Artificial intelligence is rapidly becoming a routine part of laboratory work, supporting everything from documentation and data review to administrative and analytical tasks. As these tools move from optional to embedded, questions about how laboratory data is handled, stored, and protected are becoming harder to ignore.
New research from NordVPN underscores how unprepared many workers remain. According to 2025 data from the company’s National Privacy Test, 94 percent of Americans do not understand the privacy risks to consider when using AI tools at work. In laboratory environments that manage regulated data, proprietary research, and sensitive client information, that gap creates meaningful exposure.
Observed on January 28, Data Privacy Day offers timely context for these findings. The annual awareness effort focuses on responsible data collection and protection, making it a natural moment for lab leaders to reassess AI usage, governance policies, and staff training. As AI adoption continues to outpace formal oversight, laboratory AI privacy concerns are increasingly operational issues rather than purely technical ones.
Why laboratory AI privacy matters
Laboratories operate under heightened expectations for data integrity, confidentiality, and traceability. As AI tools become more common in research, clinical, and operational workflows, they introduce new privacy considerations that traditional information security policies were not designed to address.
The National Privacy Test findings suggest that AI adoption has outpaced user understanding of how data is logged, stored, and reused. For laboratories, this disconnect increases the risk that sensitive information may be shared with external systems without appropriate safeguards or oversight. Even when AI tools improve efficiency, unmanaged use can create vulnerabilities that affect compliance, intellectual property protection, and institutional trust.
“Unlike a conversation with a colleague, interactions with AI tools can be logged, analyzed, and potentially used to train future models,” said Marijus Briedis, chief technology officer at NordVPN. “When employees share client details, internal strategies, or personal information with AI assistants, they may be creating privacy vulnerabilities they never intended.”
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Key AI privacy risks in labs
As laboratories integrate AI into routine workflows, several recurring AI privacy risks in labs have emerged that directly affect data security, compliance, and operational trust:
Unintentional disclosure of sensitive data
Laboratory professionals often rely on speed and efficiency. Copying instrument outputs, experimental notes, or internal communications into AI tools may feel routine, but it can expose sensitive laboratory data to third-party platforms with unclear retention or training practices.
AI data privacy in laboratories becomes especially challenging when staff use consumer-grade AI tools outside approved IT environments. Data Privacy Day serves as a reminder that convenience-driven behavior can undermine established safeguards.
Growing exposure to AI-enabled scams
AI privacy risks in labs extend beyond internal data handling. The same technologies driving productivity gains also support more convincing cyberattacks. According to the National Privacy Test, 24 percent of Americans cannot correctly identify common AI-powered scams, including deepfakes and voice cloning.
For laboratories, this raises concerns about fraudulent vendor requests, spoofed leadership communications, and compromised procurement or finance workflows.
What lab leaders can do on Data Privacy Day
Data Privacy Day offers lab leaders a practical opportunity to translate AI privacy awareness into concrete governance, training, and risk-mitigation actions:
Formalize AI usage policies
Data Privacy Day provides a natural checkpoint to review or establish clear AI governance policies. These should define which tools are approved, what types of data must never be entered into AI systems, and how AI-generated outputs should be reviewed and stored.
Integrate AI privacy into training programs
Policies alone are insufficient without reinforcement. Incorporating AI data privacy in laboratories into onboarding, annual compliance training, and lab meetings helps ensure staff understand expectations and risks.
“People are typing confidential information into AI tools without realizing where that data goes, how it’s stored, or who might have access to it,” Briedis said.
Reinforce verification and reporting practices
Given the rise of AI-driven scams, lab leaders should reinforce verification protocols for sensitive requests, including secondary confirmation steps and clear reporting pathways for suspected fraud.
Data Privacy Day as a laboratory governance checkpoint
Data Privacy Day is more than a symbolic observance. For laboratory leaders, it offers an opportunity to assess whether data protection strategies have kept pace with AI adoption. Addressing AI privacy risks in labs through policy, training, and oversight allows laboratories to benefit from AI tools while maintaining data integrity, compliance, and trust.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
