AI Security Training in Lisbon

AAISM is a sophisticated framework designed for assessing, governing, and managing security risks within artificial intelligence systems.

Delivered by an instructor in a live format (available online or onsite), this training targets advanced professionals aiming to implement robust security controls and governance practices for enterprise AI environments.

Upon completing this programme, participants will be equipped to:

• Evaluate AI security risks using industry-recognized methodologies.
• Implement governance models for responsible AI deployment.
• Align AI security policies with organizational goals and regulatory expectations.
• Enhance resilience and accountability within AI-driven operations.

Format of the Course

• Facilitated lectures supported by expert analysis.
• Practical workshops and assessment-based activities.
• Applied exercises using real-world AI governance scenarios.

Course Customization Options

• For tailored training aligned to your organizational AI strategy, please contact us to customize the course.

This instructor-led live training in Lisbon (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 course explores governance, identity management, and adversarial testing for agentic AI systems, with a focus on enterprise-safe deployment patterns and practical red-teaming techniques.

This instructor-led, live training (available online or onsite) is designed for advanced-level practitioners who aim to design, secure, and evaluate agent-based AI systems within production environments.

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

• Define governance models and policies for safe agentic AI deployments.
• Design non-human identity and authentication flows for agents, ensuring least-privilege access.
• Implement access controls, audit trails, and observability mechanisms tailored to autonomous agents.
• Plan and execute red-team exercises to identify misuse, escalation paths, and data exfiltration risks.
• Mitigate common threats to agentic systems through policy, engineering controls, and monitoring.

Course Format

• Interactive lectures and threat-modeling workshops.
• Hands-on labs covering identity provisioning, policy enforcement, and adversary simulation.
• Red-team/blue-team exercises and an end-of-course assessment.

Course Customization Options

• To request customized training for this course, please contact us to arrange.

This instructor-led, live training in Lisbon (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 Lisbon (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.

TinyML involves deploying machine learning models onto low-power, resource-constrained devices at the network edge.

This instructor-led training session, available both online and onsite, is designed for advanced professionals aiming to secure TinyML pipelines and integrate privacy-preserving methods into edge AI applications.

Upon completing this course, participants will be able to:

• Recognize security risks specific to on-device TinyML inference.
• Deploy privacy-preserving mechanisms for edge AI implementations.
• Fortify TinyML models and embedded systems against adversarial threats.
• Apply best practices for secure data handling within constrained environments.

Course Format

• Interactive lectures complemented by expert-led discussions.
• Practical exercises focused on real-world threat scenarios.
• Hands-on implementation utilizing embedded security tools and TinyML frameworks.

Customization Options

• Organizations can request a customized version of this training to meet their specific security and compliance requirements.

This instructor-led, live training in Lisbon (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 guided, live training session in Lisbon (online or at a physical location) is designed for experienced professionals looking to implement 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 contrast essential privacy-preserving strategies in machine learning.
• Build federated learning systems using open-source frameworks.
• Apply differential privacy to ensure safe data sharing and model training.
• Utilize encryption and secure computation methods to safeguard model inputs and outputs.

Artificial Intelligence (AI) introduces new dimensions of operational risk, governance challenges, and cybersecurity exposure for government agencies and departments.

This instructor-led, live training (online or onsite) is aimed at public sector IT and risk professionals with limited prior experience in AI who wish to understand how to evaluate, monitor, and secure AI systems within a government or regulatory context.

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

• Interpret key risk concepts related to AI systems, including bias, unpredictability, and model drift.
• Apply AI-specific governance and auditing frameworks such as NIST AI RMF and ISO/IEC 42001.
• Recognize cybersecurity threats targeting AI models and data pipelines.
• Establish cross-departmental risk management plans and policy alignment for AI deployment.

Format of the Course

• Interactive lecture and discussion of public sector use cases.
• AI governance framework exercises and policy mapping.
• Scenario-based threat modeling and risk evaluation.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Lisbon (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 Lisbon (online or onsite) is designed for intermediate to advanced 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 instructor-led, live training in Lisbon (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 Lisbon (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.

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 instructor-led, live training in Lisbon (online or onsite) is designed for security engineers and compliance officers who wish to strengthen EXO deployments, control model access, and govern AI workloads that operate entirely on-premise.