AI Agent DLP Across the Data Path: 4 Tools Plus Action Control

Sensitive data does not leave an AI-agent workflow through one channel. It can surface in a prompt, retrieval result, generated file, tool argument, network request, trace, alert, or human review screen.

Four DLP and AI-data controls below cover different points on that path. AgentGuard is evaluated separately for a bounded local side-effect decision and is not included in the four-tool count.

Follow the data path

Register a synthetic marker, then follow each representation it takes from source to destination. Write the expected response at the prompt, retrieval, tool, file, network, and evidence-store boundaries before running the trial.

*Test the source, transformation, enforcement point, destination, and evidence store. Claims remain tied to the listed evidence sources.*

Move distinct benign markers through allowed and denied paths, then search every trace, alert, ticket, webhook, and export. Compare classification continuity, response, destination, copied evidence, retention, and owner.

Because the exact US/en query returned no organic results, this guide does not infer a market shortlist from rankings. Start with the data path instead: identify where sensitive data is discovered, retrieved, placed into context, transformed, written, displayed, logged, and transmitted. Each step may be owned by a different control and evidence source.

Classification must survive transformation. Test structured fields, free text, code blocks, attachments, encoded values, summaries, and tool arguments with synthetic markers. A product may detect a source record while missing a paraphrased output, or inspect prompts while missing a file write. Record the exact representation and channel rather than treating AI DLP as one inspection point.

Use enterprise AI agent security practices to assign each data-path control and exception owner. The DLP comparison still rests on where every synthetic marker is observed or stopped.

What agent DLP must decide

An agent can retrieve a classified value, summarize it, place it in a prompt, write it to a file, pass it as a tool argument, or transmit it. Retain the marker class, source, transformation, channel, detector, response, destination, evidence copy, and retention state for each path.

Use the NIST Privacy Framework to define relevant risk language, then translate it into an expected product behavior. NIST does not show whether a product observes a transformed marker in this agent route or avoids copying it into a broader alert store.

For agent DLP, separate discovery, classification, warning, redaction, prevention, and post-event reporting. State which observer produced the result, whether transmission occurred, and who can inspect the decision without exposing the full marker.

The decision record should contain the synthetic data class, detector or policy, source, destination, agent and user identity, application, channel, transformation, decision, exception, and result. Separate discovery from prevention: a report that identifies exposure after transmission does not prove an inline deny.

Also define retention and redaction boundaries. Prompts, traces, evaluation datasets, support exports, and security logs can become new copies of sensitive data even when the original action is blocked. Ask what is stored, where it is stored, who can retrieve it, how long it remains, and whether a redacted event still supports investigation.

Run the synthetic markers through the evidence systems themselves. Search the DLP console, trace store, alert payload, ticket, webhook, and downloadable report for the original and transformed values. Verify that authorized reviewers can identify the policy match without receiving the protected content. Then test deletion and retention behavior. This catches a common coverage inversion: the product prevents transmission to the intended destination but copies the same value into a broader operational store.

Test one bypass explicitly: an alternate application, transformed value, attachment, local write, or unobserved tool destination. Mark it Unknown, detection-only, governed elsewhere, or an open route with a named enforcement and evidence owner.

Four DLP tools and one adjacent action control

Microsoft Purview, Nightfall, Prompt Security, and Lakera are the four DLP or AI-data controls counted here. AgentGuard appears afterward as an adjacent local action control, not an enterprise DLP replacement. AgentGuard publishes this page; every profile follows the same first-party evidence rule, and the marker ledger determines actual path coverage.

Microsoft Purview DLP

Microsoft Purview DLP documents enterprise data security and DLP across Microsoft environments. Verify agent surfaces and enforcement locations in your tenant.

For Microsoft Purview, map the tenant's existing labels and DLP policies to the exact agent surfaces in scope. Place synthetic markers in Microsoft-hosted and external paths, then verify which channel discovers, warns, blocks, or only reports. Do not infer agent coverage from broad information-protection scope.

Nightfall

Nightfall documents cloud-native data security and DLP for SaaS and AI workflows. Test the exact channels and data classes in scope.

For Nightfall, enumerate every SaaS, API, and AI workflow that could carry the synthetic markers. Test both native integration and any gateway or developer path under consideration. Preserve the detector, matched evidence, redaction or block, and the destination's observed content.

Prompt Security

Prompt Security documents controls for enterprise generative-AI use and data exposure. Verify application coverage and response actions.

For Prompt Security, test approved and unsanctioned generative-AI application routes with the same fake identifier. Confirm whether the product sees user input, model output, browser or application context, and downstream actions as separate stages. Ask how policy follows an agent that changes tools or destinations.

Lakera

Lakera documents runtime safeguards for AI application inputs and outputs. Test sensitive-data handling alongside prompt attacks.

For Lakera, send synthetic sensitive content through direct input and an indirect retrieved document, then transform it in a model response. Verify the input and output evidence, decision timing, and what happens when the application turns the response into a tool argument or file operation.

AgentGuard

AgentGuard documents local policy near a developer agent's risky action. Use it to stop a bounded side effect, not as a full enterprise data-classification system.

For AgentGuard, begin where sensitive context becomes a bounded local action. Ask a supported developer agent to write a synthetic marker outside the approved location or send it through a controlled tool route. Verify policy context, requested target, approval or denial, and result without presenting AgentGuard as the enterprise classifier.

The prompt injection glossary can define one benign prompt-risk scenario. The synthetic-marker ledger, not that glossary, establishes which data paths each product observes or controls.

Run a synthetic-data exfiltration test

Create unique synthetic markers for three data classes: a customer-like identifier, an internal secret pattern, and a code or document fragment. The markers must be harmless, searchable, and absent from production systems. Record where each marker enters the workflow and every store allowed to retain it.

Send the markers through separate paths rather than one blended prompt. Test direct user input, retrieved context, an uploaded file, generated code, a tool argument, a local file write, and a controlled outbound destination where relevant. Predict whether the product should discover, redact, warn, require approval, block, or log each path.

Inspect transformations. Encode a marker, split it across fields, place it in structured JSON, and let the agent summarize it. The objective is not to evade the product with an adversarial contest; it is to learn which transformations preserve classification and which create an owned gap.

Search the evidence systems after the run. The DLP console, trace store, alert payload, ticket, webhook, and downloadable report should not copy the full synthetic value into a wider audience. Test deletion and retention for those operational records as well as the original destination.

Use the OWASP sensitive information disclosure guidance to shape the risk review. It does not prove any product observes a specific agent route.

Finish with a path ledger: source, transformation, decision point, response, destination, evidence store, retention owner, and unobserved branch. Keep unsupported or untested paths explicit.

Use a path ledger that keeps observation and enforcement separate:

Create different markers for different sources. One can represent a retrieval result, another a tool response, and a third a generated file. This shows whether the product can preserve origin and policy context instead of treating every matching string as the same incident. Keep the values synthetic and register who may view them during the trial.

Run allowed and denied controls alongside the exfiltration path. An allowed workflow should reach its approved destination without excessive redaction, while the same marker sent to the reserved external target should trigger the expected response. False positives and false negatives need their own records; do not collapse them into a single detection rate from a handful of events.

Test encoding and transformation only within the authorized lab. Place the benign marker in JSON, a generated text file, and a tool argument, then observe whether normalization preserves the policy decision. The goal is to find ordinary workflow gaps, not to develop bypass techniques. Stop if the test would require real secrets, uncontrolled destinations, or evasion outside the documented scope.

Review alert access with the same care as the initial path. Determine which roles can open raw payloads, whether redacted views are available, how long evidence is retained, and what is included in exports or webhooks. A control that blocks the agent but sends the full value to a ticketing system has moved the exposure rather than contained it.

Map every path to one enforcement owner and one evidence owner. Those may be different systems. Mark routes with no observer as Unknown, routes observed but not enforceable as detection-only, and routes blocked without content context as policy enforcement. This prevents a broad DLP label from hiding material coverage gaps.

Test one synthetic data path after the markers and expected response for each route are fixed.

Where AgentGuard can stop a side effect

AgentGuard is relevant when a sensitive-data event becomes a documented local action, such as writing a file, invoking a tool, or sending data through an observed route. It is not documented as the system that discovers, classifies, retains, and governs enterprise data across SaaS and cloud estates.

Use one synthetic marker in a supported developer-agent workflow. Attempt an allowed local operation and a policy-relevant outbound or file action. Preserve the marker class without copying the full value into broad logs, then verify the requested action, decision, and result.

Inspect the AgentGuard action record for that bounded side effect. The trial should state which prompt, retrieval, remote application, and trace paths remain outside the local control.

Pair AgentGuard with a DLP platform only when the path ledger shows complementary enforcement. Avoid claiming full DLP coverage from one blocked action.

The LLM agent exploit vectors review can help select a harmless action route. It is test design context, not product evidence.