Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs Azure Language (Azure AI Language): 2026 Comparison & Alternative
Azure Language redacts sensitive data in text an application sends to its API; RedactSure, an Azure Language alternative built for AI agents, tokenizes identifiers on screen before any 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.
A team choosing an Azure Language alternative for agent work decides one thing first: where the identifier stops being readable. Azure Language places its control in the call an application makes and returns redacted output to the caller (Microsoft, PII detection, 2026). RedactSure places its control on the rendered chart, where the patient name, date of birth and medical record number become tokens before any model reads the screen.
Microsoft's pages use two names, Azure Language in Foundry Tools and Azure AI Language, and none reviewed states a rename. This page quotes them and uses one record: a patient record in an electronic health record system the organization bought rather than built, holding a patient name, a date of birth, a medical record number and a diagnosis, opened by an agent that works the chart the way a medical coder does. What Is Render-Layer Tokenization? defines the approach.
Key findings
- As an Azure Language alternative for agent work, RedactSure replaces values on the rendered chart with consistent tokens before any model reads it, so the agent works with PATIENT_001 and MRN_001 and values are released only at the approved destination (What Is Render-Layer Tokenization?).
- A model that receives a value has disclosed it to its own context, and no storage control changes that; OWASP records that "Personal identifiable information (PII) may be disclosed during interactions with the LLM" (OWASP, LLM02, 2025).
- None of Azure Language's PII pages describes a control over what a rendered screen shows an agent, and the same pages say the service "helps you identify, classify, and redact sensitive data across text, conversations, and native documents" (Microsoft, PII detection, 2026).
- For text, redaction happens where the application already handles it, "logs, prompts, forms, chat messages, or other text content," so teams can "apply PII controls inline in application and data-processing workflows" (Microsoft, redaction overview, 2026).
- Native documents move through storage jobs, where a caller will "Submit a job with source and target storage locations" and collect "A redacted document in your target storage container" (Microsoft, Document-based, 2026).
How do the two compare side by side?
Azure Language's own wording sits beside what a governed environment does on the same point, with the check a buyer can run.
| Dimension | Azure Language, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | "Submit input text to the service and receive structured output" (Microsoft, PII detection, 2026) | On the screen the agent works in, before any model reads it | Where a value is replaced |
| How coverage starts | "Create an Azure Language in Foundry Tools resource" (Microsoft, PII detection, 2026) | The agent works inside the governed environment; the bought application is unchanged | How many applications carry the control today |
| What the model receives | "when your application handles logs, prompts, forms, chat messages" (Microsoft, redaction overview, 2026) | Consistent tokens, PATIENT_001 and MRN_001, for the whole run | One screen as the model received it |
| How real values are restored | "No data is stored in your account" (Microsoft, from text, 2026) | Released at an approved destination when a named person approves | What releases it, and where |
| Who approves an action | No approval step on the pages reviewed; the caller must "handle the API output in your application" (Microsoft, PII detection, 2026) | A named person approves every chart change and claim submission | A name beside each action |
| The audit record | "Your processed documents are located in your Azure Blob Storage target container" (Microsoft, native documents, 2026) | Every screen as tokens, every action and approval, in a control record exported to the SIEM | Whether the record shows what the model saw |
Azure Language governs the text, conversation items and documents an application submits, so its answer covers the paths wired to the service and stops at the screen. RedactSure governs what an agent reads on the rendered chart, with nothing placed inside the EHR. They also differ on who turns a token back into a value: the calling application on Azure Language's pages, a named person in RedactSure's design, as What Is Supervised Delegation? sets out.
What does Azure Language protect, and where does it act?
Azure Language's PII pages describe a detection service with a wide remit. A caller sends text, conversation items or a document job and receives entities and redacted output. On the text path the service "helps you detect and redact sensitive data in raw text strings" (Microsoft, redaction overview, 2026), and a document job returns "A redacted document in your target storage container" (Microsoft, Document-based, 2026).
Detection can also be adapted with "custom synonyms for specific entity types" and a value exclusion policy, with custom regex only in the Text PII container (Microsoft, Adapt, 2026). Each benefit depends on one condition: the content has to be submitted to the service, and the caller handles the output in its own code. For a team that owns those pipelines, that is a sound place for the control. Jim Sinkoff, Deputy CEO and CFO, Sun River Health, said on the podcast that the API boundary is where the exposure starts: "That API layer is obviously the most sensitive point of penetration where AI can begin to spread" (Who Owns the Data When AI Touches Patient Records? A Health System CFO on AI Governance). The pages reviewed describe no control over what a rendered chart contains at the moment an agent reads it, and no approval step after a model proposes an action; the boundary is the API, and the screen sits past it.
What does an agent read when it opens a patient record?
The chart the agent opens is drawn by the EHR's own code. A medical coder signs in and reads the patient name, date of birth, medical record number and diagnosis; an agent working the chart the same way reads the same page, including fields the task does not need and any sidebar the vendor renders, as What Does an AI Agent See in a Screenshot? sets out. The service acts on what a caller submits, and nothing on that page was submitted. No AI agent reviewed describes replacing the customer data a chart shows, including a patient name or a medical record number, with tokens it can work with, and the tally is 0 of 15, from documentation rather than a measured test (Do AI Agents Ask Before Making a Payment?). Its document path does not reach it either: "Digital images with embedded text aren't supported" (Microsoft, native documents, 2026), and a live screen is not a storage job.
The model holds the whole chart from the moment it reads the screen, and OWASP warns that "Personal identifiable information (PII) may be disclosed during interactions with the LLM" (OWASP, LLM02, 2025). For health data, the Privacy Rule requires covered entities to "make reasonable efforts to limit protected health information to the minimum necessary" (Cornell Law School, 2026). There is an identity gap as well: the agent borrowed the coder's session, so the EHR's audit trail records the coder, and nothing in that trail says a model read the chart.
Which control fits which work?
Keep Azure Language for
- Inline redaction of text an application already handles, in code the team writes (Microsoft, PII detection, 2026).
- Native documents held in storage, where a job returns "A redacted document in your target storage container" (Microsoft, Document-based, 2026).
- Teams that need the detector tuned to their domain, where "custom synonyms for specific entity types" adapt it (Microsoft, Adapt, 2026).
Add RedactSure for
- Agents reading screens in applications the organization bought, where the model receives PATIENT_001 instead of the real value (What Is Render-Layer Tokenization?).
- A named person approving each chart change, code assignment or claim submission 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?).
Most organizations running agents end up needing both: Azure Language for submitted text, a screen control for the chart.
How do you test it on one workflow?
Run the comparison on one workflow that both vendors can see, in a single afternoon, with the answers written down.
Step 1. Choose a task the agent will run, such as preparing a chart for coding, 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 read it, not a diagram of the data flow; the output itself shows whether the model saw real values or tokens, and How Do Data Masking and AI Tokenization Differ? explains why a masked value and a token answer different questions.
Step 3. List every application in the workflow, the EHR, the coding queue and the payer portal, and mark each as covered by that vendor's control or not; the uncovered group is the first-day exposure.
Step 4. Trace the medical record number from token back to its real value: what releases it, when, and whose name is in the record. On Azure Language's pages that step belongs to the calling application; on a render-layer control it is a person.
Step 5. Calculate first-day coverage: six applications touched and one wired to the service is one of six. Compare the integration work and the agent work delayed until it exists.
What does a governed environment change?
A governed environment is a category of control rather than one product feature. For each task the planner proposes what to tokenize, starting from personal information, account numbers, credentials and record numbers.The agent runs under its own session, recorded under its own identity, and it reads a chart the coder would not recognize: values are replaced with consistent tokens before the model sees them. The patient appears as PATIENT_001, with DOB_001 and MRN_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 chart change, code assignment and claim submission, 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 that the 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 be steered into disclosing a medical record number it never received, the principle in What Is Least Exposure?: least privilege governs what an agent may do, and the screen it reads is a separate question.
What the record shows
On this record the control that decides the exposure is the one between the chart and the model, and a render-layer control replaces the patient name, date of birth and medical record number with tokens before anything reads the page. That is the job RedactSure, as the Azure Language alternative for agent work, does here: PATIENT_001, DOB_001 and MRN_001 on the chart, values released only for an approved chart change, and a record that separates the agent's reading from the coder's. Azure Language's coverage is the text, conversation items and native documents an application submits, and its pages describe no control over what a model reads on a screen the organization never wired to that service. One chart and one code change are enough for the test: ask each vendor to show the record the model was given, then follow the medical record number to the step that restored it and the name on that approval.
Methodology and limitations
This page draws on six Microsoft Learn pages, two OWASP entries and one regulation text, plus 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. Public documentation read in full gives a sound view of where each product applies its control; "does not describe" means the pages reviewed.
Neither product was tested for this page, and both are described from documentation that changes, so 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, including whether a control satisfies the minimum necessary standard, belong to the organization's counsel and assessors, and nothing here replaces a pilot of one workflow on its own screens.
Frequently asked questions
Does Azure Language protect the data an application sends to the model?
Only in the pipeline that calls it. Its text page describes redaction for raw text strings and says the feature fits an application that handles "logs, prompts, forms, chat messages, or other text content directly." Prompt and response filtering runs in the same place. When an agent opens a chart in a bought application, that text never passes through the call.
Does Azure Language cover what an agent reads on a rendered screen?
Not from the pages reviewed. The service takes text, conversation items or a document job and returns entities and redacted output to the caller. Its document path begins when a caller submits "a job with source and target storage locations" (Microsoft, Document-based, 2026), and a live chart in an EHR is none of those inputs.
Who approves the agent's action?
No one, from the pages reviewed. Azure Language returns redacted output, and the application that called it decides what happens next. An approval in that design is code the team writes around the response, not a step a named person takes on the record. In a governed design the agent's proposed chart change waits for a named coder or supervisor.
What record does Azure Language leave of what a model read?
A record of the job, not of the model. Synchronous use is stateless: "No data is stored in your account, and results are returned immediately in the response" (Microsoft, from text, 2026). Document jobs leave the redacted file in "your Azure Blob Storage target container." Neither artifact says which screen an agent opened or who approved the action that followed.
How is PII redaction different from tokenization on the screen?
Redaction replaces a value in text that was submitted, and the caller gets back a new string with entities removed or substituted; the original is held wherever the application kept it. Render-layer tokenization substitutes on the page itself, before the model reads it, with one token per patient across screens, as Runtime Tokenization vs Redaction for AI Agents sets out.
Can a prompt injection reach a patient record through the agent?
Only as far as the tokens the model already holds. A model that read PATIENT_001 can be manipulated about PATIENT_001 and cannot disclose a medical record number it never received (OWASP, LLM02, 2025). OWASP records that indirect injections arrive through "external sources, such as websites or files," which is what an agent reading a bought application's page encounters.
How RedactSure closes the screen-level gap in PII redaction
Azure Language controls text an application submits, so an agent working a chart in a bought system reads the record outside that control. Its own pages describe a pipeline control, "so you can apply PII controls inline in application and data-processing workflows."
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the chart under the coder's session and the model receives the patient name, date of birth, medical record number and diagnosis. Inside the governed environment: the same chart shows PATIENT_001, DOB_001, MRN_001 and DX_001, and the real medical record number reaches the coding queue only when the named coding supervisor approves the change.
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 Azure Language. Bring one chart workflow and see, with test values, what the model receives and who approves it. Book a demo