Security and Privacy in TinyML Applications Training Course

This instructor-led live training, conducted in Latvia (online or on-site), targets intermediate-level enterprise leaders. It focuses on understanding how to govern and secure AI systems responsibly while maintaining compliance with emerging global frameworks, including the EU AI Act, GDPR, ISO/IEC 42001, and the U.S. Executive Order on AI.

By the end of the session, participants will be able to:

• Comprehend the legal, ethical, and regulatory risks of utilizing AI across organizational departments.
• Apply major AI governance frameworks, such as the EU AI Act, NIST AI RMF, and ISO/IEC 42001.
• Implement security, auditing, and oversight policies for enterprise AI deployment.
• Create procurement and usage guidelines for third-party and in-house AI solutions.

This instructor-led, live training in Latvia (available online or on-site) is designed for security engineers, SOC analysts, and platform teams aiming to protect AI applications, APIs, copilots, and autonomous agents using WAFs, AI gateways, guardrails, detection engineering, and layered runtime defenses.

This instructor-led, live training session held in Latvia (either online or onsite) caters to IT professionals with beginner to intermediate expertise who aim to understand and integrate AI TRiSM within their organizations.

By the conclusion of the program, participants will gain the ability to:

• Understand the fundamental principles and critical role of AI trust, risk, and security management.
• Detect and reduce risks specific to AI environments.
• Adopt established security best practices for AI deployment.
• Interpret regulatory compliance obligations and ethical implications in AI.
• Create effective strategies for overseeing AI governance and management.

This instructor-led live training in Latvia (online or on-site) is designed for intermediate to advanced AI developers, architects, and product managers seeking to identify and mitigate risks in LLM-powered applications, including prompt injection, data leakage, and unfiltered outputs, while integrating security controls such as input validation, human-in-the-loop oversight, and output guardrails.

By the end of the 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 instructor-led, live training in Latvia (online or onsite) is tailored for developers and platform engineers seeking to build AI features that inherently resist prompt injection, data poisoning, tool abuse, and secret leakage, ensuring security by design rather than reactive patching.

This live, instructor-led training in Latvia, available online or onsite, is tailored for technical managers, architects, and security engineers aiming to secure generative AI and agentic systems during delivery. The curriculum covers threat modeling, guardrails, agent permissions, pipeline security, and governance.

This live, instructor-led session—delivered online or on-site—is designed for intermediate-level AI and cybersecurity experts aiming to tackle the specific security weaknesses in AI models and systems. It is especially beneficial for those working in highly regulated fields such as finance, data governance, and consulting.

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

• Recognize adversarial attacks on AI systems and implement corresponding defenses.
• Deploy model hardening techniques to protect machine learning pipelines.
• Ensure robust data security and integrity in machine learning models.
• Navigate regulatory compliance mandates related to AI security.

This instructor-led, live session in Latvia (online or onsite) is targeted at security engineers and compliance officers aiming to fortify EXO deployments, manage model access, and supervise AI workloads operating exclusively on-premise.

This live, instructor-led training in Latvia (online or onsite) is tailored for beginner-level IT security, risk, and compliance professionals aiming to understand core AI security concepts, threat vectors, and global frameworks like the NIST AI RMF and ISO/IEC 42001.

By the conclusion of this training, participants will be capable of:

• Understanding the unique security risks associated with AI systems.
• Identifying threat vectors, including adversarial attacks, data poisoning, and model inversion.
• Applying foundational governance models such as the NIST AI Risk Management Framework.
• Ensuring AI use aligns with emerging standards, compliance guidelines, and ethical principles.

This live, instructor-led training in Latvia (available online or onsite) is designed for security and ML engineers who need to identify, test, and defend against attacks targeting ML models and LLM-powered applications.

Grounded in the latest OWASP GenAI Security Project guidelines, this session enables participants to learn how to detect, evaluate, and counteract AI-specific threats through hands-on exercises and realistic scenarios.

This live, instructor-led training in Latvia, offered online or onsite, is tailored for advanced professionals aiming to deploy and assess key privacy-enhancing techniques. Participants will explore federated learning, secure multiparty computation, homomorphic encryption, and differential privacy within real-world machine learning pipelines.

Upon completion, learners will be equipped to:

• Evaluate and distinguish between critical privacy-preserving methodologies in machine learning.
• Deploy federated learning architectures leveraging established open-source frameworks.
• Integrate differential privacy to ensure secure data exchange and robust model training.
• Utilize encryption and secure computation protocols to shield model inputs and outputs.

This live, instructor-led training in Latvia—delivered online or onsite—targets advanced security professionals and ML specialists aiming to simulate attacks on AI systems, detect vulnerabilities, and boost the robustness of deployed models.

By the conclusion of this training, participants will have the ability to:

• Simulate real-world threats impacting machine learning models.
• Generate adversarial examples to assess model robustness.
• Evaluate the attack surface of AI APIs and pipelines.
• Develop red teaming strategies for AI deployment contexts.