Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs Keeper: 2026 Comparison & Alternative
Keeper injects a vaulted credential into a session and records it, while RedactSure, a Keeper alternative built for AI agents, replaces page values before any model reads them. 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.
Keeper's features page promises to "Deliver secrets to local tools and AI agents from your encrypted vault, with no plaintext credentials on disk," and the promise shows where a Keeper alternative has to begin, because the value lands with the workload rather than on the page the agent reads (Keeper, Features, 2026). RedactSure replaces the values on that page with consistent tokens before any model reads them, and releases a real value only at an approved destination.
This page quotes Keeper's own pages and follows one record throughout: a database password held in a vault, and the customer record the admin page behind it renders, with a name, an email address, an account number and an order history, worked by an agent the way a support rep works it. What Is Least Exposure? sets out why the values an agent receives should stop at what the task needs.
Key findings
- As a Keeper alternative for agent work, RedactSure replaces the values on the admin page with consistent tokens before any model reads it, so the agent works with CUST_001 and ACCT_001 and the real values are released only at the approved destination (What Is Render-Layer Tokenization?).
- A page that renders a full customer record for an agent is an exposure in its own right, whatever the storage layer does, and the GDPR's minimisation principle holds that personal data must be "adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed" (Regulation 2016/679, 2016).
- None of Keeper's pages describes replacing a value on a rendered page before a model reads it; the seven pages reviewed place their controls on the credential, the command and the session (Keeper, Features, 2026).
- Keeper masks passwords in the vault, the browser extension and the mobile apps so users covered by the policy "can't unmask them," a privacy screen over the vault rather than over a third party page (Keeper, Features, 2026).
- For agents the vendor adds that "Secrets are never written to disk, logs or prompts, and are encrypted end-to-end," a statement about storage and transmission rather than about what a model reads (Keeper, Secure AI Agents, 2026).
- Session control is the other half: credentials are "injected into privileged sessions without being exposed to users," and the recording includes graphical playback and text-based logs (Keeper, Privileged Session Management, 2026).
How do the two compare side by side?
The table sets Keeper's wording beside what a governed environment does on the same point, and the last column names the check a buyer can run.
| Dimension | Keeper, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | "Deliver secrets to local tools and AI agents from your encrypted vault, with no plaintext credentials on disk" (Keeper, Features, 2026) | On the screen the agent works in, before any model reads it | The exact place a value is replaced |
| How coverage starts | "Providing vault access to machines, workloads and applications" (Keeper Documentation, Secrets Manager Overview, 2026) | The agent works inside the governed environment and the application is unchanged | How many of the agent's applications are covered today |
| What the model receives | "Secrets are never written to disk, logs or prompts, and are encrypted end-to-end" (Keeper, Secure AI Agents, 2026) | The page with tokens such as CUST_001 and ACCT_001, consistent for the whole run | One screen, saved exactly as the model saw it |
| How real values are restored | Credentials are "injected into privileged sessions without being exposed to users" (Keeper, Privileged Session Management, 2026) | Released at an approved destination when a named person approves the action | Who authorizes it, and where that is recorded |
| Who approves an action | "When a user attempts an action that requires elevated access, a prompt appears based on the organization's policies" (Keeper, Endpoint Privilege Management, 2026) | A named person approves every payment, submission and record change | A person's name, not a rule |
| The audit record | "Every privileged action can be logged, recorded and linked to a specific AI identity" (Keeper, Secure AI Agents, 2026) | Every screen as tokens, every action and every approval, exported to the customer's SIEM | Whether the record shows what the model saw |
Across the two middle columns, the difference is the object each product governs. Keeper governs the credential a workload holds and the session it opens. RedactSure governs what arrives on the screen an agent reads, so its record answers for the fields the model held. They also part on who turns a token back into a real value: a policy on Keeper's pages, a named person in RedactSure's design (What Is Supervised Delegation?).
What does Keeper protect, and where does it act?
Keeper's pages describe a custody and session design for credentials a team holds.
The secrets manager documentation puts the same control in its use cases, from "Removing hard-coded credentials from source code" to "Providing vault access to machines, workloads and applications" (Keeper Documentation, Secrets Manager Overview, 2026). A pipeline, a machine and an application each receive a value the vault holds.
Session management shows where the value lands: "Ephemeral and static credentials are managed securely in the Keeper Vault and injected into privileged sessions without being exposed to users," and "Recordings include graphical playback and text-based input/output logs" (Keeper, Privileged Session Management, 2026).
Each benefit depends on one condition: the credential is in the vault and the thing that asks for it can reach it. Injection puts the real value into the session, so the person at the keyboard never touches it, and the pages describe no control over what a model reading that session's screen receives. That is a limit of where the control is placed, not a defect in it. The same limit applies to the password managers an organization already runs, as RedactSure vs 1Password: 2026 Comparison & Alternative sets out for that vault.
What does an agent read when it opens the admin page behind a customer record?
The record is a database password held in a vault, and the customer record the admin page behind it renders: a name, an email address, an account number and an order history. An agent signs in and works the page the way a support rep does.
The vault's control reaches the password, and the session control reaches the session the agent opens. Neither reaches the fields the page draws. Keeper's remote browser isolation comes closest: "Autofilled credentials are never seen or available to the end-user, providing the best protection against DOM inspection, reflected cross-site scripting attacks and API abuse" (Keeper, Remote Browser Isolation, 2026). The value withheld there is the login the browser fills, and the rest of the application still renders.
An instruction hidden in an order note or a customer email then has a target. The EchoLeak researchers showed a production assistant that "automatically fetches the external image URL included by the attacker," which is how content the agent never chose reaches its context (Reddy and Gujral, 2025). Work on browsing agents reports findings that "include prompt injection, domain validation bypass, and credential exfiltration" (How Do I Stop a Browser Agent From Seeing the Token It Creates?).
There is an identity gap as well. The admin page logs the employee whose session the agent borrowed, the arrangement What Is a Tethered Agent? contrasts with delegated authority. The split runs through the agent category as well: 9 of the 15 reviewed keep passwords or credentials away from the model, against 0 that do the same for the customer data on the screen, from vendor documentation rather than tested behavior (Do AI Agents Ask Before Making a Payment?).
Which control fits which work?
Keep Keeper for
- Credentials a workload needs at run time, where the value can arrive from an encrypted vault with "no plaintext credentials on disk" (Keeper, Features, 2026).
- Higher access on an endpoint, where "a prompt appears based on the organization's policies" before the action proceeds (Keeper, Endpoint Privilege Management, 2026).
- Remote sessions that must be recorded, since they "can record screen and keyboard activity across all protocols" (Keeper, Privileged Access Management, 2026).
Add RedactSure for
- Agents reading screens in applications the organization bought, where the model receives CUST_001 instead of the account number (What Is Render-Layer Tokenization?).
- A named person signing off on each payment, submission or record change the agent proposes (What Is Supervised Delegation?).
- The record of what the model received on each screen, in the customer's SIEM (What Is an AI Control Record?).
An organization running agents usually needs both: Keeper for the credential and the session, and a render-layer control for what the agent reads.
How do you test it on one workflow?
Run it on one real workflow, over an afternoon, with both vendors present and every answer written down as it comes, because the screen decides the comparison.
Step 1. Choose the workflow the agent will actually run and pick one screen from it, such as the customer record on the admin page, rather than a diagram of the data flow.
Step 2. Ask each vendor to replay the model's input for that screen, field by field, with the output saved, so the fields the model held are visible rather than only described.
Step 3. List every application in the workflow and mark each as covered by this vendor's control or not; the uncovered ones are the exposure on the first day, and that count belongs in the pilot plan from the start.
Step 4. Trace one value, such as the account number, from token back to its real form: what released it, when, and whose name sits in the record.
Step 5. Calculate first-day coverage. When the workflow reaches six applications and the vault covers two, the count is two of six. Compare the costs that actually differ, such as policy tuning, agent work delayed and the applications neither product covers, without assuming what either vendor publishes about pricing.
What does a governed environment change?
A governed environment is a category of control rather than a product feature. The agent works under its own session, recorded under its own identity, and it reads a different screen from the employee, because values are replaced with consistent tokens before the model reads anything. The customer appears as CUST_001, with EMAIL_001 and ACCT_001, on every page 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 goes to a record exported to the organization's SIEM.
Research explains why the control belongs in front of the model. An injected instruction can move an agent off its task, and the literature has a name for the result: researchers tracking an agent's activations "define this deviation as task drift" when the model departs from the user's original instruction (Abdelnabi et al., 2025). A model that read only CUST_001 has nothing to be moved into repeating, which is the minimisation argument applied to a screen rather than to a database.
What the record shows
Keeper's pages cover the credential a workload holds: it arrives from an encrypted vault with "no plaintext credentials on disk" (Keeper, Features, 2026). The screen the agent reads is the part those pages leave alone, which is where a render-layer control works: RedactSure, as the Keeper alternative for agent work, replaces the values on the admin page with tokens before any model reads them and releases a real value only when a named person approves the action. Test one admin page through both products: name one agent, one screen and one value, then ask each to show the fields the model held and what released the real number.
Methodology and limitations
This page rests on seven Keeper pages, two research papers, the GDPR text and RedactSure research pages. Vendor pages were read as of October 24, 2026, and each Sources line carries the date for its page. Keeper's 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; where this page says a source does not describe something, it refers to the pages reviewed.
The products on this page are described from documentation; none was tested for this page, and no customer of either product is described. Documentation describes intended design, both products change, and the page carries its date. RedactSure is deploying now with enterprise and government customers. The admin page and the record it renders are an illustrative record type, not 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 screens.
Frequently asked questions
Does Keeper protect the data an agent reads on a screen?
Only where the page is a login and the value comes from the vault. Keeper's pages describe a stored credential reaching a workload, a fill the browser extension makes, and session recording; the record the admin page renders is none of those. Its remote browser isolation withholds the autofilled login, and the rest of the page still draws, so a name, an email address and an account number reach the model.
Who approves what an agent does once it has signed in?
A person, when an endpoint asks for higher access, and a policy for the rest. Keeper's endpoint page states that "a prompt appears based on the organization's policies" and that the Keeper agent allows an elevation automatically when no approval is needed. That decision was written in advance and covers an action on an endpoint, so no named person sits on the record for a refund the agent proposes.
What record shows what the model read on that page?
Not one from the pages reviewed. Keeper records sessions, privileged actions and screen activity, and its AI page says "every privileged action" can be linked to "a specific AI identity" (Keeper, Secure AI Agents, 2026). Each describes what happened in a session. None is a record of the fields a model received from a page, the gap a screen-level control fills.
Is a vault the same as a control on the rendered page?
No. A vault holds a value the organization deposited and returns it to a workload that asks; the admin page draws its fields from a database the vault does not stand in front of, as RedactSure vs HashiCorp Vault sets out. RedactSure substitutes on the page, so the model receives CUST_001 where the account number would be, and the value resolves only at the approved destination.
Does Keeper's isolation cover an agent working in a browser?
Partly. Keeper's remote browser isolation offers a "pre-approved list of URLs within a secure browser environment" and can record what the session did. It withholds the login the protocol fills and leaves the application's record on screen, and the page names no AI agent as the user of the isolated session, so an agent reading the customer record there still receives the fields the application draws.
Can a team keep Keeper and still close the screen gap?
Yes. Keep it for the credentials a workload needs and the sessions that have to be recorded, which its pages describe. The records an agent reads in applications the organization bought are a separate question, and RedactSure answers it by replacing those values before the model reads the page, with a named approver and an exportable record for each action. The two controls answer different questions.
How RedactSure closes the screen gap a vault leaves behind
A vault hands the credential to the workload, and the session it opens renders whatever the application draws, so the customer record reaches the model intact. Keeper's masking sits in the vault, the browser extension and the mobile apps, not on the page the 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 admin page under the rep's session, and the model receives the name, the email address, the account number and the order history. Inside: the page shows CUST_001, EMAIL_001 and ACCT_001, and the real account number reaches billing only when the named lead approves the refund.
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 Keeper. Bring one admin workflow and see, with test values, what the model reads and the approval before the record changes. Book a demo