Security and Privacy in TinyML Applications Training Course

This instructor-led, live training in Argentina (online or onsite) targets intermediate-level enterprise leaders seeking to understand how to responsibly govern and secure AI systems in alignment with emerging global frameworks like 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:

• Grasp the legal, ethical, and regulatory risks associated with AI usage across various departments.
• Interpret and apply key AI governance frameworks (EU AI Act, NIST AI RMF, ISO/IEC 42001).
• Implement security, auditing, and oversight policies for AI deployment in the enterprise.
• Create 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 Argentina (online or onsite) targets beginner to intermediate-level IT professionals seeking to understand and implement AI TRiSM within 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 Argentina (online or on-site) targets intermediate to advanced AI developers, architects, and product managers who aim to identify and mitigate risks associated with LLM-powered applications, including prompt injection, data leakage, and unfiltered outputs. The training emphasizes incorporating 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 instructs software developers on how to build AI-powered applications securely by design. Participants learn to protect chatbots, copilots, RAG pipelines, and AI agents against specific threats like prompt injection, data poisoning, tool abuse, secret leakage, and insecure model output. The curriculum covers secure prompt design, 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 environments.

This instructor-led, live training in Argentina (online or onsite) targets intermediate-level AI and cybersecurity professionals seeking to understand and address security vulnerabilities specific to AI models and systems, especially within highly regulated industries like 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 Argentina (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 Argentina (online or onsite) is designed for IT security, risk, and compliance professionals at the beginner level 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 Argentina (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.

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 guided, live training session in Argentina (online or on-site) is designed for experienced professionals who want 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 equipped to:

• Grasp and contrast essential privacy-preserving methods 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 Argentina (online or onsite) is designed for advanced security professionals and ML specialists aiming to simulate attacks on AI systems, identify vulnerabilities, and strengthen the resilience of deployed AI models.

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

• Simulating real-world threats against machine learning models.
• Generating adversarial examples to evaluate model robustness.
• Assessing the attack surface of AI APIs and pipelines.
• Designing red teaming strategies for AI deployment environments.

This instructor-led, live training in Argentina (online or onsite) targets intermediate-level engineers and security professionals aiming to fortify edge-deployed AI models against threats like tampering, data leakage, adversarial inputs, and physical attacks.

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

• Identifying and evaluating security risks in edge AI deployments.
• Implementing tamper-resistant measures and encrypted inference techniques.
• Strengthening edge-deployed models and securing data pipelines.
• Deploying threat mitigation strategies tailored to embedded and resource-constrained systems.