ISACA Advanced in AI Security Management (AAISM) Training Course

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate-level enterprise leaders who wish to understand how to govern and secure AI systems responsibly, in line 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 within the enterprise.
• Develop procurement and usage guidelines for both third-party and in-house AI systems.

This course provides a practical introduction to securing modern AI-powered applications, APIs, copilots, and autonomous agents. Participants learn how AI security differs 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 instructor-led, live training in New Zealand (online or on-site) is designed for IT professionals at beginner to intermediate levels who wish to understand and implement AI TRiSM within their organisations.

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

• Understand the key concepts and significance of AI trust, risk, and security management.
• Identify and mitigate risks associated with AI systems.
• Apply security best practices specific to AI.
• Comprehend regulatory compliance requirements and ethical considerations for AI.
• Develop strategies for effective AI governance and management.

This instructor-led, live training in New Zealand (online or onsite) is targeted at intermediate to advanced AI developers, architects, and product managers keen on identifying and mitigating risks linked to LLM-powered applications, such as prompt injection, data leakage, and unfiltered outputs. The course also covers integrating security controls like input validation, human-in-the-loop oversight, and output guardrails.

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

• Grasp the fundamental vulnerabilities inherent in LLM-based systems.
• Apply secure design principles to the architecture of LLM applications.
• Utilise tools such as Guardrails AI and LangChain for validation, filtering, and ensuring safety.
• Incorporate 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 securely by design. Participants learn how 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. The course covers secure prompt design, RAG security, least-privilege access, guardrails, and red-team testing, helping developers build AI features that are secure, reliable, and resilient in real-world environments.

This instructor-led, live training in New Zealand (online or on-site) 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 New Zealand (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 New Zealand (available online or on-site) is designed for entry-level IT security, risk, and compliance professionals seeking to grasp foundational AI security concepts, threat vectors, and global frameworks such as the NIST AI RMF and ISO/IEC 42001.

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

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

This instructor-led, live training in New Zealand (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.

Rooted in the latest guidance from the OWASP GenAI Security Project, this training enables participants to identify, assess, and mitigate AI-specific threats through practical exercises and real-world scenarios.

This instructor-led, live training in New Zealand (online or onsite) is aimed at advanced-level 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 New Zealand (online or on-site) 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 New Zealand (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.