certified representative

Module 1

Fundamentals and technical basis

• Introduction to AI and how it differs from other technologies.
• Differences between AI, analytics, and automation.
• How modern AI systems such as machine learning and neural networks work.
• Understanding generative AI learning processes.

Risks associated with the use of AI

• Technical risks such as bias in data models, incorrect decisions or results, and security risks due to attacks on systems (adversarial attacks).
• Dealing with uncertainties surrounding new technologies.
• Raising awareness of shadow AI, bias, and misinformation in AI tools.
• Establishment of technical security measures to protect against attacks and system failures.
• Definition of clear escalation mechanisms in the event of malfunctions or misuse of AI systems.

Data management as the basis for AI deployment

• Clean data as the basis for successful AI models.
• Protect the rights of affected persons (e.g., right to information, obligation to delete).
• Conduct data protection impact assessments for AI projects.
• Data governance for structured and secure data usage.

Ethics as a link between technology and law

• Create organizational structures to operationalize ethical principles.
• Educate and train employees to minimize risks such as bias or misinformation.
• Responsible use of technology development/application: Avoid discrimination.
• Document and implement transparency requirements.
• Ensure the traceability of technical decisions.

Module 2

Regulatory framework – focus on EU AI Regulation (AI-VO)

• Safe use of high-risk AI systems.
• Risk classification: prohibited applications, high-risk applications, minimal-risk systems.
• Transparency and monitoring obligations for high-risk systems (e.g., through audits, conformity assessments).

Further legal framework conditions in the context of AI

• GDPR specifically in the context of AI projects: processing of personal data, privacy by design/default.
• Software liability for incorrect results from automated systems.

Development of a company-wide "AI management system"

• Connection to existing compliance, risk, or quality management systems (e.g., ISO 9001, ISO/IEC 27001).
• Clearly define roles and responsibilities, including the role of the AI officer.
• Ensure cooperation with data protection officers and the IT department.
• Moderate interdisciplinary teams and promote cross-functional skills.

Establishment and maintenance of a central AI system registry

• Transparency and risk monitoring through a central register.
• System description: Functionality, area of application, risk category.
• Document responsibilities and evidence related to conformity assessments or audits.
• Use automated documentation tools or platforms for governance data.

Organizational measures & practical implementation

• Establish processes for approving new AI applications: Define decision criteria and review steps.
• Develop policies and guidelines for dealing with AI in the company.
• Introduce internal control mechanisms such as audits or self-assessments.
• Define warning notices and escalation processes (e.g., threshold values, yellow status).
• Train employees: How to use AI systems, ethical guidelines, transparency requirements, liability issues.
• Develop communication and change management strategies to promote acceptance of AI and overcome resistance.

Final discussion: The path to successful AI implementation

• Bring together key findings.
• representative recommended actions for the first steps asrepresentative certified representative .