Is AI data security the same as LLM data security?

They overlap and sit at different altitudes. AI data security is the program view: the full set of data classes moving through AI traffic (prompts, files, RAG context, tool-call arguments, responses), where they are governable, and what evidence the controls produce. LLM data security is the narrower model-interaction layer, the prompt and response handling covered in LLM data security. A complete program needs the wider framing because RAG context and tool-call arguments leave the org without any prompt being typed.

Can network DLP protect data going to AI providers?

Only at the destination level. Network DLP and egress filtering read the TLS handshake and the provider hostname, so they can allow or block a destination. They cannot read the request body inside the encrypted session, which means they cannot tell whether a permitted call to api.openai.com carries a customer PII export or a harmless question. Reading the payload requires terminating TLS on the AI request path, which is the difference explained in AI DLP vs traditional DLP.

What data classes should an AI data security policy cover?

At minimum: prompt text, uploaded files, retrieval (RAG) context, tool-call arguments, and model responses. Each is a separate surface. A policy that scans only the prompt string will pass the same regulated data through as a file attachment or a function-call argument. The policy rule should scan every inbound surface and key its decision on both the detected data class and the destination provider, so PHI never reaches a provider without a Business Associate Agreement in place.

What evidence does a regulator expect for AI data handling?

A per-decision record that names the identity, the data classes detected, the surfaces scanned, the destination model, the policy version in force, and the action taken. EU AI Act Article 12 frames this as automatic event recording over a high-risk system's lifetime, with the input data and the persons involved identifiable. Application logs that record only the request timestamp and status code leave that obligation unmet. The field-level breakdown is in what to log for AI compliance.

AI Data Security: The Five Data Classes That Leave Your Org Through AI Traffic

When a data security program tries to account for AI, it usually starts by asking where the data is stored. That is the wrong first question for AI traffic, because the data at risk in an AI call is in motion, and it moves in five distinct shapes: the prompt text a user types, the files they attach, the retrieval context a RAG pipeline pulls from internal stores, the arguments an agent passes to a tool call, and the model's response coming back. Every one of those is a payload carrying business data to an external inference endpoint. Cloud Radix found that 77% of employees using unauthorized AI admit to pasting sensitive business data into unsanctioned models (Cloud Radix, 2026). That number is only the prompt class. The other four rarely appear in any inventory at all.

I want to walk through each of the five classes, show where they are actually governable, give the policy rule and the audit record a platform owner should be able to produce on demand, and then be precise about the three data problems this control layer does not touch.

The five data classes in an AI request

A single chat completion or agent step can carry all five at once. The prompt is the visible one: a support engineer pastes a customer's account export to ask for a summary. The uploaded file is the attachment riding alongside it, a PDF contract or a spreadsheet of transactions, base64-encoded into the same request body. RAG context is the class most teams miss, because it is assembled server-side: a retrieval step pulls passages from an internal wiki or a vector store and injects them into the prompt before the model ever sees it, so proprietary data leaves the building without any human pasting it. Tool-call arguments are the agentic class, where a model decides to call get_customer_record(ssn="...") and the arguments themselves become sensitive payload. The model response is the fifth, and it is bidirectional risk, because a response can echo back PII from the context or return regulated content the requesting identity was never authorized to see.

The narrower mechanics of protecting the model interaction itself, prompt and response handling at the token level, I covered in LLM data security. This piece stays at the program altitude: which classes exist, where you can put a control on them, and what evidence the control has to leave behind.

Where AI data is governable

All five classes converge on one place. They ride inside the HTTP request body to the provider API, encrypted in a TLS session to a host like api.openai.com or bedrock-runtime.us-east-1.amazonaws.com. A network DLP appliance sees the TLS handshake and the destination hostname, and nothing inside the tunnel. That is the structural limit I described in AI egress control: allowlisting the destination proves where the traffic went and reveals nothing about what left inside it.

The only point where the payload is readable is on the decrypted HTTP path, after TLS terminates and before the request reaches the provider. Put a control there and every class becomes inspectable in the same pass: the prompt string, the decoded file attachment, the injected RAG passages, the JSON tool-call arguments, and the streamed response. This is also the distinction between traditional content DLP and the AI-specific version, which I laid out in AI DLP vs traditional DLP. Traditional DLP was built for email and file shares, not for a JSON body where a model's function call carries a Social Security number as a string argument.

A data-class policy rule

Governance at this layer means binding a data class to an identity and an action. The rule below classifies request content, then decides pass, redact, or block based on who is sending it and where it is going. It reads as a single evaluated unit per request:

Two details carry the weight. The scan list has to name all four inbound classes, because a rule that only reads the prompt string will pass a spreadsheet of the same PII straight through as an attachment. And the PHI branch keys on the destination, because sending protected health information to a provider without a Business Associate Agreement is the exact gap Cloud Radix measured when it reported that 57% of healthcare professionals use unauthorized AI to process PHI without a BAA. A defensible program does not treat all approved providers as one bucket.

The audit record auditors read

A data security program is only as good as the evidence it can produce when a regulator asks who sent what to which model. The EU AI Act makes this explicit: Article 12 requires automatic recording of events over the lifetime of a high-risk system, including the input data and identification of the persons involved, and the high-risk obligations take effect on August 2, 2026 (European Commission; record-keeping detail). A per-decision record that satisfies that reading looks like this:

The record names the identity, the classes it detected, the surfaces it scanned, the exact policy version in force at that instant, and the action taken. That combination is what turns a claim ("we govern AI data") into evidence. The full field-level treatment of that obligation is in what to log for AI compliance. My opinion, and a committee would soften it: if your logging cannot name the policy version that decided a specific request, you do not have an audit trail, you have application logs wearing a compliance label.

What this layer does not cover

A request-path control governs data in motion through AI traffic. Three adjacent data problems sit outside it, and pretending otherwise is how programs develop blind spots.

Data at rest is the first. Once a provider receives a request, retention and storage of that data live under the provider's contract and your own vector store encryption, which a gateway on the wire does not manage. Training-set governance is the second: whether a model was trained on scraped or licensed data, and whether your prompts feed a future training run, is a procurement and contract question that no inline inspection point can answer. Endpoint DLP on local files is the third, because a file that never becomes part of an HTTP AI request, copied to a USB drive or an unmanaged laptop, never crosses the request path at all. These need data-at-rest encryption, vendor data-processing agreements, and endpoint tooling respectively. The NIST AI Risk Management Framework is the reference for stitching those adjacent controls into one program rather than assuming any single layer covers the field.

DeepInspect

This is the layer DeepInspect operates on. It is a stateless proxy on the AI request path that terminates the AI call, so it reads the decrypted HTTP payload rather than the encrypted tunnel, and it inspects all five data classes in the same pass: the prompt, the decoded attachments, the injected RAG context, the tool-call arguments, and the response.

Each request is bound to the identity the application supplies, classified across PII, PHI, MNPI, source code, and jurisdictional categories, and resolved to a pass, redact, or block decision before it reaches the provider. Every decision writes a signed record in the shape shown above, so the question "which identity sent which data class to which model, under which policy version" has an answer for any request going back through the retention window. DeepInspect governs data in motion through AI traffic and leaves the data-at-rest, training-set, and endpoint problems to the controls built for them. Book a demo today.