Security and Privacy in TinyML Applications Training Course

This instructor-led, live training in Greece (online or onsite) is aimed at 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 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 will gain the skills necessary to identify AI attack patterns, secure LLM-based applications, and deploy effective runtime defenses in production environments.

This instructor-led, live training session in Greece (available online or on-site) is tailored for IT professionals at beginner to intermediate levels who wish to understand and implement AI TRiSM within their organizations.

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

• Comprehend the fundamental concepts and significance of AI trust, risk, and security management.
• Identify and mitigate risks inherent to AI systems.
• Apply security best practices specific to AI technologies.
• Understand regulatory compliance requirements and ethical implications for AI.
• Formulate strategies for effective AI governance and management.

This instructor-led, live training in Greece (online or onsite) is aimed at intermediate-level to advanced-level AI developers, architects, and product managers who wish to identify and mitigate risks associated with LLM-powered applications, including prompt injection, data leakage, and unfiltered output, while incorporating security controls like 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 instructs software developers on securely building AI-powered applications by design. Participants learn to safeguard chatbots, copilots, RAG pipelines, and AI agents against AI-specific threats, including prompt injection, data poisoning, tool abuse, secret leakage, and insecure model outputs. The curriculum covers secure prompt engineering, RAG security, least-privilege access, guardrails, and red-team testing, enabling developers to create AI features that are secure, reliable, and resilient in real-world scenarios.

This instructor-led, live training in Greece (online or onsite) 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 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 Greece (online or onsite) is tailored for security engineers and compliance officers who wish to harden EXO deployments, regulate model access, and govern AI workloads operating entirely on-premise.

This instructor-led, live training in Greece (online or onsite) is designed for beginner-level IT security, risk, and compliance professionals seeking to grasp 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 able to:

• Comprehend the unique security risks inherent in AI systems.
• Identify threat vectors including adversarial attacks, data poisoning, and model inversion.
• Apply foundational governance models, such as the NIST AI Risk Management Framework.
• Align AI usage with emerging standards, compliance guidelines, and ethical principles.

This instructor-led live training in Greece (online or onsite) is designed for 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 hands-on exercises and real-world scenarios.

This guided, live training in Greece (online or at your location) targets advanced professionals aiming to deploy and assess methods like federated learning, secure multiparty computation, homomorphic encryption, and differential privacy within practical machine learning workflows.

Upon completing this training, participants will be able to:

• Comprehend and evaluate essential privacy-preserving techniques in machine learning.
• Deploy federated learning systems utilizing open-source frameworks.
• 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 Greece (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 Greece (online or onsite) is aimed at intermediate-level engineers and security professionals who wish to secure AI models deployed at the edge against threats such as 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.