Data Report · RedactSure Research
Chris Sowa · Founder, RedactSure
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How Do You Choose a Data Protection Vendor for AI Agents?
Choose a data protection vendor for AI agents by where it protects the data, whether it detects sensitive data or keeps the model from receiving it, who approves an action and what record it leaves. Put those questions to each vendor in writing, then run one workflow with test values and compare what the model received.
A security lead with a shortlist of data protection vendors for AI agents has a sorting problem before a buying problem. Every product page on the list says it protects sensitive data from AI, and every one of them is telling the truth about a different place: the warehouse column, the pipeline, a vault, the model call, the tool call, the browser or the device. The agent the organization wants to run reads a customer account screen in a CRM it bought rather than built, with a name, an account number, a date of birth and a card on file, and proposes a change of payment details. A control that never sees that screen cannot protect the data on it. The method is to sort vendors by where they protect the data, ask whether each one detects sensitive data after the model has it or keeps the model from receiving it, ask who approves the payment change and what record of what the model received remains, and then settle the shortlist with one trial of that task on test values. The threat and gateway products on the same shortlist answer the same four questions, and the method names which criteria they meet that a data protection vendor does not.
Key findings
- A control that replaces sensitive values with consistent tokens before any model reads the screen changes what the model receives whether or not a detection rule fires, and the real value resolves only at the approved destination at the moment of an approved action (What Is Render-Layer Tokenization?).
- A named person who approves each payment, submission or record change, on a record of what the model saw, matches the duty on deployers of high-risk systems to "assign human oversight to natural persons who have the necessary competence, training and authority" (European Commission AI Act Service Desk, n.d.).
- None of the six vendor pages reviewed states where its control sits relative to a model reading a rendered application screen; Nightfall AI, for example, places its gateway where "Every tool call flows through the gateway," which is the tool call path and not the page the agent reads (Nightfall AI, 2026).
- OWASP states that "Indirect prompt injections occur when an LLM accepts input from external sources, such as websites or files," which is why the first sorting question is where a control sits on the path the agent reads (OWASP, 2025).
- FINRA's 2026 oversight report names "AI agents acting autonomously without human validation and approval" and "Agents may act beyond the user's actual or intended scope and authority," and records that "Complicated, multi-step agent reasoning tasks can make outcomes difficult to trace or explain, complicating auditability," the two facts behind the approval and record questions (FINRA, 2025).
Where does each data protection vendor protect the data?
Draw the path of the task in the opening paragraph, an agent changing payment details on a customer account record, before reading any product page. That path crosses the browser, the rendered screen, the model call, perhaps a tool call, and the identity the agent used, and each vendor on a shortlist protects the data at one of those points. OWASP's mitigation for prompt injection, "Restrict the model's access privileges to the minimum necessary for its intended operations," is a rule every category applies somewhere, and the sorting question is where (OWASP, 2025). Of 86 AI security and data protection products reviewed, 58 control where data can go and 1 describes masking what an agent reads on pages, so the placement question narrows the field before any trial (How Many AI Security Products Control What an AI Agent Sees?).
The seven control points below are the places a vendor can protect the data and the groups the comparison pages fall into. The table states what each vendor's own documentation governs and what a buyer verifies before placing it on the shortlist.
| Control point | What it governs, per vendor documentation | What a buyer should verify |
|---|---|---|
| Model gateway | Cloudflare states that "DLP inspects the text content of request and response bodies as they pass through AI Gateway," so the control covers the model call and nothing that never becomes one (Cloudflare, 2026) | Which agents route every model call through it, and what the agent read on the screen before the call |
| API path, tool calls and MCP | Nightfall AI's MCP gateway is placed to "Intercept data before agents process it," a control defined on traffic to and from tools rather than on a page (Nightfall AI, 2026) | Which agents use MCP, and what the agent reads outside a tool call |
| Browser | Akamai Workforce Protector "installs as an extension on the browsers your organization already runs" and governs "Text input, copy/paste, uploads, downloads, and data exchanges," which are actions that leave the browser (Akamai, 2026) | Whether a model reading a permitted page is shown the values the page renders |
| Endpoint and agent runtime | Controls in this group act on desktop and coding agents on employee devices and inside the agent's run; the pages for Zenity, Straiker and CrowdStrike cover it, starting with RedactSure vs Zenity: 2026 Comparison & Alternative | Which devices and runtimes are enrolled, and whether an agent on an unenrolled device is covered |
| Identity | Okta "manages the registration, configuration, and lifecycle of AI agents," and the page states nothing about what the agent's model reads once connected (Okta, 2026) | Whether registering the agent changes any value the model is shown |
| Data store | Snowflake's IS_AGENT_ACTIVATED "Returns the VARCHAR value 'TRUE' if an agent is active in the current execution context," so a masking policy can act on an agent's query to a column, and the control stops at that column (Snowflake, 2026) | Whether the same record, rendered by a purchased application, is masked the same way |
| Rendered screen | A control class that replaces values with consistent tokens before any model reads the page and resolves real values only at the approved destination; it does not govern API traffic, MCP, terminals, the IDE or local files, which the gateway and endpoint groups do | What the model received for one screen of the trial, in the vendor's own export |
A model gateway and an MCP gateway govern the call and the tool traffic, so a buyer whose agents reach the model only through those paths can start there (RedactSure vs AI Gateways: 2026 Comparison & Alternative). An agent that works a purchased application through its screen crosses the browser and the rendered page, and the last row is the only one on that path. The written question for this criterion is "Name your control point, and list which of our applications and agent paths cross it." The trial step is to mark each vendor's answer on the drawn path of the account record task and see which points on it are left bare.
Does the vendor detect sensitive data, or change what the model receives?
The second sort splits every category in two. A detection control acts on what a rule recognizes: a pattern, a classifier score, a label on a column or a file. It allows, warns, blocks or redacts after the recognition, and a value the rule misses passes as if it were harmless. An isolating control changes what the model receives whether or not any rule fires, because the substitution happens before the model reads anything.
Two record types have the difference written into the rule. For protected health information, a covered entity "must make reasonable efforts to limit protected health information to the minimum necessary to accomplish the intended purpose of the use, disclosure, or request," a limit on what reaches a reader rather than a duty to notice when too much did (Cornell Law School, 2026). For the card on file in the account record, PCI DSS states that the primary account number "must be rendered unreadable according to PCI DSS Requirement 3.5.1," a statement about the form of the value wherever it is held (PCI Security Standards Council, 2024). What a buyer can establish in a trial is which values the model held.
Snowflake's agent masking shows the isolating form on one path: "When an agent-initiated query accesses the ssn column, only the last four digits are visible," while "Direct user queries see the full value," and the control stops at the column, so the same customer rendered on the CRM screen is outside it (Snowflake, 2026). The written question is "For one screen of our workflow, show us what the model received: real values, masks or tokens." In the trial, a vendor that detects will show the value where its rule did not fire; a vendor that isolates will show a mask or a token in every place the model touched.
Who approves an agent's action, and what record exists afterward?
The third and fourth questions are the ones a regulator asks first after an incident, and the documentation record shows why. FINRA's 2026 oversight report records the concern that "Complicated, multi-step agent reasoning tasks can make outcomes difficult to trace or explain, complicating auditability" (FINRA, 2025). Under the EU AI Act, a deployer of a high-risk system keeps the logs it generates "of at least six months" (European Commission AI Act Service Desk, n.d.). Neither text names a product, and both name an outcome a buyer can test: a person with authority approved the consequential step, and a record of the run exists that someone could reconstruct later.
Vendors answer the approval question at their own control point. Silverfort documents that its gateway can "Require approvals before agents execute sensitive tool actions (human in the loop)" and "Map agent actions to responsible humans for compliance and investigations," an approval and an attribution placed on the tool call, and the page does not address an agent that works an application in a browser (Silverfort, 2026).
On the account record, the consequential step is the change of payment details. The written question is "Who approves a payment, a submission or a record change, by name, and what does that person see before approving?" Its companion is "Export the record of one run: what the model saw, what it did, who approved, and where the record is kept." An approval that shows the approver only a summary written by the model being approved is weaker than one that shows the screen, and a record of tool traffic cannot answer what the model saw.
What do the vendors themselves say?
Cloudflare states that "AI Gateway can scan both incoming prompts and outgoing AI responses for sensitive information," so a value the agent read on a page and never sent is outside it (Cloudflare, 2026). Akamai Workforce Protector applies the rule that "an autonomous agent acting in the browser is governed by the same policies as a person," policies on typing, paste, upload and download rather than on what a model is shown on a page (Akamai, 2026). Okta documents that "Apps are linked to AI agents for human-access accountability," an identity control that states nothing about what the agent's model reads once connected (Okta, 2026) (RedactSure vs Okta: 2026 Comparison & Alternative). Snowflake's masking, read above, acts on a query against a column; the data platform comparison is RedactSure vs Snowflake: 2026 Comparison & Alternative.
A data store or pipeline vendor governs the table, the column, the file and the estate; the BigID, Cyera, Varonis, Purview, Wiz, Databricks and Skyflow pages sit in that group with Snowflake. The same record appears on the screen of a purchased application after every data-layer control has finished, so that group answers the estate and leaves the screen question open.
A data protection vendor leaves criteria that other categories meet, and the last sorting rule names which: API traffic, MCP tool calls, terminals, the IDE and local files belong to model gateways, MCP gateways and endpoint controls; agent registration, lifecycle and token scopes to identity providers; discovery, classification and column masking to data platforms and posture vendors; a person's own copy, paste, upload and download on a managed device to enterprise browsers; and filters on model output to AI firewalls.
What should you ask each vendor in writing, and how do you run the trial?
Put five questions to every vendor in writing.
- Name your control point, and list which of our applications and agent paths cross it.
- For one screen of our workflow, show us what the model received: real values, masks or tokens.
- Who approves a payment, a submission or a record change, by name, and what does that person see before approving?
- Export the record of one run: what the model saw, what it did, who approved, and where the record is kept.
- Which of our purchased applications are covered on the first day, without code changes, connectors or vendor integration?
The fifth question is the one most often skipped. Nightfall AI's MCP gateway starts when "Developers add a single line to their MCP configuration pointing to the gateway," so coverage begins with a configuration change on each agent and does not extend to an application the agent works through its screen (Nightfall AI, 2026) (RedactSure vs Nightfall AI: 2026 Comparison & Alternative).
First, seed the sandbox copy of the account record with the four test values and write them down. Second, run the agent through the task up to the proposed payment change, in a purchased application for which the vendor has no connector. Third, search the model's context, the prompt log and the vendor's logs for each value. Fourth, note who was asked to approve the payment change, through what, and what the approver saw. Fifth, request the export of that run and check that it names the approver and shows the values as the model received them. The twenty questions of a full security review are on the checklist page; the trial is the part a buyer can run without the vendor's help (What Should an AI Agent Security Review Cover?).
What does a governed environment change?
A governed environment is a control class on the rendered screen, and it answers the four questions in a fixed way. Values chosen by policy are replaced with consistent tokens before any model reads the screen, so the agent working the account record is shown a token for the name, a token for the account number and a token for the card, and the same token for the same value on every screen of the run. Real values are released only at the approved destination at the moment of an approved action, which for this record is the payment field in the CRM at the moment a named person approves the change. A named person approves every payment, submission and record change, and sees the screen rather than a summary before approving. The organization keeps one exported record of every screen as the model received it, every action and every approval with a name.
Its scope is the rendered screen the agent reads. It does not govern API traffic, terminals, the IDE, local files or MCP. The browser group sits on the adjacent point of the same path: the browser extension governs what a person or an agent types, pastes, uploads and downloads, and the render-layer control governs what the model is shown on the page before any of that happens (RedactSure vs Akamai Workforce Protector: 2026 Comparison & Alternative).
For the buyer, the second written question becomes a measurement: with a render-layer control the answer is the token list, and the export shows it (What Is Render-Layer Tokenization?).
What the record shows
What the model receives on the screen, who approves the consequential action and what record remains are the three facts a data protection choice for AI agents turns on, and the six vendor pages read for this page each protect the data at one point only: the model call, the tool call, the browser's outbound actions, the agent's identity or the warehouse column. None describes the rendered screen of a purchased application, which is the point the account record task crosses last and the one the regulator texts on approval and reconstruction point at. A shortlist sorted by where each vendor protects the data therefore has fewer surprises than one sorted by threat or by feature list, and the trial decides the rest. Seed a sandbox copy of an account record with invented test values, run the payment change through each vendor's control, then compare the exports for the three facts above.
Methodology and limitations
This page rests on vendor documentation for six products, on standards and regulatory texts (OWASP, the HIPAA minimum necessary standard, PCI DSS v4.0.1, Article 26 of the EU AI Act and FINRA's 2026 annual regulatory oversight report), and on RedactSure research pages cited inline. Vendor pages were read between October 21, 2026 and October 23, 2026, and each Sources line carries the date for its page.
The products on this page are described from their public documentation, and none was tested for this page. "Not described" means the pages reviewed, and vendor features change, so the page carries its date. RedactSure is deploying now with enterprise and government customers. The account record is a record type and describes no organization. Whether a control satisfies any of those texts is a determination for counsel, assessors and the organization's own security and compliance officers, and nothing on this page replaces a pilot of one workflow on the organization's own screens.
Frequently asked questions
How do you choose a data protection company for AI?
By where it protects the data first. Sort each vendor by the point it protects, the data store, the pipeline, a vault, the model call, the tool call, the browser or the rendered screen, then ask whether it detects sensitive data or keeps the model from receiving it, who approves a consequential action and what record remains. Put those questions in writing to every vendor, run one workflow with test values and compare where the values turned up. The shortlist that survives the trial is the one to negotiate with.
Does a data protection company for AI cover the screen the agent reads?
Not from the pages reviewed. A data protection company for AI governs the table, the column, the file and the pipeline through discovery, classification, masking at the query and vault storage, and the pages reviewed show those controls stopping at the store they sit on. An agent that works a purchased application reads the same record after they have finished. Choose a data-layer vendor for the estate, and ask separately which control governs what the model reads on the screen.
Is a data protection vendor for AI agents chosen by where data is stored, or by what the model receives?
By what the model receives, in the end. A data protection vendor is usually sorted by where the data is stored, the table, the column, the file or the vault, and a threat-side product by the attack it blocks, but the same four questions resolve both: the control point, detection or isolation, the approver and the record. A buyer who sends the same questions to every vendor on either list gets answers that can be compared.
What should the model receive from a screen during the trial?
A token or a mask for every value the policy covers, and nothing else. Seed the sandbox copy of the account record with the invented name, account number and test-format card, run the task, and search the model's context, the prompt log and the vendor's logs for each value. A detection control shows the value wherever its rule did not fire; an isolating control shows the substitute in every place the model touched.
Who should approve an agent's payment change, and what record should exist?
A named person with the authority to make that change, who sees the screen before approving and is recorded as the approver. FINRA's 2026 oversight report and Article 26 of the EU AI Act point at the same two facts: a person approved the action, and the run can be reconstructed afterward. The export a buyer requests after the trial should show what the model saw, what it did, who approved and where the record is kept, readable without the vendor's help.
Can one vendor cover every control point, including the screen the agent reads?
Not from the pages reviewed. Each vendor documents a control on the path it sits on, and none describes the rendered screen of a purchased application alongside the model call, the tool call, the identity and the column. Most enterprises need more than one group, and the sorting method shows which gap each one closes: a gateway for the call, an identity provider for registration and tokens, a data platform for the estate, and a render-layer control for the screen the agent reads.
How RedactSure Covers the Rendered-Screen Criteria in a Vendor Evaluation
Each vendor reviewed stops at its own path, which leaves the rendered screen of a purchased application ungoverned. Cloudflare states that "DLP inspects the text content of request and response bodies as they pass through AI Gateway" (Cloudflare, 2026).
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before, the agent reading the account screen received the name, account number and card on file for the whole run. Inside the governed environment it reads CUST_001, ACCT_001 and CARD_001, a named person approves the payment change with the screen in view, the card value resolves only in the payment field at that moment, and screen, action and approval land in the AI Control Record.
RedactSure is deploying now with enterprise and government customers.
Everyone else controls where data can go. RedactSure controls what the AI can see.
See it on your own workflow
Which category covers the screen your agent reads is cheaper to settle before a shortlist is signed than after. Bring one task from your workflow and see, with test values, the page as the model receives it, the approval and the record. Book a demo