Enterprise security teams have spent more than a decade refining data loss prevention. We built policies around file movement. We tuned alerts around email exfiltration. We monitored endpoints, USB devices, cloud storage, and outbound gateways. The assumption was consistent. Sensitive data moves in recognizable ways, through defined channels, and can be inspected before it leaves the organization.

Large language models break that assumption.

The conversation around enterprise AI risk tends to focus on outputs. Hallucinations. Bias. Toxicity. Model safety. These are real issues, especially in customer facing systems. But inside the enterprise, the more immediate risk sits on the input side. What employees are feeding into these systems matters far more than what the model generates in return.

Traditional DLP was not designed for conversational AI. It was built for structured transfer events. An attachment sent to Gmail. A file uploaded to Dropbox. A database export copied to a USB drive. Each of those events produces artifacts that can be inspected and evaluated against policies.

A prompt box does not look like a file transfer.

When an employee copies a customer record from a CRM and pastes it into a public LLM to draft an email response, most traditional DLP systems never see it. There is no attachment. No file movement. No bulk export. Just text inside a browser session that appears indistinguishable from normal web traffic.

The control surface has shifted from files to conversations

Consider how this plays out in practice. A sales representative is preparing a proposal. They paste last quarter's pricing model into an LLM to generate a revised structure for a new prospect. A support agent copies a detailed ticket history containing personal data into a chat interface to summarize the issue. A developer pastes proprietary source code into a model to refactor a function.

In each case, the intent is productivity. The risk is exposure.

Traditional DLP systems rely heavily on pattern matching and predefined policy triggers. Credit card numbers. Social security numbers. Confidential document classifications. They are effective when sensitive data is moving in bulk or in recognizable formats. They are far less effective when fragments of sensitive information are embedded in natural language prompts.

A prompt is not labeled as confidential. It is contextual. It may contain a few fields of personal data woven into paragraphs of ordinary text. It may include proprietary logic disguised as code snippets. It may reference internal strategy without copying a full document. These are subtle leaks, but at scale they compound.

There is also a visibility problem

Most organizations do not have centralized logging of prompt level activity. If employees are using public LLMs through a browser, that interaction may not be captured in a way that allows inspection of the actual content submitted. Secure web gateways might log domains. They rarely log full conversational payloads. Even when traffic is proxied, privacy and legal considerations often limit deep inspection of user generated text.

As a result, security leaders are left with policy documents instead of controls. They circulate guidelines about not pasting sensitive data into AI tools. They conduct awareness training. They rely on acceptable use policies.

Policies are not enforcement.

There is another structural issue. Traditional DLP assumes a clear boundary between internal and external systems. Data inside the network is trusted. Data leaving the network is scrutinized. LLMs blur that boundary. Many enterprises now run a mix of public APIs, third party AI tools, and internally hosted models. Employees move fluidly between them. The same prompt behavior can target vastly different endpoints with different risk profiles.

Without prompt level inspection and contextual awareness, organizations cannot differentiate between acceptable and risky use.

The blocking approach is short sighted

Some teams respond by blocking access to public LLMs entirely. This approach is understandable but short sighted. Employees will find workarounds. They will use personal devices. They will copy data manually. And in doing so, visibility decreases even further.

The better approach is to recognize that conversational interfaces represent a new data channel.

In the same way that email required dedicated security controls, and cloud storage required a rethink of perimeter assumptions, LLM interaction requires its own layer of governance. This does not mean recreating traditional DLP with a new label. It means acknowledging that intent and context matter more than file signatures.

A prompt contains purpose. It reflects what a user is trying to accomplish. Security controls that operate at this layer can evaluate risk before submission rather than after exfiltration. They can detect when sensitive data categories appear in combination with external model endpoints. They can enforce policy dynamically based on user role, data classification, and model destination.

This is fundamentally different from scanning attachments at a gateway.

Governance requires cross functional alignment

It also requires tighter integration between security, engineering, and compliance teams. AI usage is not confined to a single department. It spans sales, support, product, finance, and legal. Governance cannot be an afterthought bolted onto existing controls. It must be designed into the workflow.

Another overlooked dimension is auditability.

Regulators and internal auditors increasingly expect organizations to demonstrate control over how sensitive data is processed by AI systems. If prompts are invisible, there is no defensible audit trail. When asked how customer data was used in AI tools, many organizations can only point to policy statements. That will not hold up under scrutiny as AI adoption matures.

The shift from documents to conversations as the primary unit of data exchange is significant. Conversations are ephemeral. They are iterative. They evolve over time. A single prompt may seem harmless. A sequence of prompts may reveal far more. Traditional DLP does not track conversational state.

Security leaders should be asking different questions

Not simply whether AI tools are approved, but how data enters them. Not just whether outputs are monitored, but whether inputs are governed. Not only whether models are secure, but whether the organization has visibility into how employees interact with them.

The age of LLMs forces us to rethink where data risk originates.

For years, we focused on preventing large scale exfiltration events. Massive database dumps. Insider threats moving gigabytes of files. That risk still exists. But a quieter, more pervasive form of leakage is emerging. Small fragments of sensitive information distributed across thousands of prompts every day.

Individually, these events may not trigger alarms. Collectively, they represent a meaningful expansion of the organization's exposure surface.

Traditional DLP is not obsolete. It still plays a critical role in protecting structured data flows. But it is incomplete in the context of conversational AI. Treating prompt level interaction as just another web session misses the point.

The prompt box has become a gateway

If enterprises continue to treat it as an unmonitored input field, they will struggle to maintain control over how their data is used in AI systems. The organizations that adapt will be the ones that recognize the prompt layer as a first class control surface and design governance around it intentionally.

The conversation about AI risk is still evolving. Many teams are experimenting. Few have mature controls in place. That creates both opportunity and risk.

The question is not whether employees will use LLMs. They already are.

The real question is whether security teams will adapt their data protection strategies to match how work is actually happening now.