Compliance

What does compliance mean in the field of AI and why is it so important for companies? You can find out more about this topic here.

What does compliance mean?

In the AI context,compliancemeans adhering to rules, laws and ethical guidelines when developing and using AI systems. As AI is increasingly being used in sensitive areas, companies and organizations must ensure that their AI solutions arelegally compliantandresponsible. AI compliance includes data protection laws (e.g. GDPR), industry-specific regulations, theEU AI Actand internal company guidelines.

Aspects of AI compliance

• Data protection & security: AI systems must not collect or pass on any personal data without authorization. Information fed in – for example when using cloud-based LLMs – must be protected in accordance with applicable law. For example, sensitive customer data should not be entered directly into external AI services, as this data is often transferred to US servers and processed there.

• Freedom from discrimination: Closely linked to bias issues – an AI system must be checked to see whether it discriminates against certain groups (gender, ethnicity, etc.). Legal principles of equal treatment should not be violated.

• Transparency & traceability: Many regulations require that decisions made by an AI must be explainable. For high compliance in certain fields of application, it is necessary to document the decision-making processes (e.g. for automated credit scoring).

• Robustness & security: Regulations require that AI systems function reliably and do not pose any disproportionate risks. This includes regular testing, validation and contingency plans in the event that an AI exhibits misconduct.

Significance and implementation

As AI becomes more widespread, governments around the world are tightening regulations. In the EU, for example, a risk-based approach applies: applications in critical areas (such as medicine or justice) are subject to strict regulations. Companies are responding withcompliance frameworksspecifically for AI – including internal audits, ethics committees and developer training. Some providers already offer specialized software to support compliance measures. One example of this is theAI Gateway from VIER, which provides a central platform on which various compliance functions such as audit trails and privacy managers can be integrated and controlled.

Ensuring AI compliance ultimately means gaining the trust of users, customers and supervisory authorities. Proactive compliance with the rules reduces the risk of sanctions, reputational damage and also prevents possible misuse of AI. In short: compliance creates a reliable framework in which innovative AI applications can be developed without uncontrolled growth and legal violations.