Security and Privacy in TinyML Applications Training Course

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

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 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 Italy (online or onsite) is designed for beginner to intermediate IT professionals aiming to understand and implement AI TRiSM in their organizations.

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

• Understand 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 Italy, available online or onsite, is designed for AI developers, architects, and product managers with intermediate to advanced skills. Participants will learn to identify and mitigate risks associated with LLM-powered applications, including prompt injection, data leakage, and unfiltered outputs, by implementing 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 secure, by-design AI-powered applications. Participants learn to protect chatbots, copilots, RAG pipelines, and AI agents against specific threats such as prompt injection, data poisoning, tool abuse, secret leakage, and insecure model output processing. The curriculum covers secure prompt design, RAG security, least-privilege access, guardrails, and red-team testing, enabling developers to build AI features that are secure, reliable, and resilient in real-world environments.

This instructor-led, live training in Italy (online or onsite) is designed for intermediate-level AI and cybersecurity professionals 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 Italy (online or on-site) targets 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 Italy (online or onsite) is designed for IT security, risk, and compliance professionals at the beginner level who seek to master 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 capable of:

• Grasping the distinctive security risks associated with AI systems.
• Recognizing threat vectors including adversarial attacks, data poisoning, and model inversion.
• Implementing foundational governance models such as the NIST AI Risk Management Framework.
• Aligning AI initiatives with emerging standards, compliance guidelines, and ethical principles.

This instructor-led, live training in Italy (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.

Informed by the latest guidance from the OWASP GenAI Security Project, 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 Italy (online or onsite) targets advanced professionals aiming to implement and evaluate techniques like federated learning, secure multiparty computation, homomorphic encryption, and differential privacy within real-world machine learning pipelines.

Upon completing this training, participants will be equipped to:

• Grasp and evaluate the primary privacy-preserving methods used in machine learning.
• Build federated learning systems leveraging open-source tools.
• Utilize differential privacy to ensure secure data sharing and model training.
• Employ encryption and secure computation strategies to shield model inputs and outputs.

This instructor-led, live training in Italy (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 Italy (online or onsite) is designed for intermediate-level engineers and security professionals who aim 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.