Employee Fired After Uploading Corporate Documents to DeepSeek: How Data Security Works in AI Services

A top manager at a Moscow engineering company was dismissed after uploading internal documents containing trade secrets to the public DeepSeek service. The court sided with the employer, classifying the action as unauthorized disclosure of confidential information.

The incident reflects a broader trend. Research analyzing traffic from 150 Russian companies found that employees uploaded 30 times more corporate data to public AI services in 2025 than in the prior year. Materials included presentations, code fragments, analytics, and internal correspondence. At the same time, 60 percent of organizations still lack any formal rules governing AI tool usage.

Darya Lushkina, editor and researcher at Rating Runeta, examined these risks with Yaroslav Shmulyov, CTO of AI integrator R77 AI. When a user uploads a file such as a client contract or presentation, the document first passes through standard IT infrastructure including gateways, backend systems, and logging. The service then parses the content, extracts text and structure, and splits the text into chunks that are converted into embeddings—vector representations that capture semantic meaning.

Data therefore exists simultaneously in several forms: the original file, extracted text, text fragments, embeddings, processing logs, and metadata. The most sensitive stage is often the initial storage of the unaltered file on external servers before any further processing occurs.

Additional exposure points include logging systems that may retain fragments of content, third-party cloud providers and moderation contractors, and potential inclusion in training datasets. Once data influences model parameters during training, removal becomes technically irreversible; techniques such as machine unlearning remain an active research area with limited practical results for large language models.

Even when users enable settings that claim to prevent data use for training, the actual enforcement mechanisms are opaque. Service operators, infrastructure partners such as Google Cloud and Azure, and human moderators reviewing selected dialogues may all gain access. In 2024, Wiz Research discovered an exposed DeepSeek database containing over one million chat records and secret keys due to a misconfiguration.

Real-world consequences have already appeared at global companies. Samsung engineers sent proprietary source code and meeting notes to ChatGPT, while a U.S. cybersecurity agency head uploaded documents marked “For Official Use Only.” R77 AI consultants routinely observe similar uncontrolled usage inside client environments, prompting organizations to introduce strict data classification rules and corporate AI instances.

Looking ahead, demand is growing for local and hybrid models that keep data within controlled perimeters, alongside clearer corporate offerings that specify storage locations, training exclusions, and deletion timelines.