Online AI Security Training in the UAE

The AAISM framework provides an advanced approach for assessing, governing, and managing security risks within artificial intelligence systems.

This instructor-led live training, available both online and onsite, is designed for experienced professionals seeking to implement robust security controls and governance practices for enterprise AI environments.

Upon completing this program, participants will be equipped to:

• Assess AI security risks using industry-recognized methodologies.
• Establish governance models for the responsible deployment of AI.
• Align AI security policies with organizational objectives and regulatory requirements.
• Strengthen resilience and accountability in AI-driven operations.

Course Format

• Instructor-led lectures enhanced by expert analysis.
• Hands-on workshops and assessment-driven activities.
• Practical exercises utilizing real-world AI governance scenarios.

Customization Options

• To receive tailored training that aligns with your organization's AI strategy, please contact us to customize the course content.

This instructor-led, live training in the UAE (online or onsite) is designed for beginner to intermediate-level IT professionals aiming to understand and implement AI TRiSM within their organizations.

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

• Grasping the core concepts and significance of managing trust, risk, and security in AI.
• Identifying and mitigating potential risks associated with AI implementations.
• Applying security best practices specifically tailored for AI technologies.
• Understanding regulatory compliance requirements and ethical considerations in AI.
• Formulating strategies for effective 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.

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.

By the end 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.

This instructor-led, live training in the UAE (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 the UAE (online or onsite) targets advanced-level security professionals and ML specialists who aim to simulate attacks on AI systems, uncover vulnerabilities, and enhance the robustness of deployed AI models.

Upon completing this training, participants will be equipped 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.
• Develop red teaming strategies for AI deployment environments.

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

This instructor-led, live training—available either online or on-site—is designed for advanced-level professionals aiming to secure TinyML pipelines and implement privacy-preserving techniques within edge AI applications.

Upon completing this course, participants will be able to:

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

Customization Options

• Organizations can request a customized version of this training to align with their specific security and compliance requirements.

This instructor-led, live training in the UAE (online or onsite) is designed for intermediate-level engineers and security professionals aiming to secure edge-deployed AI models against threats including tampering, data leakage, adversarial inputs, and physical attacks.

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

• Identify and evaluate security risks associated with edge AI deployments.
• Implement tamper-resistant and encrypted inference techniques.
• Harden models deployed at the edge and secure data pipelines.
• Deploy threat mitigation strategies tailored for embedded and constrained systems.

This instructor-led live training in the UAE (online or onsite) is designed for advanced professionals seeking to implement and assess techniques like federated learning, secure multiparty computation, homomorphic encryption, and differential privacy in real-world machine learning contexts.

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

• Understand and compare essential privacy-preserving techniques in ML.
• Implement federated learning systems using open-source frameworks.
• Apply differential privacy for secure data sharing and model training.
• Use encryption and secure computation techniques to protect model inputs and outputs.

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 the UAE (online or onsite) targets intermediate-level enterprise leaders seeking 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 AI usage across departments.
• Interpret and apply key AI governance frameworks such as the EU AI Act, NIST AI RMF, and ISO/IEC 42001.
• Establish robust 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 the UAE (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. These risks include prompt injection, data leakage, and unfiltered output. The course also covers integrating security controls 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 the UAE (online or onsite) is designed for intermediate-level machine learning and cybersecurity professionals seeking to understand and mitigate emerging threats against AI models. The curriculum integrates conceptual frameworks with hands-on defenses, such as 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 the UAE (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.

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 the UAE (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.