Artificial intelligence (AI) offers enormous opportunities - and at the same time presents companies with new regulatory challenges. The times when experiments in the gray area were tolerated are over: with the EU AI Act (expected to take effect from the end of 2025 / beginning of 2026) and ever stricter data protection requirements, compliance is becoming a must.
But what does this mean in concrete terms - and how do companies put it into practice?
1. what does "compliance" mean in the context of AI?
Compliance means that AI systems must comply with applicable laws, industry-specific requirements and the company's own guidelines. right from the start comply with. This includes data protection (e.g. GDPR), IT security, ethical standards, traceability and risk management.
Important: AI compliance is not just an IT task - it affects strategy, processes and technology and Corporate culture.
2. the most important regulations
EU AI Act
The new EU law classifies AI systems according to risk classes. Particularly strict requirements apply to high-risk applications (e.g. HR, lending, critical infrastructure): complete documentation, transparency, continuous monitoring, human control and regular audits.
GDPR
Every AI that processes personal data must ensure data protection by design: Data minimization, purpose limitation, deletion concepts, consent and data subject rights are an integral part of the system design.
Industry-specific rules
In sectors such as health, finance or public administration, there are additional strict requirements that need to be incorporated into planning at an early stage.
3. central challenges in practice
- Transparency: Results must be explainable. Projects have shown that auditability is not possible without comprehensible model logic.
- Data basisOrigin, legal basis and up-to-dateness of the data must be verifiable at all times.
- LiabilityWho is responsible when AI delivers results that lead directly to actions or decisions - providers, users or both?
- Human controlDefined intervention points ("human in the loop") safeguard critical decisions.
- Continuous monitoringCompliance is not a one-off check - monitoring, feedback mechanisms and regular adjustments are mandatory.
4 Practical steps towards AI compliance
- Systematic risk analysis: Record all use cases at an early stage and evaluate them according to risk classes.
- Documentation & audit trailsEvery model change, every use of data and every decision should be automatically logged ("auditable by design").
- Data protection & IT securityData protection impact assessment (DPIA), role-based authorization concept, penetration tests and EU hosting.
- Transparency & communicationUser information, opt-out options and clear explanations increase trust.
- Feedback and control systems: Anchor user feedback and incident response processes directly in the system.
- Training & GuidelinesClear guard rails instead of bans - otherwise shadow IT will be created via private accounts.
- External audits & certifications: Recommended for high-risk AI - standards such as ISO/IEC 42001 provide orientation.
5. best practice from implementation
In a medium-sized production company, generative AI tools were to be completely banned for safety reasons. After an internal risk and potential analysis, the decision was made to Introduction of a locally hosted system with clear role and authorization concepts, fixed deletion periods, documented approval processes and mandatory employee training.
The result: higher productivity, minimized security risks - and robust proof of compliance.
6. typical tripping hazards
- Unclear responsibilities (IT vs. legal vs. specialist department)
- "Black box" AI without explainability
- No regular checks
- Missing documentation or unclear data origin
- No processes for incidents or complaints
7 Conclusion: Compliance is teamwork - and an ongoing project
AI compliance is neither the sole responsibility of IT nor a purely legal project. It requires interaction between management, specialist departments, data protection, IT and users.
Those who wake up early Transparent processes, seamless documentation and continuous improvement not only minimizes risks, but also creates genuine trust - both internally and externally.
Curious?
From October we offer a AI readiness check with which you can quickly and practically assess the current status of your company - including recommendations for your next steps.
