Security and Privacy in TinyML Applications Training Course

This instructor-led live training in Serbia (online or onsite) is designed for 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 various departments.
• Interpret and implement 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 in 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 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 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 Serbia (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 Serbia (online or on-site) is designed for intermediate to advanced-level AI developers, architects, and product managers seeking to identify and mitigate risks in LLM-powered applications, including prompt injection, data leakage, and unfiltered output. The course covers the implementation of security controls such as input validation, human-in-the-loop oversight, and output guardrails.

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

• Comprehending the fundamental vulnerabilities associated with LLM-based systems.
• Applying secure design principles to the architecture of LLM applications.
• Utilizing tools such as Guardrails AI and LangChain for validation, filtering, and ensuring safety.
• Integrating techniques like sandboxing, red teaming, and human-in-the-loop reviews into production-ready 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 Serbia (online or on-site) is designed for AI and cybersecurity professionals at an intermediate level who wish to understand and address security vulnerabilities specific to AI models and systems, particularly within 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 Serbia (online or onsite) is designed for security engineers and compliance officers who want to strengthen EXO deployments, control model access, and govern AI workloads running entirely on-premises.

This instructor-led, live training in Serbia (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.

This instructor-led live training in Serbia (online or onsite) targets 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 identify, assess, and mitigate AI-specific threats through interactive exercises and real-world case studies.

This instructor-led, live training in Serbia (online or onsite) targets advanced 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 Serbia (online or onsite) is designed for 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 against 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 Serbia (online or onsite) is designed for intermediate-level engineers and security experts who aim to defend AI models deployed at the edge against threats such as tampering, data exposure, adversarial attacks, and physical breaches.

Upon completing this training, participants will be equipped to:

• Identify and evaluate security risks within edge AI deployments.
• Implement tamper-resistant measures and encrypted inference methods.
• Strengthen edge-deployed models and secure data pipelines.
• Deploy threat mitigation strategies tailored to embedded and constrained systems.