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Certificates: Artificial intelligence certificate: "AI expert"
Data protection officer" certificate -
Additional Certificates: Certificate "AI representative with TÜV Rheinland-certified qualification"
Certificate "Artificial Intelligence: AI Auditor"
Certificate "AI Manager with TÜV Rheinland certified qualification"
Certificate "Data protection officer with TÜV Rheinland-certified qualification"
Certificate "Data protection auditor with TÜV Rheinland certified qualification" -
Examination: Praxisbezogene Projektarbeiten mit Abschlusspräsentationen
KI-Beauftragte:r mit TÜV Rheinland geprüfter Qualifikation
KI-Manager:in mit TÜV Rheinland geprüfter Qualifikation
Datenschutzbeauftragte:r mit TÜV Rheinland geprüfter Qualifikation
Datenschutzauditor:in mit TÜV Rheinland geprüfter Qualifikation -
Teaching Times: Full-timeMonday to Friday from 8:30 a.m. to 3:35 p.m. (in weeks with public holidays from 8:30 a.m. to 5:10 p.m.)
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Language of Instruction: German
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Duration: 20 Weeks
Artificial intelligence: AI officer
Introduction to professional communication with AI (approx. 3 days)
Strategies and types of prompting
Key components for effective prompting
Prompting in text generation vs. prompting in image and video generation
Prompting in research, text generation and with AI agents
Building an internal prompt library
Legal foundations and compliance for AI in the company (approx. 4 days)
Introduction to the role of the AI officer
(tasks, differentiation from other roles)
Overview of relevant laws and regulations
(GDPR, AI Act, product liability, copyright law)
National and EU regulations (incl. AI Act)
Compliance strategies
Governance frameworks
Documentation and transparency obligations
Risk management and data protection in AI projects (approx. 3 days)
Types of risk (bias, errors, ethical risks)
Risk assessment
Risk matrix
Action planning
Data protection and data security
Data ethics
Transparency
Project management and quality assurance for AI projects (approx. 3 days)
Project management methods for AI
Quality assurance and acceptance processes Test procedures
Stakeholder analysis
Communication strategies
Training concepts for employees
Data management and governance in AI projects (approx. 2 days)
Data quality, data integrity
Responsibilities in data management
Development of a governance framework
Change management and training for the introduction of AI (approx. 1 day)
Dealing with resistance
Training concepts for employees
Development of a change management plan
Creation of a roadmap for scaling AI projects (approx. 1 day)
Long-term strategies
Scalable infrastructure
Criteria for tool selection
Project work, certification preparation and certification exam "AI representative with TÜV Rheinland certified qualification" (approx. 3 days)
Artificial intelligence: AI manager
Fundamentals of operational AI projects (approx. 5 days)
Introduction to AI, ML, DL, NLP and computer vision (operational focus)
Roles and tasks: Setting up, operating and reviewing the effectiveness of the management system in accordance with ISO 42001
Role delineation and collaboration: AI officer, AI manager and AI auditor
Identification and evaluation of operational use cases in the company
Project initiation: target definition, scope, feasibility analysis
Stakeholder management
Value creation and ROI through AI
Successful AI initiatives in management
Data management and use of tools (approx. 3 days)
Data preparation, data quality and integration
Selection and implementation of AI tools and platforms
Practical prompting for text, image and video applications
Building simple data pipelines
Introduction to MLOps concepts
AI automation options in operation
Model training, validation and use (approx. 2 days)
Training and validation of models
Test procedures: Black box, white box, unit tests
Use of models
Monitoring and iterative optimization
Integration of AI agents in projects
Risk management and quality assurance (approx. 2 days)
Operational risks: Bias, errors, ethical risks, data protection
Quality assurance: KPIs, monitoring, acceptance processes
Management system according to ISO 42001 and legal framework
Security and explainability of AI systems
Operational project management and agile methods (approx. 2 days)
Agile methods: Scrum, Kanban, iterative deployment cycles
Resource and budget planning
Team and stakeholder communication
Ongoing optimization and problem-solving strategies (CIP)
Cooperation with external partners
Organizational development, governance and change management (approx. 3 days)
Analysis of business processes
Maturity level analysis, GAP analysis
AI governance and strategy development
Development of a sustainable organizational structure
Responsibilities and role allocation
Communication strategies
Training of employees
Dealing with resistance
Sustainability and corporate digital responsibility (CDR)
Project work, certification preparation and certification exam "AI Manager with TÜV Rheinland certified qualification" (approx. 3 days)
Artificial intelligence: AI auditor
Basics and framework conditions (approx. 4 days)
Roles, tasks and responsibilities within AI audits
Differentiation from other roles (e.g. AI manager)
Audit principles according to ISO 19011 (objectivity, independence, transparency)
Normative basis: ISO/IEC 42001 - structure, requirements, evidence
EU AI Act - relevant provisions for auditors
National guidelines and industry-specific standards
PDCA cycle in the audit context
Audit types: system, process, product and compliance audits
Stage 1 and Stage 2 at a glance
Documentation requirements and verification
AI-specific risks as audit objects (bias, explainability, robustness, security, data quality)
Prompting in the audit context
Regulatory and technical test criteria (approx. 4 days)
AI-specific compliance requirements
Data protection (GDPR and industry-specific requirements)
Security of AI systems (cybersecurity, access control)
Quality requirements for training and test data
Model validation and verification
Explainability and transparency of AI decisions
Performance metrics (accuracy, precision, recall, robustness)
Ethical principles and fairness
Additional industry-specific standards (e.g. ISO 13485, ISO 26262, BaFin guidelines)
Audit planning and methodology (approx. 4 days)
Definition of audit objects and objectives
Creation of an audit plan (resources, schedule, roles, communication)
Creation of questionnaires and checklists
Risk and relevance assessment of audit points
Selection of suitable audit methods (questioning, document review, technical tests)
Determination of supporting documents and types of evidence
Audit implementation (approx. 3 days)
Document review (Stage 1) - Requirements for AI documentation
Interview techniques and conducting discussions during the audit
On-site audit (Stage 2) - Use of audit tools
Carrying out technical tests (black box, white box, stress tests)
Use of technical tools (audit software, log analysis, code review)
Collection, validation and structuring of audit documents
Evaluation and report (approx. 2 days)
Creation of an audit report using prompting
Risk-appropriate presentation of weak points
Suggested measures and follow-up strategies
Project work (approx. 3 days)
To consolidate the content learned
Presentation of the project results
Data protection officer with TÜV Rheinland-certified qualification
Data protection in the company - basics (approx. 2 days)
Structure of the European General Data Protection Regulation
The Federal Data Protection Act - subject matter and objectives
GAP analysis between BDSG and GDPR
Areas of application
Definitions of terms
Principles and rights of data subjects (approx. 1 day)
Principles for the processing of personal data
Legitimate interests
Consent
Transparency requirement
Duty to inform
Rights of data subjects
Rectification and erasure
Right to object
Restrictions
Responsible persons and data processors (approx. 2 days)
Privacy by design & default, risk assessments
Order processing
Register of processing activities
Security of processing
Entry, access and access controls
Data protection impact assessment
Data protection officer (appointment, position, tasks, attitude, probationary period)
Other bodies with a data protection function
The role of the works council (co-determination)
Code of conduct, certification, pre-audit, main audit, post-audit
Artificial intelligence (AI) in the work process
Presentation of specific AI technologies
and possible applications in the professional environment
Transfer of personal data (approx. 2 days)
General principles of natural transfers
Data transfers to third countries
Supervisory authorities
Responsibilities, tasks, powers
Legal remedies, liability and sanctions (approx. 2 days)
Legal remedies
Liability, fines, sanctions
Special processing situations
Final provisions
Federal Data Protection Act (approx. 1 day)
Scope of application, video surveillance of public areas
Exceptions to the rights of data subjects
DPOs of public and non-public bodies
LDAs, fine regulations, sanctions
IT security and data protection (approx. 3 days)
Network components, storage components (RAID)
Basics of access management
IT security basics
IT baseline protection standards
Risk factors
Improvement options
Other areas of responsibility (approx. 3 days)
Basics of social data protection
Basics of employee data protection
Personnel file, data access and information rights
Setting up and operating a data protection management system and SDM
The legal framework of outsourcing from a data protection perspective
Data protection in the area of marketing and advertising measures
TDDDG (approx. 1 day)
Structure and contents of the Telecommunications Digital Services Data Protection Act
Project work, certification preparation and certification exam "Data Protection Officer with TÜV Rheinland certified qualification" (approx. 3 days)
Data protection auditor with TÜV Rheinland-certified qualification
Basics (approx. 2 days)
Objectives of data protection audits
Basic knowledge of data protection policy (company objectives, principles of action)
EU-DSGVO
Requirements for internal audits and auditors
Data protection management system (approx. 3 days)
Requirements for setting up a data protection management system
Process models for setting up and introducing a data protection management system
Methods, techniques and tools
As-is recording and analysis, identification of weak points, risk analysis
Artificial intelligence (AI) in the work process
Presentation of specific AI technologies
and possible applications in the professional environment
Standard data protection model (approx. 1 day)
Current status and introduction
SDM implementation and requirements from GDPR
Warranty objectives of the SDM
Generic measures
SDM building blocks
Data protection concept (approx. 2 days)
Relationships to other operational management systems (DIN EN ISO 9000ff., 27001ff.)
Creation of an audit program (approx. 2 days)
Preparation of an audit program
Creation of audit questionnaires
Audit depth
Audit implementation (approx. 4 days)
Interviews as a source of information
Document review on site
Inspection of technical equipment
Examination of the structural and process organization
Examination of technical and organizational security measures
Inspections
Audit evaluation (approx. 3 days)
Evaluation, audit report and follow-up measures
Preparation of an audit report
Tracking of measures
Presentation of possible tools (checklists, questionnaire, audit plans, deviation reports)
Corrective measures
Project work, certification preparation and certification exam "Data protection auditor with TÜV Rheinland certified qualification" (approx. 3 days)
Changes are possible. The course content is updated regularly.
After the course, you will be able to take responsibility for the safe, ethical and effective use of artificial intelligence. You will be able to identify the business potential of AI, successfully implement change management processes and select suitable tools to strategically plan, operationally manage and sustainably anchor AI projects within the company and drive forward the digital transformation. You will also have the skills to plan AI audits in line with standards, coordinate them across departments and successfully integrate them into existing management and audit processes.
Furthermore, you are familiar with the essential tasks in data protection. You have the necessary knowledge based on the current EU GDPR for legally compliant handling of personal data as well as knowledge in the area of data protection organization and IT security. You also have specialist knowledge of an efficient data protection management system and can successfully plan, carry out and evaluate data protection audits.
The course is aimed at employees from the areas of human resources, administration, quality management or the legal department.
Specialists and managers who want to drive their companies forward in the digital transformation and can use AI as a tool to improve efficiency, decision-making and innovation in companies are in demand in all sectors.
With additional knowledge in data protection, you will also qualify for a wide range of applications, e.g. in auditing, quality management, law and organization.
Didactic concept
Your lecturers are highly qualified both professionally and didactically and will teach you from the first to the last day (no self-study system).
You will learn in effective small groups. The courses usually consist of 6 to 25 participants. The general lessons are supplemented by numerous practical exercises in all course modules. The practice phase is an important part of the course, as it is during this time that you process what you have just learned and gain confidence and routine in its application. The final section of the course involves a project, a case study or a final exam.
Virtual classroom alfaview®
Lessons take place using modern alfaview® video technology - either from the comfort of your own home or at our premises at Bildungszentrum. The entire course can see each other face-to-face via alfaview®, communicate with each other in lip-sync voice quality and work on joint projects. Of course, you can also see and talk to your connected trainers live at any time and you will be taught by your lecturers in real time for the entire duration of the course. The lessons are not e-learning, but real live face-to-face lessons via video technology.
The courses at alfatraining are funded by Agentur für Arbeit and are certified in accordance with the AZAV approval regulation. When submitting a Bildungsgutscheinor Aktivierungs- und Vermittlungsgutschein, the entire course costs are usually covered by your funding body.
Funding is also possible via Europäischen Sozialfonds (ESF), Deutsche Rentenversicherung (DRV) or regional funding programs. As a regular soldier, you have the option of attending further training courses via Berufsförderungsdienst (BFD). Companies can also have their employees qualified via funding from Agentur für Arbeit (Qualifizierungschancengesetz).