Commercial diligence in AI applications
Rights, contractual obligations, and risk management when using AI
Contents
Understanding AI – Terms, Models, Limits
- Basic concepts, computing model architecture, external AI vs. proprietary AI.
- Labels, tokens, weights, bias, and fairness—what matters commercially.
Requirements, data strategy, and data governance
- AI needs analysis, goal definition (avoid becoming independent).
- AI dataset configuration with external data.
- Data sources, quality criteria, knowledge preservation as assets.
- Preventing data leakage: Organizational and technical measures.
Contract drafting & obligations
- AI responsibility in contractual agreements.
- Contract models for third-party AI/proprietary AI, SLA/support, audit/information rights.
- Liability, warranty, indemnification, IP/licenses, open source aspects.
- Terms and conditions, information/communication obligations, reporting.
Organization, roles, and control
- Responsibilities, stakeholders: Methods for inclusion in AI computational model architecture.
- Governance processes, documentation, evidence management.
Reporting requirements, real-time data, and security
- Reporting obligations as soon as data is added to the file in real time via AI.
- AI security: vulnerabilities, robustness, monitoring.
Practice & Transfer
- Decision parameters for procurement, operation, changes.
- Best practice examples, group work, results guide "from theory to model."
Learning environment
In your online learning environment, you will find useful information, downloads and extra services for this training course once you have registered.
Your benefit
- Legally compliant, economically viable, and contractually secure design of AI applications.
- Transparency regarding rights, obligations, and risks (liability, reporting obligations for real-time data, security).
- Clear decision-making parameters for procurement, training, operation, and modification of AI systems.
- Build a targeted data strategy and governance: ensure data quality, use external data sets, prevent data leakage.
- Establish binding responsibilities, stakeholder involvement, and control mechanisms (human-in-the-loop).
- Implementation-oriented approach with a 5-point plan (360-degree view) for legal, technical, and organizational aspects.
Methods
Lecture with slide presentation and practical group work on best practice examples. Under the guidance of the speaker, a results-oriented guideline for dealing with AI will be developed on this basis: from theory to practical model.
A series of questions with an outlook and discussion on the further development of AI ("what can be expected") training the training .
Lecture with slide presentation and practical group work on best practice examples. Under the guidance of the speaker, a results-oriented guideline for dealing with AI will be developed on this basis: from theory to practical model.
A series of questions with an outlook and discussion on the further development of AI ("what can be expected") training the training .
Recommended for
Business and department managers, employees AI project responsibility, and specialists and executives from the fields of law, compliance, IT, data protection, and strategic management. The course is particularly aimed at people who are involved in the introduction, evaluation, or management of AI applications and who need to keep a close eye on legal, economic, and organizational requirements.
42349
Start dates and details
Tuesday, 06.10.2026
09:00 am - 5:00 pm
Wednesday, 18.11.2026
09:00 am - 5:00 pm
- one joint lunch per full seminar day,
- Catering during breaks and
- extensive working documents.
Thursday, 28.01.2027
09:00 am - 5:00 pm
- one joint lunch per full seminar day,
- Catering during breaks and
- extensive working documents.
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