Can I just point my existing DLP at AI traffic?

Only partially, and only with work. Network DLP sits under TLS and sees an encrypted session to a provider, so it needs traffic decrypted and the specific API payload parsed to read a prompt at all. Even then it classifies documents rather than the free-text context window, and it cannot bind a personal-key API call to a corporate identity. Extending legacy DLP to AI traffic runs into the same three structural gaps rather than closing them.

Is AI DLP the same as an AI gateway?

AI DLP is a capability; an AI gateway or control plane is often where that capability runs. Inspecting and classifying prompt content is one function of an inline enforcement point at the request boundary, alongside identity-based authorization and per-decision audit. Buying "AI DLP" without an inline enforcement location leaves you with classification and nowhere to enforce it.

Does prompt inspection add latency?

The inspection and policy decision run in milliseconds against model inference that takes 500 ms to several seconds, so the added time is invisible next to the model's own response. Enforcing inline at the request boundary does not impose a latency cost a user perceives, which is why prompt-level DLP can block rather than only alert.

What about the response, not just the prompt?

Response-side inspection matters as much as the prompt. A model can return data the caller is not permitted to receive, whether through retrieval, grounding, or an unexpected completion. AI DLP at the request boundary inspects both directions, so it can redact or block a response that carries sensitive content, which endpoint and network DLP positioned for outbound files do not evaluate.

AI DLP vs Traditional DLP: Why the LLM Request Path Needs a Different Control

Traditional data loss prevention was built for a world of documents and known exits: a file leaving over email, a copy to a USB drive, an upload to a web form. It classifies the document, watches the channel, and blocks the transfer. That model has worked for two decades. It also does not see the channel that matters most for AI, because when an employee pastes a customer record into a model, the sensitive data travels inside an HTTPS POST to a provider API, and traditional DLP sees an encrypted session to a known host and nothing about the data inside it. AI DLP is the response to that gap. I want to compare the two directly on the three axes where they diverge, so a security team can see why one does not substitute for the other. For the full concept, start with the AI DLP pillar.

Traditional DLP: where it operates

Traditional DLP inspects content at rest and in motion through the channels it was designed to watch. It fingerprints documents, applies pattern and classifier rules, and enforces at the endpoint, the mail gateway, and the network egress point. Its model of "data leaving" is a file or a record crossing one of those boundaries.

That model assumes the data is a document and the exit is a channel DLP terminates. Both assumptions break on AI traffic. The data is not a file, it is text inside a prompt's context window. The exit is an API call the network layer only sees as encrypted web traffic.

AI DLP: where it operates

AI DLP inspects the content of the AI interaction itself, the prompt on the way to the model and the completion on the way back. To do that it has to operate where the traffic is decrypted and the API payload is readable, which is the AI request boundary rather than the network layer. At that point it can classify the prompt content, not the source document, and decide whether this data is permitted to reach this model.

The distinction is the context window. A prompt can carry sensitive data that never existed as a labeled document: a paragraph an employee typed, a record pulled from an API and inlined, a synthesis of several sources. Document fingerprinting has nothing to match against. Prompt-level classification evaluates the text as it moves.

The three structural differences

Identity correlation

Traditional DLP ties an action to a user through the endpoint or the corporate mail account. AI calls authenticated with a personal API key break that link: the call identifies the key, not the corporate identity behind it. AI DLP has to consume identity context attached at the request layer to know who is actually sending the prompt, which is the identity-aware part of the problem.

Data classification

Traditional DLP classifies documents. AI DLP classifies the context window. These are different units. A DLP policy that protects a labeled spreadsheet does nothing when the same numbers are typed into a prompt as free text, because there is no document to match. Classification has to move to the prompt content itself.

Enforcement location

Traditional DLP enforces at the endpoint and network egress, underneath TLS. AI DLP enforces at the request boundary, after decryption, where both the prompt and the identity are visible in the same place. Enforcing there is also what lets the decision be inline and deterministic, blocking or redacting the prompt before it reaches the model rather than alerting after it lands.

Why one does not replace the other

Traditional DLP still governs email, endpoints, and file transfers, and those channels have not gone away. AI DLP governs the model request path those tools are blind to. Running AI traffic through legacy DLP leaves the prompt uninspected, and only 37% of organizations have any AI-specific governance policy at all (Netwrix), which is part of why shadow-AI-linked breaches take 247 days to detect (IBM Cost of Data Breach Report). The two are layers of the same program, and the AI layer is the one most enterprises have not yet built.

DeepInspect

DeepInspect provides the AI DLP layer as inline enforcement. It sits as a stateless proxy at the AI request boundary, where each prompt is decrypted and inspected against identity, data classification, and policy before it reaches the model. When a prompt carries data the caller is not permitted to send, DeepInspect blocks or redacts it inline, and commits a per-decision audit record bound to the identity that made the call.

Because it governs the HTTP request rather than the document, it classifies the context window traditional DLP cannot see, and it does so across any model provider. If your DLP program stops at email and endpoints while your AI traffic runs unwatched, book a demo today.