AI Security Training in Ho Chi Minh City

The AAISM framework provides an advanced approach to assessing, governing, and managing security risks associated with artificial intelligence systems.

This instructor-led live training, available either online or on-site, is designed for experienced professionals seeking to establish effective security controls and governance practices within enterprise AI environments.

Upon completing this program, participants will be equipped to:

• Evaluate security risks related to AI using recognized industry methodologies.
• Implement governance models that support the responsible deployment of AI.
• Ensure AI security policies align with organizational objectives and regulatory requirements.
• Strengthen resilience and accountability across AI-driven operations.

Course Format

• Instructor-led lectures enriched by expert analysis.
• Hands-on workshops and assessment-based activities.
• Applied exercises utilizing real-world AI governance scenarios.

Course Customization Options

• To arrange tailored training that aligns with your organization’s specific AI strategy, please contact us to customize the curriculum.

This instructor-led live training in Ho Chi Minh City (online or onsite) targets beginner to intermediate IT professionals who wish to understand and implement AI TRiSM in their organizations.

By the end 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 associated with AI systems.
• Apply security best practices for AI.
• Comprehend regulatory compliance and ethical considerations for AI.
• Develop strategies for effective AI governance and management.

This course explores the governance, identity management, and adversarial testing required for agentic AI systems, emphasizing enterprise-safe deployment patterns and practical red-teaming methodologies.

Delivered as an instructor-led live training (available online or onsite), this program is designed for advanced practitioners aiming to design, secure, and evaluate agent-based AI systems within production environments.

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

• Defining governance models and policies to ensure the safe deployment of agentic AI.
• Designing non-human identity and authentication flows that enforce least-privilege access for agents.
• Implementing access controls, audit trails, and observability frameworks tailored to autonomous agents.
• Planning and executing red-team exercises to identify misuse scenarios, escalation paths, and data exfiltration risks.
• Mitigating common threats to agentic systems through policy enforcement, engineering controls, and continuous monitoring.

Format of the Course

• Interactive lectures combined with threat-modeling workshops.
• Hands-on labs focusing on identity provisioning, policy enforcement, and adversary simulation.
• Red-team/blue-team exercises followed by an end-of-course assessment.

Course Customization Options

• To request a customized training session for this course, please contact us to arrange details.

This instructor-led, live training in Ho Chi Minh City (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 Ho Chi Minh City (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.

TinyML refers to the deployment of machine learning models on low-power, resource-constrained devices operating at the network edge.

This instructor-led, live training (available online or onsite) is designed for advanced-level professionals who want to secure TinyML pipelines and implement privacy-preserving techniques in edge AI applications.

Upon completing this course, participants will be able to:

• Identify security risks specific to on-device TinyML inference.
• Implement privacy-preserving mechanisms for edge AI deployments.
• Harden TinyML models and embedded systems against adversarial threats.
• Apply best practices for secure data handling in constrained environments.

Format of the Course

• Engaging lectures supported by expert-led discussions.
• Practical exercises emphasizing real-world threat scenarios.
• Hands-on implementation using embedded security and TinyML tooling.

Course Customization Options

• Organizations may request a tailored version of this training to align with their specific security and compliance needs.

This instructor-led, live training in Ho Chi Minh City (online or onsite) targets intermediate-level engineers and security professionals seeking to secure AI models deployed at the edge against threats such as tampering, data leakage, adversarial inputs, and physical attacks.

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

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

This instructor-led, live training in Ho Chi Minh City (online or onsite) is designed for advanced professionals aiming to implement and assess methods like federated learning, secure multiparty computation, homomorphic encryption, and differential privacy within real-world machine learning workflows.

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

• Comprehend and compare essential privacy-preserving methods in ML.
• Build federated learning systems using open-source tools.
• Utilize differential privacy to ensure safe data sharing and model training.
• Employ encryption and secure computation methods to protect model inputs and outputs.

Artificial Intelligence (AI) introduces new dimensions of operational risk, governance challenges, and cybersecurity exposure for government agencies and departments.

This instructor-led, live training (online or onsite) is aimed at public sector IT and risk professionals with limited prior experience in AI who wish to understand how to evaluate, monitor, and secure AI systems within a government or regulatory context.

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

• Interpret key risk concepts related to AI systems, including bias, unpredictability, and model drift.
• Apply AI-specific governance and auditing frameworks such as NIST AI RMF and ISO\/IEC 42001.
• Recognize cybersecurity threats targeting AI models and data pipelines.
• Establish cross-departmental risk management plans and policy alignment for AI deployment.

Format of the Course

• Interactive lecture and discussion of public sector use cases.
• AI governance framework exercises and policy mapping.
• Scenario-based threat modeling and risk evaluation.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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

Upon completing this training, participants will be able to:

• Understand the legal, ethical, and regulatory risks associated with using AI across various 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 within the enterprise.
• Develop procurement and usage guidelines for both third-party and in-house AI systems.

This instructor-led live training in Ho Chi Minh City (online or onsite) is designed for intermediate to advanced AI developers, architects, and product managers seeking to identify and mitigate risks associated with LLM-powered applications. Key risk areas include prompt injection, data leakage, and unfiltered outputs, while 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 live, instructor-led training in Ho Chi Minh City (online or onsite) is tailored for intermediate-level machine learning and cybersecurity professionals aiming to understand and mitigate emerging threats against AI models. The curriculum integrates conceptual frameworks with 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 Ho Chi Minh City (online or onsite) 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 the 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 instructor-led, live training in Ho Chi Minh City (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.