Practical Guide to Auditing Privacy of Your Files in Generative AI Tools

The integration of artificial intelligence in graphic design and video creation has transformed productivity, but it has introduced a critical variable: the exposure of intellectual property. When uploading a file to a cloud platform, the user temporarily cedes control over that digital asset, allowing the system to process, store, or even use it for non-explicit purposes. This transfer of control is the starting point where vulnerability begins, as the asset is no longer under the exclusive custody of the creator.

According to the National Institute of Cybersecurity (INCIBE), any data entered into a digital tool must be considered potentially exposed if its terms of use have not been previously reviewed. This premise is especially relevant for professionals handling unpublished sketches, confidential contracts, or client material, where a leak can have significant legal and economic consequences. The opacity of the internal processes of these platforms aggravates this initial risk, making it difficult to trace the final destination of the information.

The first step to regain data sovereignty is to identify which operating system resources the application is requesting. Many AI tools request access to full storage, the camera, or the microphone, justifying it as necessary for functionality, but this often exceeds the real needs of the generative process. It is vital to review these requests before the first execution to avoid unnecessary concessions that expand the attack surface.

It is recommended to apply the principle of least privilege: grant only the permissions essential for the specific function to be used. For example, if you only need to generate an image from text, it is not logical for the application to request access to the entire photo gallery or the browser's browsing history. Restricting these accesses drastically reduces the probability that data unrelated to the project is captured or analyzed without authorization.

Once permissions are defined, it is crucial to review the data retention policy. The key question is: how long are uploaded files kept and, more importantly, are they used to train AI models? Many platforms enable this option by default, meaning your creations could permanently contribute to improving the algorithm without your explicit consent, a fact often overlooked in the terms of service.

In the case of Adobe Firefly, the company highlights that it trains its models with licensed content from Adobe Stock, providing a layer of legal security for commercial use. However, this does not prevent specific user files from being processed in the cloud. It is fundamental to look in the settings for the option to disable data collection of non-essential data and verify if there is an automatic deletion policy after generation, ensuring the data lifecycle ends with the requested task.

The location where the AI model runs determines the risk level. Cloud processing implies that data travels over the internet and is stored on third-party servers, while local processing keeps information within the user's device, drastically reducing the attack surface. This technical distinction is the basis of perimeter security, as it limits exposure to interceptions or unauthorized network access.

Tools like LocalLLM allow running language models directly on the iPhone or iPad, guaranteeing that nothing written is sent to the Internet. For design platforms, it is difficult to find 100% local options for large-scale models, but it is possible to verify if preview or metadata analysis is performed on the device before uploading the final file to the cloud, thus minimizing the exposure time of sensitive data.

Default settings are rarely the safest. It is necessary to enter the account's advanced settings and disable functions such as behavior analysis, ad personalization, or the use of data for research. On platforms like Zoom, official documentation details how AI feature data is managed, allowing the account administrator to control the retention of transcripts and summaries generated by AI, aligning the tool with the organization's confidentiality policies.

For creators working with teams, it is essential to establish an internal policy prohibiting the use of unaudited AI tools for confidential material. This includes verifying if the platform offers end-to-end encryption or if data is stored in specific geographic regions that comply with strict regulations like GDPR. Internal governance must align with the tool's technical capabilities to guarantee coherent and verifiable protection.

AI regulation in the European Union will impose higher transparency and supervision requirements for high-risk systems. This will force platforms to be clearer about what data they collect and how they use it. For the creative professional, this means privacy auditing will cease to be an optional task and become a regulatory compliance requirement, integrating into standard workflows of agencies and design studios.

The practical conclusion is that brand trust is not enough. The technical and legal documentation of each tool must be verified. By adopting a proactive approach, the creator not only protects their work but also positions themselves as a responsible professional in a market where data security is a differentiating value increasingly appreciated by clients and agencies, raising the standard of quality and trust in the sector.