Stop sensitive data from leaving the business through AI prompts.
PromptWall gives enterprises an AI DLP layer built for how employees really use LLMs. It detects PII, credentials, regulated data, and proprietary content across browser AI tools, copilots, and API workflows before the request reaches the model provider.
The problem
Employees are already sending regulated and proprietary data to AI tools.
Browser-based AI assistants, coding copilots, and one-off API calls have created a new outbound channel that many legacy DLP controls do not see clearly. Teams paste support transcripts, customer records, financial data, code fragments, tickets, contracts, and internal strategy notes into LLMs because it helps them work faster.
The enterprise risk is not theoretical. It is operational: sensitive data leaves the organization one prompt at a time, without consistent policy, evidence, or buyer-grade accountability.
The PromptWall answer
One AI DLP layer across every sanctioned AI surface.
PromptWall inspects prompts at the application layer, where generative AI risk actually exists. Instead of trying to infer what happened from allowed HTTPS traffic, PromptWall evaluates the real content being sent and enforces policy before that content reaches the provider.
That gives enterprise buyers a cleaner control model: governed AI usage instead of blanket bans, plus an evidence trail that security, privacy, and compliance teams can rely on.
Why enterprise buyers replace legacy thinking with AI DLP
Enterprise buyers do not buy AI DLP because they want another log stream. They buy it because traditional controls were not designed for conversational, browser-first, prompt-based exfiltration. PromptWall closes that gap with purpose-built controls for AI-native data flow.
Prompt content is now a business-critical outbound channel.
Once AI becomes part of daily work, your highest-value data no longer moves only through email, uploads, and endpoint sync. It also moves through LLM prompts. That means the organization needs detection and enforcement at the prompt layer, not just at the file or packet layer.
“Block all AI” is not a durable strategy.
Most enterprises eventually discover that prohibition drives shadow usage rather than governance. AI DLP gives buyers a better path: enable useful AI workflows while masking or stopping the specific data types that should not leave the organization.
Evidence matters as much as prevention.
CISOs and privacy teams need more than a blocked event count. They need to know what was attempted, why it was flagged, what was masked, and what reached the provider. That is why PromptWall couples AI DLP with inspection-grade logging and governance workflows.
See PromptWall AI DLP in a real buyer workflow
Watch how PromptWall detects regulated data, masks what can be safely transformed, and blocks what should never leave the company.
AI DLP architecture for enterprise buyers
PromptWall is built around an inspection pipeline that protects productivity and governance at the same time.
Step 1
Intercept the prompt before provider dispatch
PromptWall inserts inspection at the point where employees actually use AI: browser sessions, editor assistants, and governed API lanes.
Step 2
Classify sensitive content in context
Entity recognition, pattern detection, and semantic similarity analysis work together so protection is not limited to simple regex patterns.
Step 3
Apply data handling policy
The policy engine decides whether to allow, flag, mask, or block based on risk score, entity type, provider, and route.
Step 4
Log the exact outcome
Security and compliance teams get an inspection record that shows what was attempted, what was changed, and what reached the model.
Key capabilities buyers expect from a serious AI DLP platform
PII and regulated data detection
Detect names, emails, phone numbers, account identifiers, health data, and custom enterprise entities before dispatch.
PII masking for LLMs →Document leak detection
Compare prompts against protected corpora to stop teams from pasting contracts, source code, and internal strategy docs into public models.
document leak detection →Credential and secret protection
Catch API keys, tokens, connection strings, and infrastructure secrets before they become an AI data-loss event.
sensitive data in AI prompts →Channel-wide coverage
Apply one AI DLP policy across browser AI tools, Copilot-style editor flows, and sanctioned API usage.
DLP for Copilot and ChatGPT →Mask, block, or allow with conditions
Choose reversible masking, hard blocking, or review-driven flagging based on data type, confidence, and business context.
AI usage policy enforcement →Inspection-grade evidence
Keep original versus sanitized prompt context, triggered detections, and final actions for compliance and incident response.
AI audit trail →Use cases by industry
Financial services
Protect customer data and regulated records.
Prevent account details, client communications, credit data, and advisory notes from reaching public models while preserving safe research and drafting workflows.
Healthcare
Keep PHI and clinical context governed.
Detect patient identifiers, treatment details, and medical records in prompts before they create a HIPAA incident through casual AI usage.
SaaS and technology
Stop source code and secret leakage.
Protect proprietary code, roadmap details, credentials, and support data across engineering assistants, public chat tools, and internal AI experimentation.
Buying triggers that usually lead to AI DLP evaluation
The organization already knows AI use is happening.
Security teams typically start here: browser AI usage is visible informally, copilots are spreading, and leadership realizes the business has no consistent control on what data is being sent.
Compliance and privacy leaders need proof.
The next trigger is evidentiary. Buyers need a way to demonstrate prompt-level policy enforcement and support investigations when a high-risk interaction occurs.
Regulated data paths
Route AI DLP readers into industry, research, and comparison pages.
AI DLP is often the strongest commercial entry point because it connects prompt leakage to board-level risk. These links move readers from risk awareness into industry-specific and vendor-evaluation pages.
Frequently asked questions
Why is AI DLP different from traditional DLP?+
Traditional DLP was designed for email, file transfer, and endpoint events. AI DLP must understand prompt content itself, including free-form language, pasted documents, credentials, and model-specific workflows. PromptWall inspects prompts before they leave the organization, rather than trying to infer risk from network patterns after the fact.
Can AI DLP reduce data leakage without blocking AI adoption?+
Yes. The value of AI DLP is that it gives security teams more than one answer. PromptWall can block high-risk prompts, mask sensitive entities when safe to do so, or flag interactions for review while still preserving productivity.
What teams typically buy AI DLP first?+
Security, compliance, privacy, and platform teams usually lead the purchase. The common buying trigger is the realization that ChatGPT, Copilot, and other LLM tools are already in use, but the organization lacks prompt-level visibility and control.
What kinds of content should an AI DLP platform catch?+
Enterprise buyers usually expect coverage for customer PII, credentials, internal documents, source code, contractual text, healthcare data, financial data, and custom business identifiers that standard models will not detect on their own.
Explore AI DLP topics
AI Data Leak Prevention
How PromptWall prevents AI-bound data exposure in real time.
PII Masking for LLMs
Entity detection and masking for enterprise prompts.
Document Leak Detection
Protect contracts, source code, and internal documents from AI leakage.
DLP for Copilot and ChatGPT
Apply AI DLP to the tools employees actually use.
AI DLP vs Traditional DLP
See why legacy approaches miss prompt-layer exposure.
Sensitive Data in AI Prompts
Understand the most common enterprise AI data exposure patterns.
AI Data Leakage Cases
See workflow-driven leakage cases that justify AI DLP investment.
AI Data Leakage Report
Use research-style leakage framing to support the business case.