Public large language models (LLMs) log every prompt you send. If your SQL holds customer PII, proprietary business logic, or compliance-bound data, that text can be stored on external servers and potentially used to retrain the model. This creates exposure under GDPR, HIPAA, SOC 2, and internal IP policies.
Table names often reveal business secrets, column names may contain personal data, and inline values can disclose revenue or user records. Even a seemingly harmless query can give attackers useful intelligence about your schema.
Replace customer names, IDs, or financial figures with placeholders before pasting the query.
Never include literal values such as emails or credit-card hashes. Use parameterized placeholders instead.
Swap real table and column names with generic aliases (e.g., t1.user_id) so the model cannot infer your business context.
If you must call a cloud LLM API, ensure traffic stays inside a VPC or uses end-to-end TLS.
Yes. Azure OpenAI with No Training enabled, Anthropic Claude inside a private VPC, or fully self-hosted open-source models keep prompts within your own infrastructure. They cost more but eliminate third-party retention risk.
Galaxy processes SQL locally in its desktop app and never ships your text to external servers. Prompts are excluded from model-training, and credentials stay in encrypted local storage. Role-based access control, audit logs, and version history add extra governance so only approved users see sensitive queries.
Because the Copilot is context-aware, you can write or refactor complex SQL without revealing full schema details to a public model. This gives you ChatGPT-level productivity with enterprise-grade security.
1. Classify queries by sensitivity
2. Mask or tokenize PII and secret logic
3. Prefer private or on-prem LLMs
4. Use tools like Galaxy that keep prompts local
5. Log and audit all AI interactions
6. Review output for accidental data leakage
7. Update policies and conduct regular security training
ChatGPT is powerful but not designed for confidential workloads. By masking data, choosing private LLM deployments, and leveraging Galaxy’s local AI Copilot, you can enjoy faster SQL development without compromising your company’s security posture.
How do I mask PII in SQL before using AI; Best private LLM options for enterprises; SOC 2 requirements for AI tools; Can I self host ChatGPT models; SQL redaction techniques for compliance
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