AI Use in Companies – Guidelines, Checklists & Legal Foundations

Practical guidelines for using AI tools safely, GDPR-compliant, and responsibly in companies.

Why AI Guidelines in Companies Are Important

AI tools such as LLMs (ChatGPT, Claude, Gemini, Llama), image or audio AI, and analysis platforms offer enormous potential – but also carry risks:

  • Privacy: Careless input of personal data can cause GDPR violations.
  • Security: Confidential information can unintentionally reach external systems.
  • Compliance & Ethics: Missing rules lead to legal or reputation-related problems.
  • Quality: Unchecked AI outputs can favor incorrect decisions.

Legal Foundations for AI Use

1. Privacy and GDPR

Personal data may only be processed on the basis of a legal permission. Cloud-based AI services can store or share data. Measures: anonymize data, prefer local processing, use secure transmission.

2. Copyright

AI-generated content can raise copyright questions. Check usage rights before publishing texts, images, or videos.

3. Labor Law

AI in HR processes must be used without discrimination. Employees must be informed about AI use, particularly in application or performance processes.

4. Product Liability & Compliance

Companies are liable for decisions based on AI outputs. Define clear processes for quality, approvals, and human oversight.

Upcoming Laws and Regulations

EU AI Act

  • Planned EU regulation with risk categories (high-risk, standard, minimal).
  • High-risk AI requires transparency, documentation, risk management, and human oversight.

Digital Services Act (DSA) & Digital Markets Act (DMA)

Rules for platforms and AI-supported services with focus on transparency, provider responsibility, and user protection.

National Regulations

Germany plans additional requirements for AI security, transparency, and ethics. Companies should monitor developments and adjust processes early.

Guidelines for AI Use

Privacy & GDPR

  • Only enter anonymized personal data in AI tools.
  • Release cloud services only after risk analysis.

Transparency & Accountability

  • Document which teams use which tools.
  • Name responsible persons for approvals and reviews.

Ethical Use

  • Do not generate discriminatory content.
  • Clearly regulate AI use in communication, marketing, or HR.

Employee Training

  • Create awareness of opportunities and risks.
  • Offer practical help, e.g., guides for anonymization.

Technical Security

  • Control access via company accounts.
  • Perform regular updates, penetration tests, and backups.

Governance, Monitoring & Documentation

Sustainable AI use requires clear responsibilities, continuous monitoring, and traceable decisions:

  • Committees & Roles: Appoint an interdisciplinary AI committee from IT, privacy, legal, and departments.
  • Model Registry: Record which AI models are trained or used with which data.
  • Monitoring: Monitor output quality, bias, security incidents, and document deviations.
  • Incident Response: Define processes for responding to misconduct, leaks, or support requests.

Implementation Roadmap for AI Projects

  1. Define Use Case: Set business goal, data sources, and success criteria.
  2. Risk & Privacy Assessment: Check data classification, protection needs, legal foundations.
  3. Tool/Model Selection: Evaluate cloud vs. on-premise, open source vs. proprietary, security level.
  4. Proof of Concept: Test with anonymized or synthetic data, gather feedback.
  5. Rollout & Training: Document processes, train users, ensure support.
  6. Operations & Review: Measure performance, reassess risks, refine guidelines.

Checklist for Safe AI Use

Topic Measures
Data & Privacy Anonymize personal data; work GDPR-compliant
Tool Selection Risk analysis cloud vs. local; only use verified providers
Responsibility & Control Review AI outputs; define responsible persons
Training Sensitize employees; provide handbook for AI use
Compliance & Ethics Document guidelines; adhere to ethical principles
Security Manage access rights; plan updates and backups
Future Laws Observe EU AI Act; monitor national requirements

Practical Examples

  • HR & Recruiting: Pseudonymize application documents with Text Anonymizer before LLMs create analyses.
  • Legal & Compliance: Have contract drafts reviewed, but remove confidential data beforehand.
  • Product Development: Anonymize support tickets to cluster feature requests.
  • Research & Innovation: Test AI prototypes with synthetic data before using real customer data.

Tips for Operational Daily Business

To enable safe use of AI in daily business, these guidelines help:

  • Prepare standardized prompts that contain no personal data.
  • Prefer local or on-premise models when sensitive data must be processed.
  • Establish approval processes for published content (four-eyes principle).
  • Maintain automated logs of which texts or files were transmitted to AI platforms.

Conclusion

A structured, legally compliant AI use protects your company from privacy violations, reputation risks, and legal consequences. With clear guidelines, training, and the use of local or controlled tools, AI remains a productivity driver – without privacy compromises. Establish governance structures, sensitize employees, and regularly review workflows. This way you combine innovation with compliance and create trust among customers, partners, and team.