DLP for AI: Protect Data Without Blocking AI Workflows
Your employees are pasting client names, contract clauses, financial models, and source code into AI tools every day. Traditional DLP gives you two options: block the tool entirely, or allow it and hope for the best.
There is a third option. Transform the data before it leaves.
The Problem With Block-or-Allow DLP
Data loss prevention was designed for a world where sensitive data moved through known channels — email attachments, USB drives, cloud storage uploads. The security team could write rules: if the data matches a pattern, block the transfer. If it doesn't, let it through.
AI broke this model.
When an employee asks ChatGPT to summarise a client meeting, the prompt doesn't look like a data export. There's no file attachment to scan. The sensitive information: client names, deal terms, internal strategy - is embedded in natural language. It leaves through an HTTPS connection that your network sees as legitimate web traffic.
Worse, the information your people send to AI tools is precisely the information that creates the most value when AI processes it. Blocking AI access to sensitive data means blocking the use cases that matter most.
The fundamental limitation of traditional DLP in AI workflows is this: it operates on a binary. Block or allow. But the data your teams need AI to process is the same data you need to protect. A binary control cannot resolve this tension. You need a transform.
How DLP for AI Should Work
AliasPath™ is a data loss prevention layer built specifically for AI workflows. It sits at your network boundary - as a proxy, API gateway integration, or Docker deployment - and intercepts every prompt and file before it reaches any external AI model.
Instead of blocking the data, AliasPath pseudonymises it in real time. Real names become plausible alternatives. Real addresses become coherent fictional ones. Financial identifiers are replaced with structurally valid substitutes. The AI model receives a complete, semantically coherent prompt - it can reason, summarise, draft, and analyse exactly as your user intended. But the real identities never leave your environment.
When the AI returns its response, AliasPath rehydrates the aliases back to their real values for the authorised user. The experience is seamless. Your teams don't change how they work. The AI doesn't lose context or accuracy. The only difference is that your sensitive data never left your trust boundary.
This is the principle: transform, don't block.
Why "Block" Fails in AI Workflows
There are three structural reasons why block-based DLP doesn't work for AI:
1. The data that matters most is the data you need to protect most. A lawyer asking an AI to review a contract needs the contract text in the prompt. A financial analyst asking for a model comparison needs the actual figures. Blocking the sensitive data removes the value of the AI interaction entirely. This is why most organisations end up in a de facto "allow everything" posture - the productivity cost of blocking is too high.
2. AI data paths are invisible to network-layer DLP. When an employee types a prompt into ChatGPT, the data travels as an HTTPS POST to api.openai.com. Your network DLP sees encrypted web traffic to a legitimate SaaS provider. Without breaking TLS inspection for every AI endpoint - which introduces its own security and privacy problems - network DLP is blind to what's inside the prompt.
3. The risk surface extends beyond the prompt. Even if you could inspect and block risky prompts, the data has already entered your organisation's AI ecosystem the moment it's pasted into a Copilot-connected document, stored in a RAG knowledge base, or used to fine-tune a model. Block-based DLP addresses the front door while the data has already moved through the side entrance.
Use Cases
Legal: Law firms and in-house teams use AI to draft, review, and summarise contracts. AliasPath pseudonymises party names, deal terms, and privileged content before it reaches any model - preventing the privilege waiver risk established in cases like US v. Heppner.
Financial services: Investment teams use AI for research, modelling, and client communications. AliasPath ensures that client identifiers, portfolio data, and MNPI never reach third-party models - even when analysts use AI tools directly.
Healthcare: Clinical teams and administrators use AI for documentation, coding, and patient communications. AliasPath strips patient identifiers while preserving the clinical context AI needs to generate accurate outputs.
Government & public sector: Departments adopting Copilot and similar tools need data sovereignty guarantees. AliasPath keeps real data within the government's trust boundary - no reliance on third-party residency promises.
M&A and corporate transactions: Deal teams use AI to process due diligence materials. AliasPath prevents bidder names, financial terms, and MNPI from leaking through Copilot queries against overshared SharePoint libraries.
Your teams are already using AI with real data. The question is whether you can see it - and whether the data is protected.
Frequently asked questions
What is DLP for AI?
DLP for AI (Data Loss Prevention for Artificial Intelligence) refers to security controls that prevent sensitive enterprise data from being exposed through AI tool usage - specifically through prompts, file uploads, and API calls to large language models like ChatGPT, Copilot, and Gemini. Unlike traditional DLP, which focuses on email, cloud storage, and endpoint file transfers, DLP for AI must address the unique challenge of sensitive data embedded in natural language queries.
How is AliasPath different from Microsoft Purview DLP?
Microsoft Purview operates on block-or-allow logic: it scans content for sensitivity labels and patterns, then either permits or prevents the action. AliasPath takes a fundamentally different approach - it transforms the data through pseudonymisation so the AI interaction can proceed with full semantic fidelity while real identities remain protected. Purview blocks the workflow; AliasPath enables it.
Does AliasPath work with private or self-hosted models?
Yes. AliasPath operates at the network boundary regardless of where the AI model is hosted. For organisations running open-weight models internally, AliasPath addresses the risks that self-hosting doesn't eliminate: prompt-layer data concentration, RAG pipeline exposure, insider threat, and regulatory requirements for pseudonymisation as a technical measure.
What about data that's already in a RAG or vector database?
This is a critical blind spot in most DLP strategies. Once sensitive data enters a RAG pipeline or is embedded in a vector store, it can be retrieved by any query with sufficient semantic similarity - regardless of who made the original query. AliasPath pseudonymises data before it enters the RAG pipeline, so the vectors themselves contain only aliases. The real data is never retrievable from the knowledge base.
How does AliasPath handle context across a long conversation?
AliasPath maintains consistent alias mappings within a session. If "Sophie Chen" is replaced with "Emilia Chang" in the first prompt, every subsequent reference to that individual within the session uses the same alias. The AI model sees a consistent narrative. When the conversation ends, the alias mapping is retained under your cryptographic control for audit and rehydration purposes.