Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs Limina (Private AI): 2026 Comparison & Alternative
Limina replaces identifiers in the text, files and prompts code sends to its container; RedactSure, a Limina 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 Limina alternative for AI agents are choosing where an identifier gets removed. Limina, whose announcement of March 5, 2026 states that "Private AI has officially rebranded as Limina, a name derived from the Latin" word for threshold, sits in the data pipeline: code sends text, a file or a prompt to its container and gets back a copy with identifiers replaced (Limina, Rebrands, 2026). RedactSure sits on the screen: values become consistent tokens on the rendered page, so the model never receives the real name or claim number.
The record used throughout is a patient chart in a bought electronic health record system, holding a name, date of birth, medical record number, insurance claim number and diagnosis, opened by an agent that works it the way a billing specialist does. What Is Render-Layer Tokenization? defines the approach, and What Is the PII Wall? explains why agent projects stall at such records.
Key findings
- As a Limina alternative for agent work, RedactSure replaces values on the rendered page before any model reads it, so the agent works the chart as PATIENT_001 and the real value is released only at the approved destination (What Is Render-Layer Tokenization?).
- Data a model receives is an exposure wherever it was stored; OWASP lists "Personal identifiable information (PII) may be disclosed during interactions with the LLM" as a disclosure risk (OWASP, LLM02, 2025).
- None of Limina's pages describes what a model reads on the rendered screen of a bought application; its introduction describes "a flexible and lightweight addition to any data pipeline or workflow" (Limina, Introduction, 2026).
- Limina describes itself as "a stateless, container-based entity detection and redaction engine that specializes in the detection and de-identification of sensitive information in unstructured and structured data" (Limina, Introduction, 2026).
- Its unit of work is the submitted string: "The process/text endpoint accepts a list of text strings and replaces each piece of PII found with a redaction marker," so a value never submitted is never seen (Limina, Usage Quickstart, 2026).
- For applications that call a model, Limina documents prompt redaction in which "you'll deidentify user prompts, send them to OpenAI's ChatGPT, and then re-identify the responses," code written around each model call (Limina, Prompt Redaction, 2026).
How do the two compare side by side?
Each row sets Limina's wording beside a render-layer control on the same dimension, with the check a buyer can run.
| Dimension | Limina, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | "a flexible and lightweight addition to any data pipeline or workflow" (Limina, Introduction, 2026) | On the screen the agent works in, before any model reads it | The exact point a value is replaced |
| How coverage starts | "Limina integrates with your existing data infrastructure through REST APIs and containerized deployment" (Limina, Data De-identification, 2026) | The agent works inside the governed environment; the bought application is unchanged | How many of the agent's applications are wired to the container |
| What the model receives | "you'll deidentify user prompts, send them to OpenAI's ChatGPT, and then re-identify the responses" (Limina, Prompt Redaction, 2026) | The screen with consistent tokens, PATIENT_001 and MRN_001, for the whole run | One screen as the model saw it |
| How real values are restored | "Entities that are removed prior to sending to the LLM can be re-injected into the response from the LLM" (Limina, llms.txt, 2026) | Released at an approved destination when a named person approves the action | What releases it, and where that is recorded |
| Who approves an action | The pages reviewed describe no approval step; the calling code receives the de-identified copy and decides what happens next | A named person approves every claim submission, payment and record change | A name beside each submission, not a code path |
| The audit record | "a copy of the file with all entities removed is created and returned" (Limina, llms.txt, 2026); no record of what a model read | Every screen as tokens, every action and every approval, exported to the customer's SIEM | Whether the record shows what the model saw |
Limina governs the strings, files and prompts a pipeline submits to its container, so its answer to what the model saw covers the applications wired to it and is silent about the rest. RedactSure governs what an agent reads, with nothing placed inside the application, and its answer is a record of the screen as the model saw it. They also differ on who turns a token back into a real value: calling code on Limina's pages, a named person in RedactSure's design (What Is Supervised Delegation?). Placement explains the shape of the field: across 86 AI security and data protection products reviewed, 58 control where data can go and 27 control what a model receives in pipelines or traffic they handle, while a single product describes masking what an agent reads on the web pages it works in (How Many AI Security Products Control What an AI Agent Sees?).
What does Limina protect, and where does it act?
Limina's pages describe a detection and redaction engine that runs inside the customer's own infrastructure. Its product page says "Context-aware ML models identify PII, PHI, and PCI across 50+ entity types the way a trained human would," and that "Container-based deployment means all processing stays in your VPC or on-premises infrastructure" (Limina, Data De-identification, 2026). Images and scanned documents are covered when they are submitted, with a guide for "setting up the Limina container with the Azure OCR for processing images and documents" (Limina, Azure OCR, 2026). For a research dataset, that is a complete control, and a breach of the de-identified copy exposes markers, not patients. Rishov Chatterjee, AI engineer at Curology and former Director of Data Science and AI at Integra Connect, said the definition comes first: "we have to think of how do we constitute PHI to begin with." (HAL Transcript: Rishov Chatterjee).
Every benefit depends on one condition: the data has to be sent to the container. An agent working a chart in a bought EHR reads the screen that system renders, and no pipeline the customer built sits between that screen and the model. The design ends at the container's API; the screen lies past it.
What does an agent read when it opens a patient record?
A research copy of the chart may have passed through the container with the name and medical record number replaced, but the agent never opens that copy. It signs in to the EHR the way a billing specialist does and reads the chart the system renders: the name, the date of birth, the medical record number, the claim number and the diagnosis, all in the model's context at once (What Does an AI Agent See in a Screenshot?). Nothing on that screen was ever a string submitted to a de-identification endpoint; the EHR vendor's code draws the page.
OWASP's disclosure entry warns that the result "can result in unauthorized data access, privacy violations, and intellectual property breaches," and the model holds the full chart from the moment it reads the screen (OWASP, LLM02, 2025). HHS reminds covered entities that even properly applied de-identification methods "yield de-identified data that retains some risk of identification," so a chart the model read in full is well past that line (HHS Office for Civil Rights, 2026). Since the agent used the billing specialist's session, the EHR's audit trail records a person, and no record of what the model saw exists.
Which control fits which work?
Keep Limina for
- Datasets a team wants to "Turn your most restricted data into fuel for AI, analytics, and research with data de-identification that works inside your own environment" (Limina, Data De-identification, 2026).
- Data that needs a treatment chosen per use: "redact, pseudonymize, tokenize (reversibly), or replace with synthetic data that preserves the statistical shape of your dataset" (Limina, Data De-identification, 2026).
- Applications the team builds around a model, using "prompt redaction and re-hydration" so that "confidential information remains safe while interacting with AI models" (Limina, Prompt Redaction, 2026).
Add RedactSure for
- Agents reading screens in bought applications, where the model receives PATIENT_001 instead of the real value (What Is Render-Layer Tokenization?).
- A named person approving each claim submission, payment or record change the agent proposes (What Is Supervised Delegation?).
- The record of what the model received on each screen, exported to the customer's SIEM (What Is an AI Control Record?).
An organization running agents usually needs both; this block shows which product protects the screen.
How do you test it on one workflow?
Run the comparison on one real workflow, over an afternoon, with both vendors present and the answers written down.
Step 1. Choose a task the agent will run, such as preparing a claim from a chart, and pick one screen from it, the patient record as the EHR renders it.
Step 2. Ask each vendor to show that screen exactly as the model saw it; the output settles the question. How Do Data Masking and AI Tokenization Differ? explains why a token the model can follow across screens differs from a masked value.
Step 3. List every application in the workflow, the EHR, the payer portal and the clearinghouse, and mark each as sending its data through the container or not; the rest are the first-day exposure.
Step 4. Trace the claim number from marker or token back to its real value: what releases it, when, and whose name is in the record. On Limina's pages that step belongs to the calling code; on a render-layer control it is a person.
Step 5. Calculate first-day coverage: five applications touched and one wired to the container is one of five. Compare the costs that differ, the pipeline work per application and the agent work delayed until it exists.
What does a governed environment change?
A governed environment is a class of control, not a feature of one product. The agent works under its own session and identity, and it sees a different screen from the billing specialist because values are replaced with consistent tokens before the model reads anything. The patient appears as PATIENT_001, with MRN_001 and CLAIM_001, on every screen of the run. Real values resolve only at approved destinations at the moment of an approved action, a named person approves every claim submission, payment and record change, and every screen, action and approval is written to a record that exports to the SIEM.
OWASP notes that "Indirect prompt injections occur when an LLM accepts input from external sources, such as websites or files," and its first mitigation is to "Restrict the model's access privileges to the minimum necessary for its intended operations" (OWASP, LLM01, 2025). A model that only ever held PATIENT_001 cannot disclose a medical record number it never received, the principle in What Is Least Exposure?: least privilege governs actions and credentials, not the screen an agent reads.
What the record shows
The buyer's one question is who decides what the model receives, and whose name is on the release. The patient's name, medical record number and claim number become tokens on the chart before the model reads it, which is what RedactSure, as the Limina alternative for agent work, does for this record, with real values released only for an approved submission and a record kept apart from the billing specialist's. Limina's coverage is the text, files, images and prompts a pipeline submits to its container; its pages describe no control over what a model reads on the screen of an application never wired into that pipeline. Test one chart, one claim: ask each vendor to show the record as its model reads it, then follow the claim number to the step that restored it and the name on that step.
Methodology and limitations
This page draws on seven Limina pages, two OWASP entries, one HHS guidance page 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. Vendor statements are quoted in the vendor's 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.
Neither product was tested for this page; both are described from documentation, both change, and the page carries its date. RedactSure is deploying now with enterprise and government customers. The patient record is a record type, not an organization. Determinations rest with the organization's counsel, assessors and security officers, and a pilot of one workflow on its own screens is the test this page cannot replace.
Frequently asked questions
Does Limina protect data sent to an LLM?
Only in applications wired to it. Limina's guide shows how to "deidentify user prompts, send them to OpenAI's ChatGPT, and then re-identify the responses," and its index notes that removed entities "can be re-injected into the response from the LLM." Both run as code around each model call; when an agent opens a chart in a purchased application and reads the screen, no call to the container happens.
Does Limina cover what an agent reads on a purchased application's screen?
Not from the pages reviewed. Limina's pages describe an engine that receives text strings, files and images through its API and returns a de-identified copy, processed inside the customer's environment. A rendered screen in a bought application is never submitted to that API, because the application vendor's code draws it, so those screens need a control between the page and the model.
Who approves the agent's action, the calling code or a person?
The calling code, on Limina's pages. The quickstart returns de-identified text to whatever submitted it, and the LLM guide re-identifies responses in application code, so restoring a value is decided in the pipeline in advance. In RedactSure's design a named person approves each claim submission, payment or record change the agent proposes. Ask each vendor whose name appears beside a restored value.
Will the EHR's audit log show that an agent read the chart?
Only as the person whose session the agent used. An agent signed in with the billing specialist's credentials appears in the EHR's audit trail as the billing specialist, and a container that never saw the screen has nothing to log. A record separating the agent's reads and actions from the person's has to come from the environment the agent worked in; RedactSure's AI Control Record holds every screen, action and approval, exported to the SIEM.
Is de-identification the same as render-layer tokenization?
No. De-identification, as Limina's pages describe it, is a set of transformations applied to submitted data: redaction markers, pseudonyms, reversible tokens or synthetic values, chosen per dataset. Render-layer tokenization describes where substitution happens: on the screen, before a model reads it, with one token per patient for the whole run. A product can do the first well without the second; they answer different questions.
Can Limina's image processing read the screen an agent sees?
No. Limina's image processing "handles both visual and textual PII" in image files a pipeline submits, with OCR to find text inside them, and its Azure OCR guide covers "processing images and documents" likewise. A live screen an agent reads is never an image file submitted to that container; the model reads it directly, so image support covers documents at rest, a different job from a screen in use.
How RedactSure closes the screen-level gap in pipeline de-identification
Pipeline de-identification covers only what a team's code submits to the container, so an agent working a chart in a bought EHR reads the record as displayed. Limina's quickstart describes a text endpoint that "accepts a list of text strings and replaces each piece of PII found with a redaction marker."
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the EHR under the billing specialist's session and the model receives the name, date of birth, medical record number, claim number and diagnosis. Inside the governed environment: the chart shows PATIENT_001, MRN_001 and CLAIM_001, and the real claim number reaches the payer portal only when the named billing supervisor 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 Limina. Bring one claim workflow and see, with test values, the chart as the model receives it and the submission awaiting approval. Book a demo