Data Loss Prevention for Enterprise AI Systems

AI DLP is data loss prevention applied to AI interactions rather than files. Every request to a model passes three checks before it leaves: model authorization (is this user allowed to use this AI model), purpose authorization (is the intended use case permitted under policy), and sensitive data inspection (does the prompt, file, or context contain PII, confidential records, credentials, or proprietary content). The outcome of every check is logged.

Traditional DLP has one lever at the AI boundary - ban the destination or allow it - and where it inspects, it matches patterns inside known file formats rather than reading what the content means. An AI interaction gives it nothing to work with: free text typed into a browser, a file dropped into a desktop app, or an API call from an internal tool. Domain-level controls can see that traffic reached an AI service, but not whether that user was authorized for that model, whether the use case was permitted, or which entities were in the payload.

Model authorization is the first check: whether this specific user is entitled to use this specific AI model at all. Entitlements come from your identity provider and org structure, so a contractor, an analyst, and a CFO can each have a different set of approved platforms - and a personal account on an otherwise approved service can be treated differently from a managed corporate one.

Access to a model is not blanket permission to use it for anything. Purpose authorization checks the intended use case against policy, so the same employee on the same platform can be cleared to draft marketing copy while being barred from candidate screening or credit decisions. Keeping it separate from model authorization is also what lets high-risk use cases be gated or escalated without banning the tool outright.

There are two block outcomes. Access Denied means the user was not authorized for that model, or the use case was not permitted - the request stops at check 1 or 2, before any payload is transmitted. Sensitive Data Blocked means the user and purpose were fine but the prompt or file carried sensitive data at check 3. Both are logged with the reason, the policy version, and the user.

Where your policy allows it, Walled Redact masks the sensitive values rather than stopping the request: the model receives the structure and context of the prompt with the real names, identifiers, and figures replaced by tokens, and the real values are restored only in the response the employee sees. Some data genuinely can't leave, and for that the block stands.

PII, PHI, financial identifiers such as account and card numbers, credentials and secrets, source code, contract terms and deal values, and customer or client identities - 50+ entity types across 15+ modalities. The scan is deep rather than format-based: it opens the document, reads the text inside a screenshot, parses code, and works on meaning in context, so an unformatted customer list inside a paragraph is caught as surely as a card number. Custom entity types cover anything specific to your business.

Browser sessions in Chrome, Edge, and Safari; native desktop AI clients such as ChatGPT, Claude, and Copilot that run outside the browser; and APIs or integrations - internal tools, IDEs, notebooks, automated workflows, and agents that call a model directly. All three surfaces run through the same three checks and the same policy.

The three checks complete inline with under 30ms of overhead on a typical request, so enforcement happens before anything leaves your boundary without a noticeable delay for the user.

Yes. WalledAI is model and vendor agnostic. The same policy applies whether the request goes to ChatGPT, Claude, Gemini, Copilot, Perplexity, Llama, Mistral, or a self-hosted model, because the checks run on the request path rather than inside the model.

They answer different questions and run alongside each other. Legacy DLP makes a coarse decision about a destination - allow the channel or ban it - and inspects by matching patterns inside known file formats. AI DLP decides per request: whether this user may use this model, whether the use case is permitted, and what a deep scan of the payload finds in the prompt, document, image, code, or context. Both can feed the same SIEM and GRC workflows.

Every request is logged with the user and role, the AI model and account type, the use case, the entities detected, the policy version applied, and the outcome - allowed requests included, not just blocks. The Governance Dashboard exports this as evidence mapped to frameworks such as the EU AI Act, MAS TRM, NIST AI RMF, PDPA, HIPAA, and SOC 2.

Yes. WalledAI deploys on-premise, in your private cloud, or fully air-gapped. Prompts, masking maps, and audit logs stay inside your boundary, which matters for a control whose whole purpose is preventing sensitive data from reaching third parties.