A 15-million-euro fine, a class action lawsuit, and a regulator's ruling: your AI policy isn't what you wrote in the document, it's what you can actually prove on every single API call.

Last year, Italy's Garante fined OpenAI €15 million, with the core allegation being that ChatGPT used personal data for training without an adequate legal basis and violated transparency obligations. Although that fine was later overturned by a Rome court, the compliance logic it exposed remains a warning for every company using AI: when your employees call AI APIs every day, can you prove data further use limitations, legal basis picked, and what real processing actually looked like?

Similarly, Figma has been accused of silently opting users into using their design data for AI training (not as a technical vulnerability but as a breakdown of GDPR and EU AI Act accountability).

The problem is that the vast majority of companies' AI policies currently consist of a PDF and a promise. Application logs are written by the party that should be supervised, and can be modified, deleted, or simply not recorded.

What regulators will need is not a beautiful dashboard: it's evidence that can be independently verified and isn't controlled by you or your vendors. In 2024, the EDPB made it clear as early: AI models trained on personal data are generally not anonymous, and developers must demonstrate a legitimate interest through a three-step test and provide transparency . No "we train in a black box" legally untenable!

The real compliance threshold is actually quite specific: before every API call your teams make to deepseek, gemini, Anthropic, or Mistral, is the policy enforced in real-time? Can you prove it? Are you ready for a regulator's investigation?

The good news is that we launch an SDK generating signed proof on each call with just one line of code -leaving a verifiable chain of evidence. Worth spending 30 minutes to see what "default enforcement plus cryptographic proof" actually looks like? Contact@idside.eu