protocols.
• Fortify models at the edge and safeguard data pipelines.
• Deploy threat mitigation strategies appropriate for embedded and constrained systems.

Delivered by an instructor in Thailand (online or onsite), this session targets mid-level ML and cybersecurity experts looking to grasp and counter new risks to AI models, combining conceptual understanding with practical defenses such as robust training and differential privacy.

Upon completion, participants will be equipped to:

• Recognize and categorize AI-specific threats like adversarial attacks, inversion, and poisoning.
• Utilize tools like the Adversarial Robustness Toolbox (ART) to simulate attacks and evaluate models.
• Implement practical countermeasures such as adversarial training, noise injection, and privacy-preserving methods.
• Develop threat-aware model assessment strategies for production environments.