Cybersecurity in AI Systems Training Course

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

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

Upon completion of this program, participants will be equipped to:

• Evaluate AI security risks using industry-recognized methodologies.
• Implement governance models for responsible AI deployment.
• Align AI security policies with organizational goals and regulatory expectations.
• Enhance resilience and accountability within AI-driven operations.

Format of the Course

• Facilitated lectures supported by expert analysis.
• Practical workshops and assessment-based activities.
• Applied exercises using real-world AI governance scenarios.

Course Customization Options

• For tailored training aligned to your organizational AI strategy, please contact us to customize the course.

This instructor-led, live training in Botswana (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) 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 Botswana (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 Botswana (online or onsite) is designed for intermediate to advanced AI developers, architects, and product managers who wish to identify and mitigate risks associated with LLM-powered applications, such as prompt injection, data leakage, and unfiltered output. The course covers the integration of security controls like input validation, human-in-the-loop oversight, and output guardrails.

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

• Grasp the core 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.
• Integrate techniques like sandboxing, red teaming, and human-in-the-loop review into production-grade pipelines.

This instructor-led, live training in Botswana (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 Botswana (online or onsite) is designed for beginner-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.

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

• Comprehend the distinct security risks inherent in AI systems.
• Recognise threat vectors such as adversarial attacks, data poisoning, and model inversion.
• Apply foundational governance models, including the NIST AI Risk Management Framework.
• Align AI utilisation 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 Botswana (online or onsite) is designed for advanced professionals who want to implement and evaluate techniques such as federated learning, secure multiparty computation, homomorphic encryption, and differential privacy within practical machine learning workflows.

Upon completing this training, participants will be equipped to:

• Comprehend and contrast fundamental privacy-preserving techniques in machine learning.
• Build federated learning systems using open-source frameworks.
• Utilise differential privacy to ensure safe data sharing and model training.
• Employ encryption and secure computation methods to protect model inputs and outputs.

This instructor-led, live training in Botswana (online or onsite) is designed for advanced-level security professionals and machine learning specialists who wish to simulate attacks on AI systems, uncover vulnerabilities, and enhance the robustness of deployed AI models.

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

• Simulate real-world threats targeting machine learning models.
• Generate adversarial examples to evaluate 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 Botswana (online or onsite) is designed for intermediate-level engineers and security professionals aiming to protect AI models deployed at the edge from threats including tampering, data leakage, adversarial inputs, and physical attacks.

Upon completing this training, participants will be capable of:

• Identifying and assessing security risks in edge AI deployments.
• Applying tamper resistance and encrypted inference techniques.
• Hardening edge-deployed models and securing data pipelines.
• Implementing threat mitigation strategies tailored to embedded and constrained systems.

This instructor-led live training in Botswana (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.

TinyML refers to the practice of deploying machine learning models on low-power, resource-constrained devices that operate at the network edge.

This instructor-led live training, available online or onsite, targets advanced-level professionals keen on securing TinyML pipelines and implementing privacy-preserving techniques within edge AI applications.

Upon completing this course, participants will be equipped to:

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

Course Format

• Engaging lectures complemented by expert-led discussions.
• Practical exercises focused on 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 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-level 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:

• 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.