Is it safe to share {X} with {Y}?
Sharing research data with ChatGPT is not safe under standard usage conditions. Inputs submitted to ChatGPT can be used by OpenAI to train and improve its models unless API access with data controls is configured. By default, data may be retained for up to 30 days before deletion.
Why this matters
- Research data shared through the standard ChatGPT interface is processed on OpenAI's servers, removing institutional control over that information.
- Unpublished findings, proprietary methodologies, or grant-funded data may violate confidentiality agreements or funding body terms if exposed to third-party systems.
- Once data enters an external model's training pipeline, there is no reliable mechanism to retrieve, correct, or delete it from future outputs.
For enterprise
Employees who submit research data through personal or unmanaged ChatGPT accounts bypass institutional data governance frameworks entirely. This creates direct exposure to compliance violations under data protection regulations, university research ethics policies, and sponsor agreements. Research teams operating under IRB protocols or NDAs face particular risk when data leaves approved secure environments.
Compliances at risk
What counts as Research Data?
- Market research
- User research
- Survey results
- Experimental data
- Research findings
Why people share Research Data with ChatGPT
- To summarize research findings
- To identify trends
- To prepare reports
- To analyze collected data
What actually happens when you paste Research Data into ChatGPT
When you paste Research Data into ChatGPT, that data is transmitted from your device to external servers operated by the AI provider.
Depending on system configuration and policies, the data may be logged, temporarily stored, or reviewed for safety and quality purposes. Retention can last from days to weeks, and in some cases may extend beyond the immediate session.
Statements such as “we do not train on your data” do not eliminate risks related to retention, logging, or internal access. These controls vary by product and setting, and are not always visible to end users.
From a governance perspective, any non-zero retention window introduces exposure risk when sensitive data is shared without controls, auditability, or enforcement.
Risks of sharing Research Data with ChatGPT
- Loss of competitive advantage: Proprietary knowledge may become accessible outside the organization.
- IP theft: Product plans and research may be copied or reused without authorization.
- Innovation leakage: Future product strategy can be exposed before release.
Real incidents
- Samsung employee data leak : Employees exposed confidential source code and hardware information.
- API abuse by alleged DeepSeek-affiliated actors : Unauthorized API activity raised concerns about intellectual property theft.
- Training data extraction : Researchers extracted proprietary content from ChatGPT.
Is this allowed under policy or law?
Safer ways to handle Research Data
Research Data should not be shared with consumer AI tools without controls in place. If AI assistance is required, organizations should use systems that enforce data redaction, access controls, and policy enforcement before data leaves their environment.
- Automatically redact sensitive fields before sending data to AI models
- Prevent unauthorized data from being entered into external tools
- Maintain audit logs and visibility into how data is used
- Ensure compliance with frameworks like GDPR, CCPA, and SOC 2
Platforms like Wald are designed to enable safe AI usage by ensuring sensitive data never leaves your control unprotected.
How Wald.ai handles this safely
Wald adds a governance layer to AI usage, helping organizations monitor and control how sensitive data like Research Data is shared.
AI DLP
Identifies Research Data in context and enables teams to:
- Observe AI usage
- Detect sensitive data in prompts
- Allow, warn, or block actions
- Maintain audit logs
LLM Pack
Provides controlled access to multiple AI models (ChatGPT, Claude, Grok, and others) through a single governed environment.
- Centralized model access
- Policy enforcement
- Usage visibility
- Auditability