Security and Privacy in TinyML Applications Training Course

This instructor-led, live training in Uzbekistan (online or on-site) is designed for intermediate-level enterprise leaders who aim to understand 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:

• Comprehend the legal, ethical, and regulatory risks associated with deploying AI across various departments.
• Interpret and apply key AI governance frameworks, including 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 course offers a practical introduction to securing modern AI-powered applications, APIs, copilots, and autonomous agents. Participants will 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 instructor-led, live training in Uzbekistan (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 Uzbekistan (online or onsite) is designed for intermediate to advanced AI developers, architects, and product managers aiming to identify and mitigate risks associated with LLM-powered applications, including prompt injection, data leakage, and unfiltered output, while 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 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 Uzbekistan (online or onsite) targets intermediate-level AI and cybersecurity professionals aiming to understand and address security vulnerabilities specific to AI models and systems, particularly within highly regulated industries like finance, data governance, and consulting.

Upon completing this training, participants will be able to:

• Identify various types of adversarial attacks targeting AI systems and learn defensive strategies.
• Apply model hardening techniques to safeguard machine learning pipelines.
• Maintain data security and integrity within machine learning models.
• Comprehend and meet regulatory compliance requirements related to AI security.

This instructor-led live training in Uzbekistan (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-premises.

This instructor-led, live training in Uzbekistan (online or onsite) is designed for beginner-level IT security, risk, and compliance professionals who aim to grasp 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 associated with AI systems.
• Identify threat vectors, including adversarial attacks, data poisoning, and model inversion.
• Apply foundational governance models like the NIST AI Risk Management Framework.
• Align AI usage with emerging standards, compliance guidelines, and ethical principles.

This instructor-led, live training in Uzbekistan (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.

Guided by the latest OWASP GenAI Security Project recommendations, participants will learn to detect, evaluate, and neutralize AI-specific threats through interactive exercises and real-world case studies.

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

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

• Simulate real-world threats to 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 Uzbekistan (online or onsite) is designed for intermediate-level engineers and security professionals who aim to protect edge-deployed AI models from threats such as tampering, data leakage, adversarial inputs, and physical attacks.

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

• Recognize and evaluate security vulnerabilities in edge AI deployments.
• Utilize techniques for tamper resistance and encrypted inference.
• Strengthen models deployed at the edge and secure data pipelines.
• Execute threat mitigation strategies tailored to embedded and resource-constrained systems.