Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs Immuta: 2026 Comparison & Alternative
Immuta controls what an agent's queries return, through a temporary data platform role, while RedactSure, an Immuta alternative built for AI agents, tokenizes the screen before the 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 weighing an Immuta alternative for AI agents find that the two products answer different questions. Immuta decides what an agent's query may return, through a temporary role in the connected data platform; RedactSure decides what the agent's model receives from a screen, by replacing identifiers on the rendered page with consistent tokens before the model reads anything.
This page quotes Immuta's own documentation and uses one record throughout: a patient record in an electronic health record system the organization bought rather than built, holding a name, a date of birth, a medical record number and a diagnosis, opened by an agent that works the chart the way a care coordinator does. What Is Render-Layer Tokenization? defines the control RedactSure applies, and How Do Data Masking and AI Tokenization Differ? explains the difference from masking.
Key findings
- As an Immuta alternative for agent work, RedactSure replaces the name, date of birth and medical record number on the chart with tokens before any model reads it, so the agent handles PATIENT_001 and MRN_001 and real values are released only at an approved destination (What Is Render-Layer Tokenization?).
- A value the model has received is an exposure however well the database copy is protected; OWASP lists "Personal identifiable information (PII) may be disclosed during interactions with the LLM" as a disclosure risk, and tokens placed before the model reads the screen keep that value out of its context (OWASP, LLM02, 2025).
- None of Immuta's pages describes an agent that opens the chart of a bought application; the agents they describe query a data platform through a vended role, for "text-to-SQL tasks: chatbots and AI analysts" (Immuta, Introduction, 2026).
- Immuta registers agents as their own identities: "In Immuta, AI agents are registered users in the data ecosystem," and when one acts for a person "it operates under that human’s Immuta-defined access rights" (Immuta, Agentic Data Access immuta.com, 2026).
- Enforcement and record both sit at the data platform: "These rules are enforced at the data tier, so no prompt can bypass them," and "Every query executed by an agent is tagged with both the agent ID and the end-user ID" (Immuta, Agentic Data Access immuta.com, 2026).
How do the two compare side by side?
The table sets Immuta's wording beside a governed environment on each point, with the check a buyer can run.
| Dimension | Immuta, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | "Immuta applies policy-based access controls natively in each data platform, controlling data at the moment it’s accessed, inside the system that serves it" (Immuta, Govern, 2026) | On the screen the agent works in, before any model reads it | Where, in one workflow, a value is masked or replaced |
| How coverage starts | An agent harness that "first calls the Immuta agentic access API to obtain an ephemeral role" (Immuta, Reference Guide, 2026) | The agent works inside the governed environment; nothing is placed inside the application | How many of the agent's applications sit behind a connected platform today |
| What the model receives | The query result the role permits, where a restricted "column is masked when the result is returned to them" (Immuta, Introduction, 2026) | The screen with consistent tokens, such as PATIENT_001 and MRN_001, for the whole run | One screen, shown exactly as the model saw it |
| How real values are restored | The pages describe the end of access, not a release step: "Access is removed as soon as the agent’s task is complete" (Immuta, Agentic Data Access immuta.com, 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 request falls outside of policy, or requires human review, Immuta automatically routes it to the right reviewers" (Immuta, Provisioned, 2026) | A named person approves every payment, submission and record change | Whether the approval names a person, and whether it covers an application action or a data request |
| The audit record | "Every query executed by an agent is tagged with both the agent ID and the end-user ID" (Immuta, Agentic Data Access immuta.com, 2026) | Every screen as tokens, every action and every approval, exported to the customer's SIEM | Whether the record shows what the model saw |
Immuta governs the query an agent sends to a connected data platform and the rows and columns that come back; RedactSure governs what the agent reads, and its answer to what the model saw is one record of the chart as received.
What does Immuta protect, and where does it act?
Immuta's agentic data access feature is, in its documentation, "a centralized authorization framework that governs how AI agents and LLMs interact with sensitive data" (Immuta, Introduction, 2026). The mechanism is a role: "Immuta vends an ephemeral role that allows the agent to inherit the user’s specific permissions temporarily," and "Agents cannot log into Immuta and can only interact with Immuta by using the API to request the ephemeral role" (Immuta, Reference Guide, 2026). Inside the platform, "data policies govern data visibility through row-level restriction, column-level masking, purpose-based controls, and more" (Immuta, Govern, 2026).
Jim Sinkoff, Deputy CEO and CFO, Sun River Health, said he encourages colleagues "to really think about data governance from this very core perspective who owns the data?" (Who Owns the Data When AI Touches Patient Records? A Health System CFO on AI Governance).
Each benefit depends on one condition: the agent reaches data by querying a platform Immuta is connected to. The CTO's post describes agents that "dynamically generate code, like SQL, to query enterprise platforms on the fly," and when "the Time-to-Live (TTL) expires, Immuta automatically drops the role" (Immuta, Breaking Point, 2026). An agent that opens the chart screen of a bought application sends no query for a vended role to scope, so the control has nothing to act on there, a boundary of the design rather than a defect.
What does an agent read when it opens a patient record?
Consider the patient record. The agent never queries the warehouse; it opens the electronic health record under the coordinator's session and reads the chart the application renders. No vended role sits in that path, because the role scopes "every query executed against the data platform" and the chart screen is not a query (Immuta, Reference Guide, 2026). The model receives the name, date of birth, medical record number and diagnosis as the screen shows them, with everything else on the page (What Does an AI Agent See in a Screenshot?).
The exposure happens in the model's context rather than in storage: OWASP's disclosure entry warns that the result "can result in unauthorized data access, privacy violations, and intellectual property breaches," and for health data the minimum necessary standard asks for "reasonable efforts to limit protected health information to the minimum necessary" for the purpose (OWASP, LLM02, 2025; Cornell Law School, 2026). A narrowly permissioned agent still reads every field its permissions open (Does Least Privilege Cover What an AI Agent Sees?). There is an identity gap as well: Immuta's pages hold that "Agents act on behalf of users, not as users," but an agent that borrows the coordinator's session is logged by the application as the coordinator (Immuta, Introducing, 2026).
The record of that read is where the category is uneven: of the 8 data security posture and governance products reviewed, 4 describe recording prompts or request payloads at their gateway or pipeline and 2 describe a SIEM export of AI activity (documentation, not tested behavior) (How Many AI Security Products Control What an AI Agent Sees?).
Which control fits which work?
Keep Immuta for
- Queries against a connected data platform, where "every query executed against the data platform is scoped to exactly what the end user is permitted to access" (Immuta, Reference Guide, 2026).
- Masking inside the warehouse, since "These policies are written once in Immuta, yet are enforced in all your data platforms" (Immuta, Breaking Point, 2026).
- Access requests decided at question time, since "Immuta's dynamic determination evaluates each agent request in real time and makes an instant decision" (Immuta, Provisioned, 2026).
Add RedactSure for
- Agents reading screens in bought applications, where the model receives PATIENT_001 and MRN_001 and nothing is placed in the application (What Is Render-Layer Tokenization?).
- A named person approving each 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?).
Most organizations running agents need both products; this block shows which one protects the screen.
How do you test it on one workflow?
Compare the two on one real workflow, with both vendors present and the answers written down.
Step 1. Choose a workflow the agent will run, such as preparing a referral, and pick one screen from it, the patient's chart.
Step 2. Ask both vendors for the chart as the model saw it, nothing summarized. Immuta's answer will be a query result scoped by the vended role; a render-layer control's answer is the chart with tokens in place of the identifiers.
Step 3. List every application in the workflow and mark each as reached through a connected data platform, or opened as a screen. The second group is the first-day exposure.
Step 4. Trace one value, the medical record number, from token back to its real value: what released it, when, and whose name appears in the record. On Immuta's side, ask for the activity tab, which "shows which role was vended for which users," and check whether it names an approver or only the person whose entitlements the role copied (Immuta, Reference Guide, 2026).
Step 5. Calculate coverage. Compare the costs that differ: connecting each platform and harness to Immuta, and the agent work delayed until then, so both figures sit beside the coverage count and are read together.
What does a governed environment change?
A governed environment is a class of control, not a product feature. The agent works under its own session and sees a different screen from the coordinator, because values are replaced with consistent tokens before the model reads anything. Real values resolve only at approved destinations at the moment of an approved action, a named person approves every submission and record change, and every screen, action and approval lands in a record exported to the SIEM.
Research explains why the control sits in front of the model: OWASP's prompt injection entry notes that "Indirect prompt injections occur when an LLM accepts input from external sources, such as websites or files," and its listed mitigation, "Restrict the model's access privileges to the minimum necessary for its intended operations," governs what the model may do rather than what it has already read (OWASP, LLM01, 2025). A model that only ever received PATIENT_001 cannot be made to disclose a name it never held, the principle in What Is Least Exposure?.
What the record shows
The record this page follows is a patient record in an electronic health record system the organization bought rather than built, carrying a name, date of birth, medical record number and diagnosis. On that chart a render-layer control replaces those fields with tokens before the model reads it, the job RedactSure, the Immuta alternative for agent work, does here, with real values released only for approved actions. Immuta covers the query path: a registered agent, a temporary role vended in the data platform, rows filtered, columns masked in the result, every query tagged with agent and person. Its pages describe no control over a screen an agent reads. The test takes one workflow, one screen: have each vendor show the chart the model saw, then trace the medical record number to the action that released it and who approved it.
Methodology and limitations
This page draws on seven Immuta pages, five of documentation and two executive blog posts, two OWASP entries, the federal minimum necessary standard as republished by Cornell's Legal Information Institute, and RedactSure research pages. Vendor pages were read between October 21, 2026 and October 23, 2026. Public documentation read in full gives a sound view of where each product applies its control; "not described" refers to the pages reviewed.
Both products are described from documentation and neither was tested for this page; both change, and the page carries its date. Immuta's documentation labels agentic data access a public preview. RedactSure is deploying now with enterprise and government customers. The patient record is a record type, not an organization. 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 Immuta protect data an agent sends to an LLM?
Only where the agent reaches that data by querying a platform Immuta governs. Its documentation describes "a centralized authorization framework that governs how AI agents and LLMs interact with sensitive data," so a masked column comes back masked. Data the agent reads from an application screen never passes through that role, so the model receives what the screen shows.
Does Immuta cover what an agent reads on a purchased application's screen?
Not from the pages reviewed. Immuta's pages describe agents that write queries, obtain an ephemeral role through its API and receive the results that role permits. An agent that opens a health record and reads the chart sends no query for a role to scope, so that screen needs a control in front of the model.
Who approves the agent's action, a policy or a person?
A policy, with human review for requests outside it. Immuta's provisioning page routes a request that "falls outside of policy, or requires human review" to reviewers, and the thing reviewed is a data access request, not an action in an application such as submitting a referral. In RedactSure's design a named person approves each submission or record change, and the approval carries that name.
Will the application log show that an agent acted?
Not on its own. When an agent works the chart under a coordinator's session, the health record logs the coordinator, and Immuta's record covers queries, each "tagged with both the agent ID and the end-user ID," not screens. A record that separates the two has to come from the environment the agent worked in; RedactSure's AI Control Record holds every screen as tokens, every action and every approval.
Is a masked query result the same as a tokenized screen?
No. Immuta masks a column when the result is returned, which protects the value for that query. A tokenized screen replaces the identifier the application displays with a consistent token, such as MRN_001, before a model reads it, and keeps that token on every screen of the run so the agent can follow the patient. The first protects a result set; the second protects what the model is shown.
Does an organization still need Immuta if it adds a screen-level control?
Yes, for the query path. A render-layer control covers what a model reads on a screen and leaves the warehouse untouched; it does not filter rows for an analyst's query, and Immuta's pages say its policies are "written once in Immuta, yet are enforced in all your data platforms." Teams whose agents both query governed platforms and work application screens usually run both, and the question is which one protects each screen.
How RedactSure closes the screen-level gap in data-tier access control
A temporary role vended at the data tier scopes what an agent's query returns, so an agent working the chart screen of a bought application reads the record as displayed. The vendor's documentation gives the use case as "text-to-SQL tasks: chatbots and AI analysts."
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the health record under the coordinator's session, the chart shows the name, date of birth, medical record number and diagnosis, and the model receives all of them. Inside the governed environment: the same chart shows PATIENT_001, DOB_001 and MRN_001, and the real medical record number is released to the referral form only when the named coordinator approves the submission.
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 Immuta. Bring one workflow and see, with test values, the screen the model receives and the referral waiting for the coordinator. Book a demo