Answers
Customer records and credentials reach language models through prompts, uploads and retrieved documents, and OWASP warns that data users provide can later appear in a model's output. Controls detect and redact or tokenize that data, limit access, and show which AI tools staff use.
Sensitive data enters an LLM through what people and systems send it and can leave through what the model returns. OWASP lists personal data, financial details, health records, confidential business data and security credentials, and warns that data users provide can later appear in output. Nightfall describes a common exit: corporate data leaving through personal ChatGPT accounts, unmanaged copilots and browser AI tools.
It can, because placeholders remove context the model may need. Consistent replacements limit the damage: Limina's coreference option gives repeated mentions of one entity the same marker, so the model can still tell people apart, and it also offers masking or synthetic values. Tokenization with controlled re-identification is another route. Compare answer quality on your own prompts before rolling a redaction policy out widely.
GenAI DLP inspects prompts, responses and AI tool activity; traditional DLP mostly inspects files, email and endpoints. In our analysis the difference is where the data moves, and the detection rules can be shared. Cloudflare AI Gateway DLP scans prompts and responses with the same detection profiles as Cloudflare One's DLP, and Nightfall describes inspecting and blocking prompts, tool calls and responses inside a live session.
Pick an action per detection: pass, flag or block. Cloudflare AI Gateway DLP offers those three; Flag records findings in a response header and the logs while the response still reaches the client, and Block discards the provider's response and returns an error. Harmonic describes the same rollout path: warn and log first, then block high-confidence events. Flagging first shows your false positives before users feel them.
Some products can stop sensitive prompts before they reach public AI tools, often through a browser extension, but confirm upload coverage in writing. Harmonic says its Guide product stops a flagged prompt and shows the user what triggered it, with blocking added for high-confidence events. Nightfall says it can stop corporate data leaving through personal ChatGPT. Neither vendor's pages we read mention file uploads, so ask each vendor.
A privacy vault stores the real sensitive values and hands out tokens in their place. Skyflow describes tokenization as swapping sensitive data for non-sensitive tokens, with role-based controls deciding who sees masked, redacted or plaintext data and who can re-identify tokens. In our analysis, an LLM then works only with tokens, and only authorized systems map them back to real values.
No. Presidio detects and masks private data in text and images, and its own documentation says there is no guarantee it will find all sensitive information and that additional systems and protections should be employed. Pair it with access controls, logging, limits on what you send to a model, and a review process for what the detector misses.
LLM02:2025 Sensitive Information Disclosure is the OWASP entry for data exposed through an LLM application. Its mitigations include data sanitization, strict access controls, restricted data sources, federated learning, differential privacy, user education, and tokenization and redaction. OWASP also warns that system prompt restrictions may not be honored and can be bypassed through prompt injection, so instructions alone cannot protect data.
Start with an inventory of AI use, then set policy by tool, account and data type. Harmonic describes a browser extension that inventories AI interactions, including AI features inside sanctioned apps and personal accounts, and Nightfall describes stopping data leaving through personal ChatGPT and unmanaged copilots. For AI assets inside your own cloud accounts, look at discovery and posture tools instead.