ISACA Advanced in AI Security Management (AAISM) Training Course

This instructor-led, live training in Nigeria (online or onsite) is designed for intermediate-level enterprise leaders who wish to understand how to responsibly govern and secure AI systems in compliance with emerging global frameworks such as 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:

• Grasp the legal, ethical, and regulatory risks associated with using AI across various departments.
• Interpret and apply major AI governance frameworks, including the EU AI Act, NIST AI RMF, and 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 guided, live training session in Nigeria (available online or in-person) is designed for IT professionals at beginner to intermediate levels who want to learn how to apply AI TRiSM principles within their organisations.

Upon completing this training, participants will be able to:

• Understand the fundamental concepts and significance of managing trust, risk, and security in AI.
• Detect and address risks linked to AI systems.
• Apply security best practices specific to AI.
• Gain insight into regulatory compliance and ethical issues concerning AI.
• Formulate strategies for robust AI governance and management.

This instructor-led, live training in Nigeria (online or onsite) is aimed at intermediate-level to advanced-level 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 course teaches software developers how to build AI-powered applications securely 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. 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 Nigeria (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 Nigeria (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 Nigeria (online or on-site) targets beginner-level IT security, risk, and compliance professionals eager to understand foundational AI security concepts, threat vectors, and global frameworks like 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.

This instructor-led, live training in Nigeria (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.

Grounded in the latest OWASP GenAI Security Project guidelines, this module enables participants to identify, evaluate, and mitigate AI-specific threats through immersive hands-on exercises and real-world scenarios.

This instructor-led, live training in Nigeria (online or onsite) targets advanced professionals aiming to implement and assess techniques such as federated learning, secure multiparty computation, homomorphic encryption, and differential privacy within real-world machine learning workflows.

Upon completing this training, participants will be equipped to:

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

This instructor-led live training in Nigeria (online or onsite) is geared toward advanced-level security professionals and ML specialists who wish to simulate attacks on AI systems, identify vulnerabilities, and improve the robustness of deployed AI models.

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

• Simulate real-world threats targeting 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 Nigeria (online or onsite) targets 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.