Data Retention in AI: Where Does Your Data Go, and Why It Matters for Enterprises

Your Data’s Journey in AI Models

As enterprises increasingly experiment with AI tools, one topic comes up again and again: data retention. No matter how powerful these tools may be, enterprises can’t afford to use them without clarity on the data setup:

• How long is your data stored?

• Where is it stored?

• What is it used for—training, moderation, or something else?

For businesses in regulated industries—like finance, healthcare, or anywhere GDPR

applies—understanding these nuances isn’t optional. It’s critical.

How You Access AI Changes Data Retention Policies

Enterprises typically interact with AI models in one of two ways:

1. Through Applications: Tools like ChatGPT, Github Copilot, or industry-specific chatbots.
2. Through APIs: Direct integration with models like OpenAI’s GPT-4 or Anthropic’s Claude, often via cloud providers.

The difference matters—especially when it comes to data retention, compliance, and control.

AI Through Applications: Convenience with Trade-offs

Applications make it easy to experiment with AI: no setup, no maintenance. But for enterprises, this convenience comes with complexity—particularly around data retention and residency.

More Vendors, Fewer Standards

While most enterprises default to trusted cloud providers (AWS, Azure, Google, OpenAI), the landscape of AI application vendors is much broader. These smaller players often lack established best practices around data handling.

What Happens to Your Data?

1. Input Data: Typically stored temporarily to enable functionality (e.g., remembering a chat session).
2. Output Data: Often saved in user accounts unless explicitly deleted (e.g., ChatGPT history).
3. Training Risks:
   1. Public tools like ChatGPT (free or Plus) may use inputs to train models.
   2. Enterprise versions guarantee no training, but that doesn’t mean no data retention.
4. Moderation Retention: Even if data isn’t used for training, it may be retained for moderation purposes, or as part of a broader feature set. For example, queries and outputs might be logged for system improvements.

In our experience at Avido, employees rarely understand these complexities or realize how their data may be processed. The best way to secure your enterprise data? Centrally vet and enable approved tools to prevent well-intentioned employees from using convenient, unvetted applications.

AI Through APIs: Greater Control—But Read the Fine Print

APIs offer enterprises more control: you send input, get output—end of story. Or so it seems.

1. Data Retention for Training

Good news: Large API providers do not use inputs or outputs for training. Your data remains yours.

2. Data Retention for Moderation Purposes

Here’s where things get trickier:

• Many providers retain data for 20-30 days to ensure safe and appropriate usage of their models.
• While this data isn’t used for model training, it can be reviewed. Insights from such reviews may inform moderation system improvements.
• Example: If a query triggers a review (e.g., inappropriate content), findings might help refine moderation logic—but the actual input won’t train the underlying model.

3. Enterprise-Grade Options for Data Residency

Enterprise-grade solutions offer stricter data control policies, including:

• Data Residency: Many providers allow data to remain within specified regions, such as the EU, to meet compliance requirements like GDPR. This ensures input and output data are processed locally.
• Retention Policies: Enterprise plans often guarantee no data retention for training or moderation purposes unless explicitly enabled. However, it’s worth noting that moderation features—when enabled—can still result in temporary retention for safety and abuse monitoring.

These enterprise-focused offerings help organizations address compliance concerns without compromising on the capabilities of the underlying AI models.

Moderation: Why It’s Necessary—And What to Consider

Turning on moderation can be the right move, especially for direct-to-consumer AI applications. Moderators handle:

• Inappropriate Content: Detecting harmful or sensitive outputs.
• Malicious Use: Preventing abuse, like using the model for spam or disinformation.

While you can implement custom moderation in your own application, it comes with risks—like reputational damage if malicious actors exploit gaps.

Minimizing Retention Risks with Smarter Architectures

One overlooked strategy for managing data retention? Limit what data you send to the model in the first place.

• Keep sensitive data on your side and add it client-side after receiving the model’s response.
• This approach reduces retention exposure while maintaining functionality.

(More on this in our upcoming post on AI application architectures.)

Myths vs. Facts: What Enterprises Need to Know

1. Myth: “If I use an API, my data disappears instantly.”

Fact: Many providers retain data for 20-30 days for moderation—but not for training.

1. Myth: “AI applications like ChatGPT or Claude aren’t safe for enterprises.”

Fact: Enterprise-grade versions offer stronger guarantees—but limitations (like OpenAI’s lack of EU data residency) still apply.

1. Myth: “Data stored temporarily is no big deal.”

Fact: For regulated industries, even short-term retention can conflict with compliance policies—or, at minimum, needs to be mapped and documented.

5 Ways Enterprises Can Take Control of Data Retention

Here’s how enterprise teams can stay in control:

1. Prioritize Data Residency: Use solutions like AWS Bedrock, Azure OpenAI, or Google Cloud to ensure GDPR compliance. Just make sure to read the fine print—data residency guarantees can vary across providers and plans.
2. Choose Enterprise Agreements: Opt for tools with stricter privacy and retention guarantees, such as ChatGPT Enterprise, Amazon Bedrock, or other enterprise-grade offerings.
3. Understand Moderation Policies: Clarify how long providers retain data for moderation and what conclusions, if any, are drawn to refine moderation systems.
4. Implement Internal Data Governance: Centrally vet tools and align team usage with your organization’s data policies to avoid shadow AI usage.
5. Consider Self-Hosting: For highly sensitive data, explore self-hosted models like LLaMA or Falcon. If you need frontier model capabilities, consider cloud providers with moderation disabled—but weigh the trade-offs in safety and abuse prevention.

Conclusion: Data Retention Isn’t an Afterthought

Understanding how AI tools handle your data is just as important as understanding what the tools can do.

• Applications offer ease of use—but come with data residency and retention trade-offs.
• APIs provide more control but require enterprises to stay informed about moderation and storage policies.

Always ask the right questions: Is my data stored? Where? For how long? And who can access it?

In the fast-moving world of AI, trust and transparency matter. By understanding data retention, you can make smarter choices and keep your enterprise data exactly where it belongs—safe, secure, and under your control.

Disclaimer: This blog post is for informational purposes only and does not constitute legal advice. Enterprises should consult their legal, compliance, and IT teams to ensure alignment with applicable regulations and internal policies when evaluating AI tools and data retention practices.