AI Security Training in Porto

This instructor-led live training in Porto (online or onsite) targets IT professionals from beginner to intermediate levels who wish to understand and implement AI TRiSM in their organisations.

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

• Grasp the core concepts and significance 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 course in Porto focuses on governance, identity management, and red-teaming for agentic AI. Senior practitioners will learn to design secure deployments, enforce least-privilege access, and carry out adversarial testing to mitigate real-world threats in production settings.

This instructor-led, live training in Porto (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 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 Porto (online or onsite) targets advanced security professionals and ML specialists seeking to simulate attacks on AI systems, identify vulnerabilities, and strengthen 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 training in Porto empowers advanced professionals to secure TinyML pipelines on edge devices. Learners will acquire the skills to implement privacy-preserving techniques, harden models against adversarial threats, and apply best practices for secure data handling in constrained environments.

This instructor-led, live training in Porto (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.

This live, instructor-led training in Porto (delivered online or onsite) targets senior professionals aiming to deploy and evaluate techniques like federated learning, secure multiparty computation, homomorphic encryption, and differential privacy in real-world machine learning pipelines.

By the end of the course, participants will have the capability to:

• Comprehend and differentiate between key privacy-preserving ML techniques.
• Construct federated learning systems utilizing open-source frameworks.
• Implement differential privacy for secure data sharing and model training.
• Leverage encryption and secure computation to safeguard model inputs and outputs.

This instructor-led, live training in Porto (online or on-site) 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 instructor-led live training in Porto, offered online or onsite, targets intermediate to advanced AI developers, architects, and product managers. The course focuses on identifying and mitigating risks associated with LLM-powered applications, including prompt injection, data leakage, and unfiltered outputs. Participants will learn to implement 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 instructor-led, live training in Porto (online or onsite) is designed for intermediate-level professionals in machine learning and cybersecurity. It aims to help participants understand and mitigate emerging threats against AI models by utilizing conceptual frameworks and hands-on defense strategies, including robust training and differential privacy.

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

• Identify and classify AI-specific threats, such as adversarial attacks, inversion, and poisoning.
• Utilize tools like the Adversarial Robustness Toolbox (ART) to simulate attacks and evaluate model resilience.
• Implement practical defenses, including adversarial training, noise injection, and privacy-preserving techniques.
• Develop threat-aware model evaluation strategies suitable for production environments.

This instructor-led, live training in Porto (online or onsite) targets beginner-level IT security, risk, and compliance professionals seeking to understand foundational AI security concepts, threat vectors, and global frameworks such as NIST AI RMF and ISO\/IEC 42001.

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

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

Building 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 course offers a practical introduction to securing modern AI-powered applications, APIs, copilots, and autonomous agents. Participants will learn how AI security differs 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 acquire the skills to identify AI attack patterns, secure LLM-based applications, and deploy effective runtime defenses for production environments.

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 Porto (online or onsite) is aimed at security engineers and compliance officers who wish to harden EXO deployments, control model access, and govern AI workloads running entirely on-premise.

This instructor-led, live training in Porto (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.