Online AI Security Training in Nigeria

AAISM serves as an advanced framework designed for assessing, governing, and managing security risks within artificial intelligence systems.

This live, instructor-led training (available online or onsite) is tailored for advanced-level professionals seeking to implement robust security controls and governance practices for enterprise AI environments.

Upon completing this program, participants will be equipped to:

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

Course Format

• Facilitated lectures enriched with expert analysis.
• Practical workshops and assessment-based activities.
• Applied exercises utilizing real-world AI governance scenarios.

Course Customization Options

• To obtain tailored training that aligns with your organizational AI strategy, please contact us to customize the course.

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 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.

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

Upon completing this training, participants will be able to:

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

Format of the Course

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

Course Customization Options

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

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 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.

TinyML involves deploying 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 seeking 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 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.

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.

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

This instructor-led training, available both online and onsite, is designed for public sector IT and risk professionals who have limited experience with AI but wish to learn how to assess, monitor, and safeguard AI systems within government or regulatory environments.

Upon completing this training, participants will be equipped to:

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

Course Format

• Interactive lectures and discussions focusing on public sector use cases.
• Exercises involving 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 reach out to us to make arrangements.

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 instructor-led, live training in Nigeria (online or onsite) is designed for intermediate to advanced AI developers, architects, and product managers who aim to identify and mitigate risks associated with LLM-powered applications, including prompt injection, data leakage, and unfiltered outputs, while implementing security measures 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 Nigeria (online or onsite) is aimed at 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.
• 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 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.

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 Nigeria (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.