The Data Your AI Creates is Your Biggest GDPR Risk
Your privacy notice is lying to your customers. It meticulously lists the data you collect - names, emails, purchase histories - but ignores the vast, hidden dataset your AI is creating about them every second.
When your AI generates a "churn probability score," segments a user into a "high-value prospect," or flags an employee as a "flight risk," it is creating new, regulated personal data. Under UK and EU GDPR, you are fully liable for this inferred data the moment it's linked to an individual.
At Janus, we've seen this become the single most expensive compliance failure in modern AI deployments. Here is why - and what you need to do about it.
The Hidden Data Factory in Your Tech Stack
Inferred data is the lifeblood of modern business intelligence:
Marketing platforms creating "likelihood to convert" scores.
HR systems generating "engagement risk" indicators.
Financial models producing "creditworthiness" assessments.
Customer service tools labeling "complaint propensity."
Each of these inferences is personal data. Each requires full GDPR compliance. Most organizations have no idea this data even exists in their systems.
Three Compliance Failures That Will Cost You
1. The Transparency Violation (Articles 13 & 14)
Your privacy notice tells users what data you collect, not what you conclude about them. Users have the right to know about the personality profiles your AI builds or the behavioral predictions it makes.
Real Impact: The Dutch DPA fined Uber €290 million partly for failing to be transparent about driver profiling algorithms.
2. The Access & Correction Nightmare (Articles 15-22)
When a user requests their data under Article 15, you must provide every inference you hold on them. If your AI incorrectly labels someone as a "high credit risk," they have the right to demand its correction. They cannot fix what they don't know exists.
This means you need systems to: track every inference, explain how it was generated, and provide mechanisms for users to challenge and delete automated assessments.
3. The Accuracy Time Bomb (Article 5)
GDPR requires personal data to be accurate. AI inferences are probabilistic - they are sophisticated guesses, not facts. Using a 73% confidence score to deny someone a loan is not just a business risk; it may be illegal discrimination.
The Enforcement Reality: Regulators are targeting algorithmic bias. Italy's DPA fined Deliveroo €2.5 million for discriminatory AI inferences about rider reliability.
From Liability to Compliance: The Path Forward
Effective governance for inferred data allows you to innovate safely. It requires a new layer of technical, legal, and operational controls.
Technical Infrastructure: You need inference inventories to map what data is created, lineage tracking from raw data to decision, and constant accuracy monitoring for all models.
Legal Framework: Your privacy notices must explicitly cover categories of inferred data, and you need documented legitimate interests assessments for the processing.
Operational Processes: Your DSAR procedures must be updated to capture inferred data, and you need human review mechanisms for significant automated decisions.
Companies that master inferred data governance avoid fines and also build trust. Being transparent about how your AI makes decisions is a powerful competitive advantage.
At Janus Compliance, we specialize in this exact intersection of AI innovation and data protection. Our Outsourced DPO as a Service builds the comprehensive governance frameworks that let you deploy AI confidently, knowing every piece of data - collected or created - is properly managed.
The age of AI has created a new category of personal data. Is your compliance program ready for it?