Using Public LLMs with Sensitive Data – Are We Being Careful Enough?

[rom_c]

Hi everyone,

With the rise of public LLM tools (ChatGPT, online AI assistants, etc.), many of us use them to debug code, generate SAS scripts, or analyze problems faster.

But I’ve been wondering about something important —

If we paste real client data, production code, or sensitive datasets into public tools, there may be privacy and compliance risks.

Some things to consider:

• Data may be stored or logged externally

• Possible exposure of confidential information

• Regulatory concerns (HIPAA, GDPR, company policies)

• Intellectual property leakage

A few safer practices I’ve started following:

• Use dummy/sample data instead of real data
• Mask sensitive fields
• Check company policies before uploading
• Prefer internal/enterprise AI tools if available

Curious how others here handle this —
Do you use LLMs with your SAS workflow? Any best practices you follow?

Would love to hear different perspectives.

[Rachel_McLawhon]

I'm with you! It’s a challenging balance—leveraging these tools while keeping sensitive information protected. Your best practices are spot‑on, and I also make a point to be more cautious than necessary when handling anything sensitive.

Thankfully in my role, the situations that demand heightened data privacy are usually pretty clear. Still, it’s always better to be safe than sorry!