Based on these challenges, here’s a recommended set of rules (or best practices) companies should follow when embedding AI into business processes — to reduce risk, maintain trust, and stay on the right side of regulation.

1. Perform scoping and risk assessment early

• At the ideation or planning phase, assess whether the proposed AI use will process personal data, and whether it will be “high risk.”
• Map out all data flows: where data comes from, who processes it, how it’s moved, stored, or shared.
• Conduct a Data Protection Impact Assessment (DPIA) and — where required under AI Act — a Fundamental Rights Impact Assessment.
• Decide roles: Who is controller, processor, provider, deployer? Clarify responsibilities.

2. Limit data collection and processing (minimization by design)

• Only collect what is strictly needed. Avoid capturing extra fields “just in case.”
• Where possible, start with pseudonymized or aggregated data, or synthetic data, before resorting to raw personal data.
• Use privacy-enhancing technologies (PETs) where feasible (differential privacy, federated learning, encryption, SMPC).

3. Define and document purpose strictly

• Specify in writing the purpose(s) of each data processing step (for training, inference, feedback loops) before doing it.
• If later you wish to reuse data for a different purpose, revisit it through compatibility assessment or solicit fresh consent/authorization.
• Ensure the purpose is communicated clearly (transparently) to data subjects.

4. Choose a lawful basis and maintain justification

• For personal data use, decide whether consent, legitimate interest, or contractual necessity (or other permitted basis) applies.
• For “special categories” of data (health, biometric, race etc.), stricter conditions apply. Under GDPR, usually prohibited unless explicit consent or a narrow exception; under the AI Act, some limited use for bias mitigation is permitted under strict safeguards.
• Document the legal basis, balancing tests (e.g. for legitimate interest), and internal decision rationale.

5. Build transparency, explainability and user rights into the system

• Use Explainable AI (XAI) techniques where possible, or at least generate understandable rationales or simplified summaries of decisions.
• Provide clear, accessible notices to individuals: what data is used, why, for how long, who else sees it, rights they have.
• Ensure systems can respond to rights requests (access, rectification, erasure, objection).
• Design rollback or override mechanisms where users contest decisions or ask for human review.

6. Embed human oversight and validation

• Especially for high-risk AI, ensure decisions are not purely automated: humans should monitor, review, override when needed.
• Implement thresholds or flags for uncertain cases or high-impact predictions.
• Log human review actions and decisions.

7. Test, validate and mitigate bias or unfair outcomes

• Before deployment and periodically after, run bias audits, fairness testing, and scenario checks.
• Use diverse teams and datasets to spot blind spots or discrimination.
• If unfair behavior is detected, adjust the model, retrain with balanced data, or limit usage in sensitive subpopulations.

8. Secure models, data, and infrastructure

• Apply strong encryption in transit, at rest, and if possible for computations (homomorphic encryption, secure enclaves).
• Limit access to data and model internals only to authorized personnel.
• Conduct adversarial testing: check for inversion or membership inference vulnerabilities.
• Maintain logs, versioning, change control, and regular penetration tests.

9. Manage cross-border transfers carefully

• If data or model parameters are transferred outside the EEA, rely on GDPR-approved mechanisms (SCCs, BCRs, etc.).
• Reevaluate whether “anonymized” data is truly anonymous; pseudo-data may still be subject to GDPR.
• Document all transfers and safeguards.

10. Maintain accountability through documentation and audit trails

• Keep detailed records of all processing operations (Article 30).
• Document decisions at each stage: risk assessments, justification of choices, human reviews, model changes.
• For high-risk systems, retain logs of model inputs/outputs, versioning, test metrics, explanation logs, audits.
• Be prepared to show compliance to regulatory authorities.

11. Monitor, review and adapt

• AI systems evolve; regular reviews are needed.
• Monitor for drift, bias, or degradation over time; retrain or update models as needed.
• Update documentation, notices, and DPIAs to reflect changes.
• Stay informed about legal developments, regulatory guidance, and case law.

12. Be reactive: plan for incident response and redress

• Prepare mechanisms for handling complaints, probes or corrections (e.g. false positives, discrimination).
• Maintain a fallback safe mode if the AI behaves erratically.
• In case of breaches or non-compliance, have a plan for notification, mitigation, and remediation.

1. A European authority fined Clearview AI for assembling a database of biometric material without proper justification or transparency.
2. Advocacy groups filed a complaint against platform X (formerly Twitter) for using user data to train AI models without sufficient consent.
3. Regulators are investigating Google’s handling of personal data in its AI models under GDPR.

These cases underscore that data regulators are increasingly willing to challenge large, complex AI systems — even ones from tech giants.

Data protection isn’t a hurdle to AI adoption — it’s the foundation of sustainable innovation. The companies that win with AI are those that treat compliance not as a legal checkbox but as part of system design. When privacy, explainability, and human oversight are built in from day one, AI projects scale faster, integrate more easily, and face fewer roadblocks later.

At Blocshop, we help businesses build and integrate AI systems with this balance in mind. From early discovery and data-flow mapping to secure architecture, API integration, and post-deployment monitoring, our approach combines engineering precision with regulatory awareness.

Whether you need to connect your existing stack with AI-driven automation, develop a compliant data pipeline, or modernize legacy systems to meet the EU AI Act requirements, we design solutions that keep your data — and your reputation — protected.

Contact us to discuss how to make your next AI initiative both powerful and compliant.