Prompt DLP: Stop Secrets and PII Reaching LLM Providers

A backend engineer pastes a failing database migration into ChatGPT, including the production connection string in the error message. A data scientist sends customer records to Claude to help write a data transformation script. A DevOps engineer asks Copilot to debug an infrastructure provisioning script that contains Azure client secrets in environment variables. Every one of these prompts sends sensitive data to a third-party LLM provider — outside your enterprise boundary, outside your control.

The Scale of the Problem

Enterprise AI adoption is accelerating. Over 80% of Fortune 500 companies now use ChatGPT or similar tools. GitHub Copilot is active in over 1.8 million organizations. Anthropic's Claude is embedded in workflows across financial services, healthcare, and technology companies. Every one of these tools processes user prompts on external infrastructure. If a prompt contains a secret or PII, that data has left the building.

The risk is not theoretical. Samsung famously banned ChatGPT after engineers leaked proprietary source code through prompts. Multiple organizations have reported incidents where database credentials, API keys, and customer data were included in prompts sent to external AI providers. Traditional DLP tools were not designed for this vector. They monitor email, file sharing, and web uploads. They do not intercept AI prompts.

What Is Prompt DLP?

Prompt DLP (Data Loss Prevention for AI) is a category of security control that inspects outbound AI prompts for sensitive data before they reach the LLM provider. Netallion AI Assurance's Prompt DLP applies the same 497 detection patterns and BPE tokenization engine used for log and code scanning to every outbound prompt.

When a prompt contains a detected secret or PII, Netallion applies one of three configurable actions:

• Audit — Log the detection, allow the prompt through. Useful for initial deployment to understand the scope of exposure without disrupting workflows.
• Redact — Replace the detected secret or PII with [REDACTED] and forward the cleaned prompt. The developer gets their AI response; the LLM provider never sees the credential.
• Block — Reject the prompt entirely and notify the user. Reserved for the most sensitive data categories or high-risk environments.

What Gets Caught

Netallion Prompt DLP detects the same categories of secrets in AI prompts that it detects in logs, code, and collaboration tools:

• Cloud credentials: AWS access keys, Azure connection strings, GCP service account keys
• API tokens: OpenAI keys, Stripe keys, GitHub PATs, SendGrid tokens
• Database credentials: PostgreSQL, MySQL, MongoDB, Redis connection strings
• Infrastructure secrets: SSH private keys, JWT tokens, OAuth secrets
• PII: Social Security numbers, credit card numbers, email addresses, phone numbers

BPE tokenization catches secrets that regex patterns alone would miss — particularly obfuscated credentials, non-standard formats, and credentials embedded in larger code blocks.

Compliance and Auditability

Prompt DLP is not just about preventing leaks. It generates evidence for compliance frameworks that increasingly require controls on AI data flows. Netallion maps Prompt DLP detections to OWASP LLM Top 10 categories (particularly LLM06: Sensitive Information Disclosure), NIST AI RMF controls, and EU AI Act transparency requirements.

Per-user prompt hygiene scores track how often individuals include sensitive data in AI prompts. These scores feed into security awareness programs and help identify teams or individuals who need additional training or tooling (such as better secret management practices with vaults and temporary credentials).

Deployment

Netallion Prompt DLP supports OpenAI, Anthropic, and Azure OpenAI endpoints. It integrates at the network or proxy layer, requiring no changes to the developer's workflow. Prompts are inspected in transit, detections are logged, and configured actions (audit, redact, or block) are applied before the prompt reaches the provider.

Most organizations start in audit mode to establish a baseline of exposure, then move to redact mode for the majority of cases, with block mode reserved for specific high-risk data categories like PII or production database credentials.

The Bottom Line

AI tools are productivity multipliers. Blocking them entirely is not realistic. But allowing unrestricted prompt traffic to external LLM providers is a data loss vector that traditional DLP tools do not cover. Prompt DLP closes this gap — letting developers use AI tools while ensuring secrets and PII never leave your boundary.