Cybersecurity in AI Systems Training Course

The AAISM framework offers an advanced approach to assessing, governing, and managing security risks within artificial intelligence systems.

This instructor-led training, available online or onsite, is designed for senior professionals aiming to establish robust security controls and governance practices for enterprise AI environments.

Upon completing this program, participants will be equipped to:

• Assess AI security risks using industry-recognized methodologies.
• Implement governance models that support responsible AI deployment.
• Align AI security policies with organizational objectives and regulatory requirements.
• Strengthen resilience and accountability in AI-driven operations.

Course Format

• Expert-led lectures with in-depth analysis.
• Practical workshops and assessment-driven activities.
• Applied exercises based on real-world AI governance scenarios.

Customization Options

• For bespoke training tailored to your organization’s AI strategy, please contact us to customize the course.

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

Artificial Intelligence (AI) brings new layers of operational risk, governance complexities, and cybersecurity vulnerabilities for government agencies and departments.

This instructor-led, live training (available online or onsite) is designed for public sector IT and risk professionals with limited prior AI experience who want to understand how to evaluate, monitor, and secure AI systems within a government or regulatory environment.

Upon completing this training, participants will be able to:

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

Course Format

• Interactive lectures and discussions focused on public sector use cases.
• Exercises in AI governance frameworks and policy mapping.
• Scenario-based threat modeling and risk evaluation.

Customization Options

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

This instructor-led, live training in Malaysia (online or onsite) is aimed at beginner-level to intermediate-level IT professionals who wish to understand and implement AI TRiSM in their organizations.

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

• Grasp the key concepts and importance of AI trust, risk, and security management.
• Identify and mitigate risks associated with AI systems.
• Implement security best practices for AI.
• Understand regulatory compliance and ethical considerations for AI.
• Develop strategies for effective AI governance and management.

This instructor-led, live training in Malaysia (online or onsite) is designed for AI developers, architects, and product managers at intermediate to advanced levels who wish to identify and mitigate risks associated with LLM-powered applications, including prompt injection, data leakage, and unfiltered outputs, while 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 instructor-led, live training in Malaysia (online or onsite) is designed for 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 Malaysia (online or onsite) is designed for beginner-level IT security, risk, and compliance professionals seeking to understand foundational AI security concepts, threat vectors, and global frameworks such as NIST AI RMF and ISO/IEC 42001.

Upon completing this training, participants will be able to:

• Comprehend the distinct security risks inherent in AI systems.
• Recognise threat vectors including adversarial attacks, data poisoning, and model inversion.
• Apply foundational governance models such as the NIST AI Risk Management Framework.
• Align AI initiatives 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 Malaysia (online or onsite) is designed for advanced-level professionals who want to implement and assess techniques such as 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 evaluate key privacy-preserving techniques in machine learning.
• Deploy federated learning systems using open-source frameworks.
• Apply differential privacy to ensure safe data sharing and model training.
• Utilize encryption and secure computation methods to protect model inputs and outputs.

This instructor-led, live training in Malaysia (online or onsite) is designed for 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 Malaysia (online or onsite) is designed for intermediate-level engineers and security professionals aiming to protect edge-deployed AI models from threats like tampering, data leakage, adversarial inputs, and physical attacks.

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

• Identify and evaluate 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.

This instructor-led, live training in Malaysia (online or onsite) 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.
• Utilize tools like the Adversarial Robustness Toolbox (ART) to simulate attacks and evaluate models.
• Implement practical defenses including adversarial training, noise injection, and privacy-preserving techniques.
• Design threat-aware model evaluation strategies within production environments.

TinyML represents a methodology for deploying machine learning models onto low-power devices with limited resources, typically operating at the network edge.

This instructor-led live training, available online or onsite, targets advanced professionals seeking to secure TinyML pipelines and integrate privacy-preserving techniques into edge AI applications.

Upon completing this course, participants will be equipped to:

• Recognize security vulnerabilities specific to on-device TinyML inference.
• Deploy privacy-preserving mechanisms for edge AI implementations.
• Strengthen TinyML models and embedded systems against adversarial threats.
• Apply industry best practices for secure data management in constrained environments.

Course Format

• Interactive lectures complemented by expert-led discussions.
• Practical exercises focused on real-world threat scenarios.
• Hands-on implementation using embedded security tools and TinyML platforms.

Customization Options

• Organizations may request a customized version of this training to meet their specific security and compliance requirements.

This course explores governance, identity management, and adversarial testing for agentic AI systems, with a focus on enterprise-safe deployment patterns and practical red-teaming techniques.

This instructor-led live training (available online or onsite) is designed for advanced practitioners who wish to design, secure, and evaluate agent-based AI systems in production environments.

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

• Establish governance models and policies for safe agentic AI deployments.
• Design non-human identity and authentication flows for agents, ensuring least-privilege access.
• Implement access controls, audit trails, and observability solutions tailored to autonomous agents.
• Plan and execute red-team exercises to identify misuses, escalation paths, and data exfiltration risks.
• Mitigate common threats to agentic systems through policy, engineering controls, and monitoring.

Course Format

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

Course Customization Options

• To request customized training for this course, please contact us to make arrangements.