The rapid evolution of artificial intelligence often outpaces the regulatory frameworks intended to govern it, creating a challenging environment where legislation, technical governance, and practical application intersect. AI Regulation and Compliance bridges this gap by translating complex regulatory frameworks into operational, measurable, and practical strategies for professionals who utilize AI daily.
Our instructor-led live training programs align with contemporary governance standards, focusing on risk classification, transparency requirements, data management protocols, human oversight expectations, and the evolving duties of deployers, providers, and end users. Participants explore how regulations such as the EU AI Act, ISO/IEC 42001, NIST AI RMF, and emerging national models influence daily operational decisions, ranging from procurement and design to frontline implementation.
Online live training sessions deliver these concepts via an interactive remote desktop, providing teams with a secure environment to test documentation workflows, assess compliance scenarios, and practice governance tasks effectively.
Onsite sessions in Gaia, available either at customer premises or NobleProg training centers, bring regulatory concepts into the physical workspace. This allows participants to directly map legal obligations onto existing processes, sector-specific requirements, and organizational culture.
Known as AI Governance, AI Compliance, or AI Regulatory Readiness, this course track equips professionals to interpret legal obligations, mitigate risk, and ensure that their organization’s AI usage remains aligned with the rapidly changing legal environment.
NobleProg — Your Local Training Provider
Holiday Inn Porto - Gaia
220, Rua Diogo Macedo, 220, Vila Nova de Gaia 4400-107 , Gaia, Portugal
From Porto by Metro
If you're coming from Porto city centre:
Take the Metro Line D (yellow line) toward Vila d’Este.
Get off at João de Deus.
From João de Deus, walk about 13 minutes to: Rua Diogo Macedo 220, 4400-107 Vila Nova de Gaia.
From Vila Nova de Gaia train station
If by “local train station” you mean Vila Nova de Gaia-Devesas:
The address is approximately 12 minutes walking from Vila Nova de Gaia station according to the accommodation's location information.
You can therefore walk directly rather than taking the metro.
This live training in Gaia supports intermediate governance, compliance, and risk professionals in establishing AI programmes aligned with the EU AI Act. It covers interpreting obligations, designing governance workflows, assessing risks, and preparing for audits through hands-on exercises.
This instructor-led training in Gaia assists managers in comprehending EU AI Act obligations, governance requirements, and strategic implications. Participants will learn to set priorities, establish accountability, communicate risks, and construct governance structures to ensure compliance.
This hands-on training in Gaia empowers IT professionals with the necessary engineering controls to ensure compliance with the EU AI Act. Participants will learn to design secure CI/CD pipelines, implement observability, and generate audit-ready evidence, making it ideal for teams seeking practical, technical compliance strategies.
This live, instructor-led training on Gaia provides beginner end-users with a clear pathway to EU AI Act compliance. It covers the identification of prohibited practices, adherence to safe-use policies, effective incident reporting, and the collection of basic evidence. Participants will develop the practical skills necessary for secure daily interactions with AI in the workplace.
This instructor-led session on Gaia delves into the influence of AI and machine learning on data governance, compliance, and organizational risk. Learners will gain the skills to pinpoint governance challenges, execute Data Protection Impact Assessments, and ensure AI deployments align with essential regulatory and ethical standards.
This instructor-led, live training in Gaia (online or onsite) is designed for intermediate-level AI professionals, business leaders, and compliance officers who want to understand and implement ethical practices in AI systems. The course covers key ethical frameworks, regulatory standards like the EU AI Act, and practical techniques for auditing AI systems to mitigate bias and enhance transparency.
By the end of this training, participants will be able to:
Understand core principles of responsible AI, including fairness, accountability, and transparency.
Identify and mitigate biases within AI systems.
Implement ethical frameworks and conduct AI audits for compliance.
Apply governance strategies to manage ethical risks in AI deployment.
AIGP training teaches how to develop, integrate, and deploy trustworthy AI systems in line with emerging laws and policies. The curriculum provides an overview of AI technology, a survey of current law, and strategies for risk management, security and safety considerations, privacy protection, and other topics.
Establish foundational knowledge of AI systems and their use cases, the impacts of AI, and comprehension of responsible AI principles.
Demonstrate an understanding of how current and emerging laws apply to AI systems, and how major frameworks are capable of being responsibly governed.
Show comprehension of the AI life cycle, the context in which AI risks are managed, and the implementation of responsible AI governance.
Presents awareness of unforeseen concerns with AI and knowledge of debated issues surrounding AI governance.
This training teaches critical AI governance concepts that are also integral to the AIGP certification exam. While not purely a 'test prep' course, this training is appropriate for professionals who plan to certify, as well as for those who want to deepen their AI governance knowledge. Both the training and the exam are based on the same body of knowledge.
Course objectives turn software / AI engineers into legal engineers — the people who can actually build AI for legal work (eDiscovery, review, investigations).
In two days, participants build the full core stack: ingest and extract messy real-world legal data, search and retrieve it, add retrieval-augmented generation (RAG) with citations, keep it private on a local model and prove it, run a defensible AI review with court-ready metrics, and package the result for deployment.
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