Run the three-part GDPR test — purpose, necessity, and balancing — and get an AI-powered analysis of your Art. 6(1)(f) lawful basis with documentation guidance.
Run the three-part GDPR test — purpose, necessity, and balancing — and get an AI-powered analysis of your Art. 6(1)(f) lawful basis with a structured LIA record ready for your DPA.
Questions? Contact us
Describe the processing for which you want to rely on legitimate interests as a lawful basis under GDPR Art. 6(1)(f).
The GDPR requires a legitimate interest that is lawful and clearly defined. Under Recital 47–49 and Art. 6(1)(f), the purpose must be genuine and not override fundamental rights.
Under Art. 6(1)(f) and Recital 47, processing must be necessary to achieve the purpose. A less privacy-intrusive alternative that achieves the same result makes legitimate interests unavailable.
Art. 6(1)(f) requires that your interests do not override the interests, rights, and freedoms of data subjects — particularly where they have no reasonable expectation of processing.
Safeguards can tip the balance in your favour. The presence of an opt-out mechanism, clear privacy notice, and documented LIA significantly strengthen the lawful basis.
Enter your email and we'll send you the full three-part test analysis with recommendations for strengthening your Art. 6(1)(f) lawful basis.
Our AI is assessing all three tests against GDPR requirements.
Our GDPR Gap Assessment covers your entire processing register — ROPAs, lawful bases, DPIAs, data subject rights, and third-party transfers — with a personalised roadmap.
View GDPR Assessment →Informational use only. This tool is provided for awareness purposes to help businesses understand their current situation regarding EU regulations. It does not constitute legal, regulatory, or professional advice. Results are indicative only and should not be relied upon as a substitute for qualified legal counsel. Verdaio accepts no liability for decisions made based on this tool’s output. Your inputs are processed ephemerally and are not stored or used for model training.