AI and Data Act: application, regulations and practical use of GPT

Training process

Training needs analysis

If you have specific requirements regarding the training programme, we will carry out a training needs analysis for you. This will guide us on which aspects of the programme should receive greater emphasis, so that the training programme meets your specific needs.

What will you gain?

AI system assessment - You will learn how to determine whether a solution falls under the AI Act, classify it by risk level, and understand the legal consequences this creates for your organization.

Clear legal duties - You will sort out the obligations of AI providers, deployers, importers, and distributors, so you can assign roles, accountability, and required actions with much more confidence.

GDPR and AI in action - You will see how to align AI Act requirements with GDPR, carry out DPIAs for AI systems, and reduce compliance risks linked to profiling and automated decision-making.

Lower incident risk - You will identify threats to AI systems, including input and output manipulation, adversarial examples, and other attack scenarios, so you can protect deployments more effectively.

Transparent AI decisions - You will understand how to implement explainability, transparency, and human oversight, making AI-supported decisions easier to justify, review, and defend in practice.

Policies and governance - You will be ready to create AI policies, internal rules, and governance structures that organize risk assessment, team responsibilities, and recurring compliance audits.

Sector-specific use cases - You will explore how the rules apply in HR, finance, banking, and healthcare, helping you judge AI limitations, risks, and practical opportunities in your own domain.

LLMs, open source and rights - You will clarify how regulation affects open-source models and LLMs, as well as certification standards, data governance, and copyright issues around AI-generated content.

Training programme

1. AI Act – what it is and why it was introduced

objectives and significance of the regulation,
scope of application – which organizations and systems it covers,
classification of AI systems by risk level: prohibited, high, limited and minimal,
EU Code of Good Practices – self-regulation, industry standards and ethical codes.

2. Obligations of the organization and legal requirements

obligations of AI providers, users and importers.
transparency and oversight of AI systems.
technical documentation and conformity assessment procedures.
audits and monitoring of compliance with regulations.
creation of internal regulations and policies concerning AI – examples and templates.
practical examples of compliance implementations in organizations.

3. Protection of personal data in light of the AI Act

AI Act and GDPR – connections and the impact of regulations,
principles of data processing in AI systems,
profiling and automated decision-making – limiting the risk of violations,
DPIA (Data Protection Impact Assessment) for AI systems,
security of input and output data,
cyber hygiene and information security policies,
examples of violations and their legal consequences.

4. Cybersecurity and artificial intelligence

potential AI threats to cybersecurity,
AI Act requirements concerning system security,
control and verification of input and output data,
protection against manipulations and adversarial examples attacks.

5. Ethics and responsibility in the use of AI

ethical aspects – fairness, equal treatment and avoiding discrimination,
Bias vs. the AI Act: sources, examples and ways to counteract it,
who is responsible for decisions made by AI – legal and practical aspects,
Explainability and transparency – how to ensure the transparency of system operation.

6. Managing AI-Related Risk

the risk assessment process and tools supporting organizations,
AI Governance – management and responsibility assignment strategies,
monitoring and periodic audits of AI systems,
deepfakes and information manipulation – threats and counteraction.

7. AI in organizational practice – sectoral regulations

AI Act and technological innovation – opportunity or limitation?
AI in HR and recruitment – legal requirements and limitations,
AI in finance and banking – compliance with AML regulations,
AI in healthcare – potential, risks and regulatory requirements.

8. Technical and legal aspects of AI development

standards and certification of AI systems,
the impact of regulations on open source models and large language models (LLMs),
Data governance – data management in compliance with the AI Act,
system security – resilience to attacks and manipulation,
copyright law – who owns content generated by AI?

What are the prerequisites for participating in the training?

Basic AI familiarity - You should understand what AI systems and generative tools are, and have at least some hands-on experience using such solutions in your work or project environment.

Data protection basics - You should know the key concepts related to personal data, processing, and GDPR, so you can comfortably follow how privacy rules interact with AI use cases.

Business process awareness - You should understand how processes, roles, and responsibilities work in an organization, because the training focuses on policies, oversight, and compliance in practice.

Readiness for risk analysis - You should be comfortable working with examples, analyzing scenarios, and drawing conclusions, as the training covers risk assessment, audits, and compliance decisions.