AI Data Loss Prevention (DLP): a complete guide

What is AI DLP?

AI data loss prevention (AI DLP) is a category of security control that inspects the content employees send to generative AI services and blocks, warns on, or logs anything sensitive before it leaves the device. It is a response to a simple shift in behaviour: people now paste spreadsheets, customer records, contracts and proprietary code straight into an AI chatbot to get work done faster.

Traditional data loss prevention watches email, USB drives and file uploads. AI DLP watches the new exfiltration channel — the prompt. The moment an employee types or pastes into ChatGPT, or an IDE assistant sends a code file to a model, that data is leaving your control. AI DLP inspects that content in real time and applies policy.

Why AI DLP matters now

Adoption ran ahead of governance. Surveys through 2025 consistently found that a large share of employees paste sensitive work data into AI tools, often through personal accounts their employer cannot see. That behaviour is called shadow AI, and it is the single biggest driver of AI data leakage.

• The data never comes back. Once a prompt reaches a third-party model, you cannot recall it. It may be logged, cached, or used to train future models depending on the provider and plan.
• It is invisible to legacy tooling. Prompts go out over ordinary HTTPS to trusted domains, so email and network DLP never see them.
• The regulatory exposure is real. Leaking personal data into an AI tool can be a reportable breach under GDPR and the UK DPA 2018.

What AI DLP should detect

Effective AI DLP classifies content by category and lets you set a different action for each. The categories that matter most in practice are:

• PII — names, emails, phone numbers, addresses and national identifiers such as NI or Social Security numbers.
• PCI / payment data — card numbers, bank accounts and sort codes.
• Credentials and secrets — API keys, tokens, passwords and connection strings.
• Confidential IP — source code, internal documents and trade secrets.

How AI DLP works

There are two broad architectures. A network gateway approach routes AI traffic off the device to a cloud service that inspects requests. It is easy to centralise but breaks on desktop apps, blind to anything that bypasses the gateway, adds latency, and means your prompt content is transmitted somewhere else to be scanned. An endpoint approach inspects content at the operating-system level on the device itself, before it reaches an AI service — covering browsers, desktop apps and IDEs, with no browser extension to install per-app.

Redbax takes the endpoint approach. The agent inspects AI traffic through a proxy that runs locally on the device — not a gateway in the cloud — so the content being classified never leaves the machine, and policy is applied in single-digit milliseconds. For a deeper comparison, see AI DLP vs traditional DLP.

Block, warn, or log

Good AI DLP is not a blunt ban. Blanket blocking pushes employees to personal devices and makes the problem invisible again. The better model is graduated policy: block the highest-risk categories, warn on medium risk so the user can make a judgement call, and log everything for visibility. That keeps AI useful while closing the leak.

AI DLP buyer's checklist

• Inspects prompts and file uploads across desktop apps and IDEs — not just the browser.
• Classifies PII, PCI, credentials and source code with low false positives.
• Runs detection locally so content is not sent elsewhere to be scanned.
• Supports per-category block / warn / log policies by team or role.
• Gives the SOC a real-time view of what is being shared, storing metadata only.
• Meets GDPR / UK DPA requirements with configurable data residency.

Redbax is built to this checklist. See the full feature set or register your interest.