Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs Tonic AI: 2026 Comparison & Alternative
Tonic AI redacts sensitive values in text, files and prompts sent to it; RedactSure, a Tonic AI 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 Tonic AI alternative for AI agents find that both products substitute for sensitive values, in different places. Tonic AI works on data a team sends to it: Tonic Textual redacts or synthesizes entities in text, audio and files handed to its SDK, API or interface, and Tonic Structural produces de-identified copies of databases. RedactSure works on the screen an agent is about to read, so the model receives a token where the application displayed a patient name.
This page quotes Tonic AI's own documentation and uses one example throughout: a patient record in an EHR billing module the organization bought rather than built, with a name, a date of birth, a medical record number and a free text clinical note, worked by an agent that handles the billing queue the way a coder does. What Is Render-Layer Tokenization? defines the approach RedactSure uses.
Key findings
- RedactSure, a Tonic AI alternative built for agent work, replaces the identifiers on a rendered screen with consistent tokens before any model reads it, so the agent works the billing queue as PATIENT_001 and the real values are released only at the approved destination (What Is Render-Layer Tokenization?).
- Whatever a pipeline redacts, the data a model receives from a screen is a disclosure risk of its own: OWASP lists "Personal identifiable information (PII) may be disclosed during interactions with the LLM" (OWASP, LLM02, 2025).
- Nothing is placed inside a bought application for the control to apply, because the substitution happens where the page renders; least privilege governs actions and credentials, not the content of the screen an agent reads (What Is Least Exposure?).
- Tonic Textual acts on the text, audio and files a team sends to it: it "uses proprietary Named Entity Recognition models to identify and transform sensitive entities" and will "Redact or synthesize sensitive entities consistently, without compromising quality or context" (Tonic.ai, Unstructured, 2026).
- Its LLM privacy proxy, written for chatbot prompts rather than a screen an agent opens, "sits between users, applications, and large language models to prevent sensitive data from being exposed to LLMs" (Tonic.ai, Chatbot, 2026).
How do the two compare side by side?
Each row pairs Tonic AI's own wording with what a governed environment does on the same point, then names the check to run.
| Dimension | Tonic AI, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | "sits between users, applications, and large language models" (Tonic.ai, Chatbot, 2026) | On the screen, before any model reads it | Where one value is replaced |
| How coverage starts | "Use the Textual Python SDK or the Textual REST API to manage datasets or to remove sensitive values from individual text strings and files" (Tonic.ai, Textual guide, 2026) | The agent works inside the governed environment; the application is unchanged | Which of the agent's applications send text to Textual |
| What the model receives | "Provide LLMs redacted data while optionally exposing the unredacted text to approved users" (Tonic.ai, Unstructured, 2026) | The screen with PATIENT_001, DOB_001 and MRN_001, consistent for the whole run | One screen, exactly as the model read it |
| How real values are restored | "the chatbot can display the original text to users while ensuring the LLM processes only the redacted data" (Tonic.ai, Chatbot, 2026) | At an approved destination, when a named person approves the action | Who releases it, and where that is recorded |
| Who approves an action | "Reviewers can then refine, adjust, or apply fully manual redactions wherever needed" (Tonic.ai, Guided Redaction, 2026) | A named person approves every payment, submission and record change | A person's name against the agent's action |
| The audit record | "Every status change, comment, and decision is automatically recorded with timestamps and user attribution" (Tonic.ai, Guided Redaction, 2026) | Every screen as tokens, every action and every approval, exported to the customer's SIEM | Whether the record shows what the model saw |
The two middle columns govern different objects. Tonic AI governs the text, files and database copies a team routes through it, so its answer to what the model saw is silent about a screen the agent opened on its own. RedactSure governs what the agent reads, and its answer is one record of the screen as the model read it, the placement difference also found against a payload API in RedactSure vs Protecto: 2026 Comparison & Alternative.
What does Tonic AI protect, and where does it act?
Tonic Textual will "Instantly redact sensitive PII from text and audio to safely train models, and secure real-time agentic workflows," and will "Redact sensitive information prior to using it within LLM prompts to prevent sensitive values from ever entering the chatbot system" (Tonic.ai, Unstructured, 2026). The REST API documentation states the operation plainly: "Redaction means to detect and replace sensitive values" (Tonic.ai, REST API, 2026). For databases, Tonic Structural is "a data de-identification platform designed to protect sensitive structured and semi-structured data," producing "compliant test data that mirrors the shape and complexity of production" (Tonic.ai, Test Data, 2026).
Each benefit depends on one condition: the data passes through Tonic. A screen in a production application the organization bought is not sent anywhere, and a test database that mirrors production is a copy, not the screen the application draws for a coder or an agent. This is a boundary of the design rather than a defect, and it matters because an agent reaches the patient record through that screen, where a control has to act before the model reads it.
What does an agent read when it opens a patient record in the billing module?
Consider the patient record. Whatever the team sent to Textual or copied with Structural, the agent working the billing queue opens the billing module the way a coder does and reads the screen it displays. No proxy sits in that path, because the proxy on Tonic AI's pages acts by "transforming, filtering, or replacing sensitive inputs before they reach external or internal models," and a screen the agent opened is not an input anyone sent (Tonic.ai, Chatbot, 2026). The model receives the name, the date of birth, the medical record number and the full note as shown, and a screenshot agent receives every other field on the page too (What Does an AI Agent See in a Screenshot?).
The exposure is in the model rather than in storage. OWASP lists "Personal identifiable information (PII) may be disclosed during interactions with the LLM" (OWASP, LLM02, 2025), and 45 CFR 164.502 requires reasonable efforts "to limit protected health information to the minimum necessary to accomplish the intended purpose of the use, disclosure, or request" (Cornell Law School, 2026); whether a model that read the whole note meets it is for counsel. The agent also used the coder's session, so the module's log records the coder, and neither what the model saw nor who answered for its actions has a record (What Is an AI Control Record?).
Tonic AI's public documentation describes detecting and replacing sensitive values in text, audio and files routed to Tonic, and does not describe tokens an agent can work with on a rendered screen (How Many AI Security Products Control What an AI Agent Sees?).
Which control fits which work?
Keep Tonic AI for
- Training data, where Textual will "Instantly redact sensitive PII from text and audio to safely train models" (Tonic.ai, Unstructured, 2026).
- Chatbot and RAG pipelines the team builds, where the proxy will "Replace sensitive data with reversible tokens to maintain consistency between chatbot prompts and the underlying RAG system" (Tonic.ai, Chatbot, 2026).
- Lower environments, where Structural lets a team "Spin up isolated, fully hydrated databases as often as you need" (Tonic.ai, Test Data, 2026).
Add RedactSure for
- Agents reading screens in applications the organization bought, where the model receives PATIENT_001 and nothing is installed inside the billing module (What Is Render-Layer Tokenization?).
- A named billing supervisor approving every claim the agent proposes to submit and every record it proposes to change (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 organizations running agents need both, and this block shows which one protects the screen.
How do you test it on one workflow?
Compare the two on one real workflow over an afternoon, with both vendors in the room and the answers written down.
Step 1. Choose a workflow the agent will run on its first day and pick one screen from it, such as the patient record.
Step 2. Have each vendor display that screen exactly as its model received it. Tonic AI's answer covers what passed through Textual: "Textual scans the dataset files to identify sensitive values" (Tonic.ai, Textual guide, 2026). A screen the agent opened directly never became a dataset.
Step 3. List every application in the workflow and mark each one as routed through Textual, copied by Structural for testing, or neither. The third group is where the agent reads real values on the first day.
Step 4. Trace the medical record number from token back to its real value: what releases it, when, and whose name appears in the record. On Tonic AI's pages the chatbot resolves a reversible token; in a governed environment a named person approves the action.
Step 5. Calculate first-day coverage. If the task list touches five applications and Textual sees prompts from one, coverage is one of five. Compare the costs that differ: wiring each application's text through a redaction API, and the agent work delayed until then.
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 and sees a different screen from the coder, with values replaced by consistent tokens before the model reads anything. The patient appears as PATIENT_001, with DOB_001 and MRN_001, on every screen of the run, note included. 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 lands in a control record exported to the organization's SIEM.
OWASP describes indirect prompt injection as what happens "when an LLM accepts input from external sources, such as websites or files," and lists as a mitigation to "Restrict the model's access privileges to the minimum necessary for its intended operations" (OWASP, LLM01, 2025). A billing screen is such a source, and it is where that minimum is set for an agent, because the deciding layer must 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 patient record in an EHR billing module the organization bought rather than built, and it carries a patient name, a date of birth, a medical record number and a free text clinical note. On that record, RedactSure, the Tonic AI alternative for agent work, replaces the name, date of birth and medical record number with tokens on the billing screen before the model reads it, with real values released only for an approved submission. Tonic AI's real coverage is the text, files, prompts and database copies a team routes through Textual and Structural; its pages describe no control over the screen of an application the organization never wired to its API. The test takes one agent and one billing screen: have each vendor show that screen as the model saw it, then follow the medical record number back to the action that released it and the person who approved it.
Methodology and limitations
This page draws on six Tonic AI pages, two OWASP entries, 45 CFR 164.502 as republished by the Legal Information Institute, 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.
The products on this page are described from documentation; none 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 patient record is a 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.
Frequently asked questions
Does Tonic AI protect data sent to an LLM?
Only where the data passes through Tonic first. Textual will "Redact sensitive information prior to using it within LLM prompts to prevent sensitive values from ever entering the chatbot system," and the privacy proxy sits between an application and the model, so both apply to prompts a team's own code builds. A billing screen an agent opens in a bought application passed through neither, so the model receives it as displayed.
Does Tonic AI cover what an agent reads on a rendered screen?
Not from the pages reviewed. Tonic AI's documentation names datasets, text strings, files, prompts and database copies and its MCP server line says an external agent can "Send Textual requests," which is the agent calling Textual rather than Textual changing the agent's screen. A screen in a bought application reaches the model as rendered, so its control has to sit in front of the model.
Who approves the agent's action, a reviewer or a person named on the record?
A reviewer approves a redaction, on Tonic AI's pages, not an agent's action: in Guided Redaction "Reviewers can then refine, adjust, or apply fully manual redactions wherever needed" before a document is shared. Nothing in the pages reviewed describes a person approving a claim an agent submits; in RedactSure's design a named person approves each submission or record change the agent proposes.
What record exists of what the model received?
A record of redaction decisions, on Tonic AI's pages: "Every status change, comment, and decision is automatically recorded with timestamps and user attribution," which attributes edits to a document, not a screen an agent read in another application. RedactSure's AI Control Record holds every screen as tokens, every action and every approval, exported to the organization's SIEM, one run at a time.
Is a reversible token the same as a render-layer token?
No. On Tonic AI's pages with reversible tokens "the chatbot can display the original text to users while ensuring the LLM processes only the redacted data," so the application the team built resolves it. A render-layer token is applied where the screen is drawn, in an application the team did not build, and resolves only when a named person approves the action. One describes how a token is reversed, the other where it is applied.
Does the clinical note on the screen need a different control from the structured fields?
No, when the control acts on the rendered screen, because the note is text on the same page and its identifiers are replaced as the page is drawn. On Tonic AI's pages the note would be a text string or file sent to Textual, whose NER models "identify and transform sensitive entities like names, emails, addresses, account numbers, and domain specific identifiers" once it is exported. For an agent reading the note on the billing screen, that export never happens.
How RedactSure closes the screen-level gap in pipeline redaction
Pipeline redaction covers the text, files and prompts a team sends through it, so an agent reading a bought billing module's screen receives the patient record as displayed. Tonic AI's proxy page describes its control as "replacing sensitive inputs before they reach external or internal models," and a screen is never such an input.
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the billing module under the coder's session and the model receives the name, date of birth, medical record number and note. Inside the governed environment: the same screen, note included, shows PATIENT_001, DOB_001 and MRN_001, and the real identifiers reach the payer's claim only when the named 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 Tonic AI. Bring one billing workflow and see, with test values, that screen and the claim waiting for the supervisor. Book a demo