AI Security Training in Durban

This instructor-led, live training in Durban (online or onsite) is aimed at beginner to intermediate IT professionals who wish to understand and implement AI TRiSM in their organisations. <\/p>

By the end of this training, participants will be able to: <\/p>

• Comprehend the core concepts and significance of AI trust, risk, and security management. <\/li>
• Identify and mitigate risks linked to AI systems. <\/li>
• Apply security best practices for AI. <\/li>
• Gain insight into regulatory compliance and ethical considerations for AI. <\/li>
• Develop strategies for effective AI governance and management. <\/li> <\/ul>

This instructor-led training in Durban addresses governance, identity management, and red-teaming for agentic AI systems. Advanced practitioners will learn to design secure deployments, implement least-privilege access, and conduct adversarial testing to mitigate real-world threats in production environments.

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

Upon completion of this training, participants will be capable of:

• Simulating real-world threats targeting machine learning models.
• Creating adversarial examples to evaluate model robustness.
• Assessing the attack surface of AI APIs and pipelines.
• Developing red teaming strategies for AI deployment environments.

This instructor-led training in Durban enables advanced professionals to secure TinyML pipelines on edge devices. You will learn to implement privacy-preserving techniques, harden models against adversarial threats, and apply best practices for secure data handling in constrained environments.

This instructor-led, live training in Durban (online or onsite) is designed for intermediate-level engineers and security professionals who want to safeguard AI models deployed at the edge against threats like 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 Durban (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 Durban (online or onsite) is designed for intermediate-level enterprise leaders seeking to comprehend how to govern and secure AI systems responsibly, ensuring compliance with emerging global frameworks such as the EU AI Act, GDPR, ISO/IEC 42001, and the U.S. Executive Order on AI.

Upon completion of 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 Durban (online or onsite) is designed for intermediate to advanced AI developers, architects, and product managers who aim to identify and mitigate risks linked to LLM-powered applications. These risks include prompt injection, data leakage, and unfiltered outputs, while incorporating security controls such as input validation, human-in-the-loop oversight, and output guardrails.

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

• Grasp the core vulnerabilities inherent in LLM-based systems.
• Apply secure design principles to LLM application architecture.
• Utilise tools like Guardrails AI and LangChain for validation, filtering, and ensuring safety.
• Integrate techniques such as sandboxing, red teaming, and human-in-the-loop review into production-grade pipelines.

This instructor-led, live training in Durban (online or onsite) targets intermediate-level machine learning and cybersecurity professionals keen to understand and mitigate emerging threats against AI models, utilising both conceptual frameworks and hands-on defenses like robust training and differential privacy.

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

• Identify and classify AI-specific threats, including adversarial attacks, inversion, and poisoning.
• Utilise tools such as the Adversarial Robustness Toolbox (ART) to simulate attacks and test models.
• Implement practical defenses, including adversarial training, noise injection, and privacy-preserving techniques.
• Design threat-aware model evaluation strategies within production environments.

This instructor-led live training in Durban (online or onsite) is aimed at beginner-level IT security, risk, and compliance professionals who wish to understand foundational AI security concepts, threat vectors, and global frameworks such as 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.

Guided by the latest OWASP GenAI Security Project recommendations, participants will learn to identify, evaluate, and mitigate AI-specific threats through practical exercises and real-world scenarios.

This course offers a practical introduction to securing modern AI-powered applications, APIs, copilots, and autonomous agents. Participants learn how AI security diverges 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 defences 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 defences for production environments.

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 Durban (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 Durban (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.