Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs Protegrity: 2026 Comparison & Alternative
Protegrity protects data elements in the stores and flows its protectors and guardrails cover, while RedactSure, a Protegrity alternative built for AI agents, protects the screen before an agent reads it. RedactSure is an alternative way to think about agent browsing security, because it protects sensitive data as it is rendered on a screen, before an agent uses it.
Security teams weighing a Protegrity alternative for AI agents find that both products replace sensitive values with tokens and disagree about where. Protegrity places a protector, a gateway or a guardrail in the path a data element travels. RedactSure replaces the value on the screen the agent is about to read, so the model never receives it.
This page quotes Protegrity's own documentation and follows one record: a borrower record in a loan servicing platform the organization bought rather than built, holding a name, a Social Security number, a loan number and a payment history, opened by an agent that works the platform as a servicing specialist would. What Is Render-Layer Tokenization? defines the approach, and Data Privacy Vault or Screen-Level Tokenization? explains why a data-layer control cannot see that screen.
Key findings
- As a Protegrity alternative for agent work, RedactSure replaces the values on the rendered servicing screen before any model reads them, so the agent works the borrower as BORROWER_001 and the real value is released only at an approved destination (What Is Render-Layer Tokenization?).
- A value a model receives is an exposure wherever it is stored: OWASP lists that "Personal identifiable information (PII) may be disclosed during interactions with the LLM," and a token on the screen before the model reads it keeps the value out of those interactions (OWASP, LLM02, 2025).
- None of Protegrity's pages reviewed describes changing what a rendered screen shows to an agent that reads it; the enforcement points its platform page names are "databases, files, SaaS endpoints, agent context, prompts, responses and tool results" (Protegrity, DAIS, 2026).
- Protegrity protects each element under one central policy, "so protection holds as data moves from databases to prompts to agents," each point named being a store, a prompt or a tool result rather than a screen (Protegrity, DAIS, 2026).
- For AI, Protegrity adds Semantic Guardrails, which "act as a checkpoint between the user (or agent) and the AI system" and "Flag risky interactions, block unsafe outputs, or hand control back to the developer," scoring conversations rather than replacing values (Protegrity, for AI Security, 2026).
How do the two compare side by side?
The table sets Protegrity's own wording beside what a governed environment does on the same point, with the check a buyer can run.
| Dimension | Protegrity, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | "Protegrity protects sensitive data at the data layer" (Protegrity, Faster, 2026) | On the rendered screen, before any model reads it | Where one borrower value is replaced |
| How coverage starts | A protector, gateway or guardrail placed in the data's path | The application renders inside the governed environment; nothing is installed in it | How many of the agent's applications carry a protector |
| What the model receives | Data protected before it reaches systems that do not need raw values | The screen with consistent tokens, BORROWER_001, SSN_001 and LOAN_001, for the whole run | One screen, as the model read it |
| How real values are restored | Policy-based control "over who sees clear vs. protected data" (Protegrity, Cloud Protectors, 2026) | At the approved destination, at the moment of an approved action | What releases the value, and whose name is on it |
| Who approves an action | "Policies decide which tools each agent can use and what data it can send to external services and models" (Protegrity, DAIS, 2026) | A named person approves every payment, submission and record change | A person's name, not a rule |
| The audit record | "Every policy decision produces a record, so auditors see exactly what happened, how and why" (Protegrity, DAIS, 2026) | Every screen as tokens, every action and approval, in the AI Control Record sent to the customer's SIEM | Whether the record shows what the model saw |
Read down the two middle columns and the difference is the object each product governs: Protegrity governs data elements at the points its policies reach, and RedactSure governs what an agent reads. They also differ on who turns a token back into a real value, a policy written in advance on Protegrity's pages or a named person at the moment of the action (What Is Supervised Delegation?).
What does Protegrity protect, and where does it act?
Protegrity's pages describe a platform that acts on individual data elements under central policy. Its AI page says the platform will "De-identify, tokenize, encrypt, or control sensitive data before it reaches systems that do not need raw values," and the cloud protectors page says it will "Enforce field-level data protection natively within cloud platforms (AWS, Azure, GCP) & services like Snowflake, Databricks, BigQuery, and S3" (Protegrity, Faster, 2026; Protegrity, Cloud Protectors, 2026).
Each benefit depends on one condition: a protector, gateway or guardrail placed where the data element is created, stored or passes. The pipeline page names the stages it covers, "at ingest, output, and everywhere in between," and each is a point in a pipeline the team built (Protegrity, Generative, 2026). The screen of a licensed application, rendered from its own database, is not among them, and that is where an agent reaches a borrower record. This is a limit of placement rather than a flaw, and it decides what the model receives from a rendered screen on the first day of agent work.
The same gap holds across the agents reviewed: 0 of 15 describe substituting tokens for the customer data on a working page, and masking or hiding a value does not count because the agent cannot act on it (Do AI Agents Ask Before Making a Payment?).
What does an agent read when it opens a borrower record?
The copy of the borrower record in the data warehouse carries Protegrity tokens, but the agent never goes there; it signs in to the servicing platform and reads the screen it renders from its own database. No protector sits in that path, because protectors are "applied locally by the Application Protector within the app context" of the applications a team deploys them in, and a licensed servicing platform is not one of them (Protegrity, Cloud Protectors, 2026). No guardrail scores the screen either, since Semantic Guardrails works "by analyzing conversations between participants" and a rendered page is not a conversation (Protegrity, Architecture, 2026). The model receives the name, Social Security number, loan number and payment history as the page shows them, every visible field included (What Does an AI Agent See in a Screenshot?).
OWASP notes that "Indirect prompt injections occur when an LLM accepts input from external sources, such as websites or files," so a page the agent reads can carry both the borrower's data and an instruction about what to do with it (OWASP, LLM01, 2025). An agent working under the specialist's session is logged as the specialist, so the platform cannot say which actions were the agent's (What Is an AI Control Record?).
Which control fits which work?
Keep Protegrity for
- Data elements in the stores a team has instrumented, where "Each data element is protected individually, so protection holds as data moves from databases to prompts to agents" (Protegrity, DAIS, 2026).
- Analytics and model training on protected data, where the platform will "Irreversibly transform PII for privacy-preserving analytics and AI model training" (Protegrity, Generative, 2026).
- Copilot deployments, since the platform "protects sensitive data before Copilot retrieves, reasons over, or exposes it" (Protegrity, Copilot, 2026).
Add RedactSure for
- Agents reading screens in applications the organization bought, where the model receives BORROWER_001 from the day the application renders in the governed environment (What Is Render-Layer Tokenization?).
- A named person approving each payment arrangement, submission or record change the agent proposes (What Is Supervised Delegation?).
- The record of what the model received on every screen, the agent's actions separated from the specialist's, in the customer's SIEM (What Is an AI Control Record?).
An organization that runs agents on licensed platforms will usually need both; this block shows which product protects the screen.
How do you test it on one workflow?
One real workflow, one afternoon, both vendors in the room, every answer written down.
Step 1. Pick a workflow the agent will run on the servicing platform, such as setting up a payment arrangement, and choose one screen from it: the borrower record, not a data flow diagram.
Step 2. Have each vendor produce the borrower screen exactly as it reached the model. The output settles the question: real values or tokens, and which visible fields the task never used.
Step 3. List every application the workflow touches and mark each as carrying a protector, routed through a gateway or guardrail, or neither. The third group is the first-day exposure; keep the count in the buyer's notes.
Step 4. Trace one value, the Social Security number, from the token the model received back to the point where the real value appeared: what released it, when, and whose name the record carries.
Step 5. Calculate coverage. If the workflow touches five applications and one carries a protector, first-day coverage is one of five. Pricing is not published by either vendor in a comparable form, so compare the costs that do differ: the instrumentation program, and the agent work that waits for it.
What does a governed environment change?
The agent works under its own session, recorded under its own identity, and sees a different screen from the specialist: values are replaced with consistent tokens before the model reads anything, so the borrower is BORROWER_001 with SSN_001 and LOAN_001 on every screen of the run. For each task the planner proposes what to tokenize, starting from personal information, account numbers, credentials and record numbers.Real values resolve only at approved destinations at the moment of an approved action, a named person approves every payment, submission and record change, and every screen, action and approval is written to a record exported to the organization's SIEM.
OWASP warns that disclosure "can result in unauthorized data access, privacy violations, and intellectual property breaches," and its listed mitigation for prompt injection is to "Restrict the model's access privileges to the minimum necessary for its intended operations" (OWASP, LLM02, 2025; OWASP, LLM01, 2025). A model that only ever received BORROWER_001 cannot be talked into disclosing a number it never held, the principle of What Is Least Exposure?. The deciding layer has to act between the application producing the screen and the model reading it (AI Gateway, DLP or Tokens: What Controls AI Exposure?).
What the record shows
The record this page follows is a borrower record in a loan servicing platform the organization bought rather than built, holding a name, a Social Security number, a loan number and a payment history. RedactSure, as the Protegrity alternative for agent work, protects that record: a render-layer control replaces the name, Social Security number and loan number with tokens on the servicing screen before the model reads it, with the real value released only for the arrangement the specialist approves. Protegrity's real coverage is the data elements its policies reach in the stores, pipelines and indexes a team instrumented; its pages describe no control over what a model reads on a licensed application's screen. Run the test on one workflow: name the borrower screen, have each vendor show it as the model saw it, then follow the Social Security number back to the action that released it and the person who approved it.
Methodology and limitations
This page draws on seven Protegrity pages, two OWASP entries and RedactSure research pages. Vendor pages were read between October 21, 2026 and October 23, 2026, and each Sources line carries the date for its page. Public documentation read in full gives a sound view of where each product applies its control; where this page says a source does not describe something, it refers to the pages reviewed.
Both products are described from their documentation, neither was tested for this page, and no customer of either is described; both products change, and the page carries its date. RedactSure is deploying now with enterprise and government customers. The borrower record is a record type, not an organization. Compliance determinations belong to counsel, assessors and the security team, and nothing here replaces a pilot of one workflow on the organization's own screens.
Frequently asked questions
Does Protegrity protect data that reaches an LLM?
Only where the data passes through a point it protects. Protegrity's pages say policies apply across "agent context, prompts, responses and tool results," and that protection holds "as data moves from databases to prompts to agents." Both describe pipelines and agents built against protected stores. When an agent reads a screen in a licensed application directly, the data reaches the model through none of those points.
Does Protegrity cover what an agent reads on a licensed application's screen?
Not from the pages reviewed. Protegrity's pages place protection at the data layer, in cloud platforms, in a network gateway and in front of a Copilot index; the cloud protectors page shows a SaaS tile with no detail on how third-party tools are protected. A licensed servicing platform renders its screen from its own database, which none of those points touches, so the screen needs a control in front of the model.
Who approves the agent's action, a policy or a person?
A policy, on Protegrity's pages: "Policies decide which tools each agent can use and what data it can send to external services and models," written in advance for a class of data or tool, with dual-principal trust verifying the agent and the person directing it. In RedactSure's design a named person approves each payment arrangement, submission or record change as the agent proposes it, under that person's name.
Will the servicing platform's logs show that an agent acted?
Not on their own. When an agent works under a specialist's session, the platform logs the specialist, and because nothing on that screen passed through a protector, the data platform has nothing to record; Protegrity's record covers policy decisions, "so auditors see exactly what happened, how and why" where a policy governs. A record separating the agent's actions from the person's has to come from the environment the agent worked in.
Is field-level tokenization the same as render-layer tokenization?
No. Field-level tokenization describes what is protected: a column or field in a store, replaced with a token that keeps its format so analytics and downstream systems still work. Render-layer tokenization describes where the substitution happens: on the screen, before a model reads it. A platform can do the first without the second, and the servicing screen is the case where only the second reaches the agent.
Does Semantic Guardrails keep the borrower's data from the model?
No. Semantic Guardrails "act as a checkpoint between the user (or agent) and the AI system" and provide "individual message risk scores and classifications" for a conversation; the models are "trained on synthetic customer-service AI chatbot datasets" and need fine-tuning for domain accuracy. A guardrail flags or blocks a message once the model is in the loop; it does not replace values on a screen, so a borrower record reaches the model before any score exists.
How RedactSure closes the screen gap that data-layer protection leaves open
Data-layer protection reaches the stores and pipelines a team instrumented, so an agent working a licensed loan servicing platform reads the borrower record as displayed. Protegrity's platform page lists the points its policies cover, from databases to prompts and tool results, and a rendered screen is not among them.
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the servicing platform as the specialist, the screen shows the name, Social Security number, loan number and payment history, and the model receives all of it. Inside the governed environment: the same screen shows BORROWER_001, SSN_001 and LOAN_001, and the real Social Security number reaches the payment arrangement only on the named specialist's approval.
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
Protecting the sensitive data an agent could read on a screen is a needed part of any solution that includes Protegrity. Bring one servicing workflow and see, with test values, the borrower screen as the model read it and the approval waiting. Book a demo