Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
Published
Last updated
RedactSure vs Proofpoint: 2026 Comparison & Alternative
Proofpoint inspects prompts, uploads, responses and MCP calls and redacts sensitive data there, while RedactSure, a Proofpoint alternative built for AI agents, tokenizes the screen before the agent's model 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 looking for a Proofpoint alternative for AI agents usually find that the two products inspect different objects. Proofpoint reads the traffic it sits in front of, a prompt typed into an AI tool, an upload, a response or a tool call crossing an MCP gateway, and redacts sensitive data there. RedactSure replaces the values on the screen an agent is about to read with consistent tokens, so the model never receives the real customer name or phone number.
This page quotes Proofpoint's own documentation and uses one example throughout: a customer record in a CRM the organization bought rather than built, holding a name, an email address, a phone number and an account note, opened by an agent that works the CRM screen as a service representative would. What Is Render-Layer Tokenization? defines the approach RedactSure uses.
Key findings
- As a Proofpoint alternative for agent work, RedactSure replaces values on the rendered CRM screen before any model reads it, so the agent works the account as CUST_001 and the real value is released only at an approved destination (What Is Render-Layer Tokenization?).
- Data a model receives is an exposure wherever the traffic control sits; OWASP lists "Personal identifiable information (PII) may be disclosed during interactions with the LLM" as a disclosure risk (OWASP, LLM02, 2025).
- The pages reviewed place the agent on "API connections, MCP, or custom integrations" and do not describe an agent that works a bought application through its rendered screen, where a service agent reads the customer record (Proofpoint, Platform, 2026).
- Proofpoint's agentic product "governs autonomous AI agents by monitoring what they can access, what they are asked to do, and what they actually do" (Proofpoint, Agentic AI Security, 2026).
- Its MCP control is a gateway: "Route all MCP traffic through a central gateway that authenticates via OAuth 2.0, controls user and agent tool access, and inspects for sensitive data and policy violations" (Proofpoint, MCP Security, 2026).
- For people using AI tools, Proofpoint will "Block or redact sensitive data in prompts, uploads, and paste actions to prevent data loss without disrupting productivity" (Proofpoint, AI Data Security, 2026).
How do the two compare side by side?
Each row pairs Proofpoint's own wording with the render-layer design on the same point, then names the check to run on the CRM.
| Dimension | Proofpoint, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | "It brings security controls to the protocol layer where AI agents connect with tools, data, and applications" (Proofpoint, MCP Security, 2026) | On the CRM screen, before any model reads it | Where, for one workflow, a value is replaced or redacted |
| How coverage starts | Traffic routed through the gateway, or prompts and uploads inspected in an AI tool | Inside the governed environment; the CRM is unchanged | Which of the agent's applications send traffic through the gateway today |
| What the model receives | "Inspects requests, responses, tool calls, and data crossing MCP links" (Proofpoint, MCP Security, 2026) | The screen with tokens, CUST_001 and TEL_001, for the whole run | One CRM screen, exactly as it reached the model |
| How real values are restored | "Block or redact requests before they reach servers" (Proofpoint, MCP Security, 2026); restoring a redacted value is not addressed | At an approved destination, when a named person approves the action | Who authorizes the release, and where it is recorded |
| Who approves an action | An intent check: "Flag agent actions that go beyond the original request, even when permissions allow them" (Proofpoint, Agentic AI Security, 2026) | A named person approves every payment, submission and record change | A person's name on the record change, not a score |
| The audit record | "Reconstruct each action from user request to outcome, and keep a defensible audit trail" (Proofpoint, Agentic AI Security, 2026) | Every screen as tokens, every action and every approval, in the customer's SIEM | Whether the record shows the screen as the model saw it |
Read down the two middle columns and the difference is the object each product inspects. Proofpoint inspects traffic, so its answer to what the model received covers the traffic it carried and says nothing about a screen it never saw; RedactSure governs the screen, so its answer is the screen itself, as tokens. They also differ on who turns a token back into a real value: a policy and an intent score on Proofpoint's pages, a named person in RedactSure's design, as What Is Supervised Delegation? describes.
What does Proofpoint protect, and where does it act?
Proofpoint is a data security platform with several controls that reach AI tools and agents. For people using AI tools, AI Access Security "inspects prompts, responses, uploads, and runtime behavior so security teams can reduce shadow AI risk, protect sensitive data," and it will "Generate a forensic record of who initiated every AI interaction, what occurred, what data it used" (Proofpoint, AI Access, 2026). For agents, the Secure Agent Gateway "uses the Model Context Protocol (MCP)" and will "Block or redact sensitive data before it reaches humans or agents" (Proofpoint, Secure Agent Gateway, 2025).
Each benefit depends on one condition: the data must pass through a point Proofpoint inspects. The platform page places agents on API connections, MCP and custom integrations, and the MCP page covers traffic routed through the gateway. An agent that opens the CRM and reads the customer record from the screen sends nothing through a prompt filter or a gateway, so neither the intent check nor the redaction has an event to act on. This is a limit of the design rather than a defect, and it is the one that matters when the agent's work is a screen.
Proofpoint's own row in that review places it among the AI gateways and prompt inspection products that redact what a model receives in the traffic they handle, and its public documentation does not describe a SIEM export of that activity or a screen where an agent works with tokens instead of the real values. It does describe recording full prompt and response activity for investigations and audits (How Many AI Security Products Control What an AI Agent Sees?).
What does an agent read when it opens a CRM customer record?
The agent opens the CRM as a representative would, finds the account, and reads the screen: the name, the email address, the phone number and the account note. Nothing in that path is a prompt typed into an AI tool, an upload or a paste, so the AI Data Security controls that "Block or redact sensitive data in prompts, uploads, and paste actions" have no event to act on (Proofpoint, AI Data Security, 2026). No MCP call is made either, because the agent did not retrieve the record through a tool server; it read a rendered page, and a gateway that "Inspects requests, responses, tool calls, and data crossing MCP links" sees no link to inspect (Proofpoint, MCP Security, 2026). The model receives all four fields as the screen shows them, with the sidebar, the notifications and any text in the note, since everything visible enters the context (What Does an AI Agent See in a Screenshot?).
There is also an identity gap: the agent used the representative's session, so the CRM's log records the representative, and the two questions a later review asks, what the model saw and who answered for the action, have no entry anywhere (What Is an AI Control Record?).
Which control fits which work?
Keep Proofpoint for
- Sensitive data employees send into AI tools, where it will "Block or redact sensitive data in prompts, uploads, and paste actions" (Proofpoint, AI Data Security, 2026).
- Agents that reach tools over MCP, where the gateway can "Block or redact requests before they reach servers" (Proofpoint, MCP Security, 2026).
- Investigations of agent activity on those paths, where the product will "Trace agent reasoning, tool calls, and data access from requests to outcomes" (Proofpoint, Agentic AI Security, 2026).
Add RedactSure for
- Agents reading screens in applications the organization bought, where the model receives CUST_001 and TEL_001 instead of the real values (What Is Render-Layer Tokenization?).
- A named person approving each record change, submission or payment the agent proposes (What Is Supervised Delegation?).
- The record of what the model received on every screen, exported to the customer's SIEM (What Is an AI Control Record?).
Most agent programs need both products; this block shows which one protects the screen.
How do you test it on one workflow?
Run the comparison on one workflow the agent will actually perform, in an afternoon, with both vendors present and every answer written down.
Step 1. Pick one screen from a workflow the agent will actually run, such as the customer record in the CRM with the account note visible, rather than a diagram of the data flow.
Step 2. Have each vendor produce that screen as the model read it, with nothing redrawn. The output, real values or tokens, shows whether the control acts between the application producing the screen and the model reading it, the structural point in AI Gateway, DLP or Tokens: What Controls AI Exposure?.
Step 3. List every application in the workflow and mark each one as routed through the MCP gateway, inspected by Proofpoint's AI tool controls, or neither.
Step 4. Trace one value, the phone number, from what the model received back to the real value: what released it, when, and whose name is in the record. Ask whether each vendor's record shows the screen the model read or only the actions that followed.
Step 5. Calculate first-day coverage as the share of those applications each control reaches. Compare the costs that differ: routing each application through an MCP server and the gateway, and the agent work delayed until that is done.
What does a governed environment change?
A governed environment is a class of control rather than a feature of either product. The agent works under its own session, recorded under its own identity, and it sees a different screen from the representative because values are replaced with consistent tokens before the model reads anything. The customer appears as CUST_001, with EMAIL_001 and TEL_001, on every CRM screen of the run. 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 control record that exports to the organization's SIEM.
Research explains why the control belongs in front of the model. OWASP notes that "Indirect prompt injections occur when an LLM accepts input from external sources, such as websites or files," and a CRM screen with a free-text account note is such a source (OWASP, LLM01, 2025). Once a value is in the model, disclosure "can result in unauthorized data access, privacy violations, and intellectual property breaches" (OWASP, LLM02, 2025). A model that only ever received CUST_001 has nothing to disclose, the arithmetic in What Does Prompt Injection Get From a Token-Only Agent? and the principle in What Is Least Exposure?: keep the data an agent receives for a task to the minimum, whatever the permissions allow.
What the record shows
Proofpoint's real coverage, in its own words, is the traffic it inspects: "Inspects requests, responses, tool calls, and data crossing MCP links" and "Block or redact sensitive data in prompts, uploads, and paste actions". Both quotes cover prompts, uploads, pastes and responses in AI tools and MCP calls routed through its gateway, and a control over what a model reads on the screen of a bought application is not described in the pages reviewed. On the CRM screen, the render-layer control replaces the name, email address and phone number with tokens before the model reads it, and that is what RedactSure, as the Proofpoint alternative for agent work, does for this record, with real values released only for an approved action and a record of the agent's work separate from the representative's. Test it on one agent and one CRM task: have each vendor show the customer record as the model read it, then trace the phone number back to the action that released it and the person who approved it.
Methodology and limitations
This page draws on five Proofpoint product pages, one Proofpoint blog post, two OWASP entries and existing 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. Vendor statements are quoted in the vendor's own wording, and public documentation read in full gives a sound view of where each product applies its control.
Both products are described from their documentation, neither was tested for this page, and no customer of either is named; both products change, and the page carries its date. RedactSure is deploying now with enterprise and government customers. The customer record is an illustrative record type rather than an organization. Compliance determinations belong to the organization's counsel, assessors and security team, and nothing here replaces a pilot of one workflow on its own CRM screens.
Frequently asked questions
Does Proofpoint protect data sent to an LLM?
Only where the data passes through a point Proofpoint inspects. Its pages describe inspection of prompts, uploads, pastes and responses in AI tools, and of MCP traffic routed through its gateway, with redaction at each point. An agent that reads a customer record from a CRM screen sends nothing through those points, so the model receives the screen as displayed; the answer depends on the path the data takes.
Does Proofpoint cover what an agent reads on a CRM screen?
Not from the pages reviewed. Proofpoint's platform page places agents on API connections, MCP or custom integrations, and its MCP page covers traffic routed through the gateway. A service agent that works a bought CRM through its screen uses none of those paths. Those screens need a control between the application and the model, which RedactSure provides by replacing the values before the model reads them.
Who approves the agent's action, a policy or a person?
A policy and an intent check, on Proofpoint's pages, which will flag actions that go beyond the original request even when permissions allow them. That judgment is made by the product against the task scope. In RedactSure's design a named person approves each payment, submission or record change when the agent proposes it, and the approval carries that person's name. Ask which of the two each product provides.
Does Proofpoint's record show what the model received from the screen?
No. The record its pages describe is a reconstruction of each action from user request to outcome, and a forensic record of who initiated an AI interaction and what data it used. Both are built from the traffic and tool calls the product saw. When the agent read a rendered CRM page, no traffic passed through the product, so a record of that screen has to come from the environment the agent worked in.
Does the Secure Agent Gateway reach agents that do not use MCP?
Not from the pages reviewed. Proofpoint's announcement says the Secure Agent Gateway uses the Model Context Protocol, and its MCP page says coverage comes from routing MCP traffic through the gateway. An agent that operates an application through its interface, with no MCP server in the path, sends nothing to the gateway. The practical check is a count of the agent's applications that send traffic through it today.
Does "without rewriting applications" mean the control reaches a bought CRM's screen?
No. The AI Access Security page says a team can "Apply guardrails to block, redact, restrict, or escalate actions" in AI interactions "without rewriting applications," which means the AI tools it inspects need no change (Proofpoint, AI Access, 2026). The CRM is not one of those tools; the agent reads its screen directly, so the phrase describes how the inspection is deployed rather than which screens it can see.
How RedactSure closes the screen-level gap in traffic inspection
Traffic inspection covers the prompts, uploads and MCP calls that pass through it, so an agent reading a bought CRM's screen falls outside every point it checks. Proofpoint's own pages place agents on "API connections, MCP, or custom integrations" and describe no screen an agent reads.
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the CRM under the representative's session, the screen shows the name, email address, phone number and account note, and the model receives all four. Inside the governed environment: the same screen shows CUST_001, EMAIL_001 and TEL_001, and the real email address is released to the mail system only when the named representative approves the message.
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 Proofpoint. Bring one CRM workflow and see, with test values, the screen the model receives and the message waiting for the representative. Book a demo