Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs Amazon Comprehend: 2026 Comparison & Alternative
Amazon Comprehend locates or redacts PII in text an application submits, while RedactSure, an Amazon Comprehend alternative for AI agents, tokenizes the screen before a 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 support organization weighing an Amazon Comprehend alternative settles three questions before it buys: what the model receives from the console screen, who approves the change the agent proposes, and what record shows what it read. Comprehend's control sits in text an application sends to its API and in documents stored in Amazon S3. A support console draws its own screen, and no API call happens when an agent reads it.
This page quotes Amazon Comprehend's developer guide and keeps one record throughout: a customer record in a support ticketing console the organization bought rather than built, holding a name, an email address, a phone number and the last four digits of a card number, opened by an agent that works the ticket queue the way a support analyst does. What Is Render-Layer Tokenization? defines the control that replaces those values before the model reads them.
Key findings
- As an Amazon Comprehend alternative for agent work, RedactSure replaces the values on the rendered screen with consistent tokens, so the agent works the ticket as CUST_001 and CARD_001 and the real values stay out of the model (What Is Render-Layer Tokenization?).
- A model that reads a support screen receives a name, an email address, a phone number and a card's last four digits whether or not the task needs them, the disclosure OWASP records when it says "Personal identifiable information (PII) may be disclosed during interactions with the LLM" (OWASP, LLM02, 2025).
- None of the Comprehend pages reviewed describes a control over what a rendered application screen contains when a model reads it, because its PII redaction belongs to a batch job over stored text (Amazon Web Services, Personally identifiable, 2026).
- Comprehend locates entities on demand: "To locate PII entities, you can use real-time analysis or an asynchronous batch job," while redaction takes the slower path, since "To redact the PII entities, you must use an asynchronous batch job" (Amazon Web Services, Personally identifiable, 2026).
- Redaction returns "a copy of the input text with redactions for each PII entity," and the service "recognizes credit or debit card numbers when only the last four digits are present," which covers this page's card digits (Amazon Web Services, PII entities, 2026).
How do the two compare side by side?
The table sets Amazon Comprehend's own wording beside a render-layer control on the same six dimensions.
| Dimension | Amazon Comprehend, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | "You simply call the Amazon Comprehend APIs in your application and provide the location of the source document or text" (Amazon Web Services, Features, 2026) | On the screen the agent works in, before any model reads it | The point where a value is replaced |
| How coverage starts | "You can run real-time analysis for small workloads or you can start asynchronous analysis jobs for large document sets" (Amazon Web Services, What is, 2026) | The agent works inside the governed environment; the bought application is unchanged | How many of the agent's applications call the API today |
| What the model receives | The text itself, up to "100 kilobytes of UTF-8 encoded characters" (Amazon Web Services, PII entities, 2026) | The screen with tokens, CUST_001, EMAIL_001 and CARD_001, consistent for the whole run | One screen, shown exactly as the model received it |
| How real values are restored | A returned copy where the value is replaced by its entity type or a chosen character, "Replace with PII entity" or "Replace with character" (Amazon Web Services, 2020) | Released at an approved destination when a named person approves the action | What releases it, and where that is recorded |
| Who approves an action | A DetectPiiEntities call returns locations and scores, and what the call does with them is the application's code (Amazon Web Services, Real-time, 2026) | A named person approves every payment, submission and record change | A person's name, not a rule |
| The audit record | Batch results are stored in "the output Amazon S3 location that you specified for the job" (Amazon Web Services, Asynchronous, 2026) | Every screen as tokens, every action and every approval, exported to the customer's SIEM | Whether the record shows what the model saw |
Read down the middle columns and the difference is the object each product governs. Comprehend governs the text that reaches its API, so its answer to what the model saw covers the documents a team sends and is silent about the screens nobody sends. RedactSure governs what an agent reads, and its answer is one record of the screen as the model read it. They differ on who turns a value back into a real one: the calling application on Comprehend's pages, a named person in RedactSure's design, which What Is Supervised Delegation? describes.
What does Amazon Comprehend protect, and where does it act?
Comprehend is a natural language processing service, and its pages are precise about the work: it will "detect personally identifiable information (PII) in English or Spanish text documents" (Amazon Web Services, Personally identifiable, 2026). The unit of work is a document, submitted as text or named as an S3 object for a batch job. For a support knowledge base, the documented pattern is to redact the text before indexing the documents in the search index. A team that controls the data path gets a working control over stored documents.
Every benefit depends on one condition: the data must reach the service. The call comes from the customer's application, which supplies the text or its location, so an application the organization bought and cannot change is outside the path unless its vendor wrote the call. A console screen drawn in a browser is not a document submitted anywhere, so the design ends at the API call. The limit runs through the category: 0 of 86 AI security and data protection products describe a token an agent can use in place of a value a screen shows, on documentation read between October 22 and 27, 2026 (How Many AI Security Products Control What an AI Agent Sees?).
What does an agent read when it opens a support ticket?
The record in the ticketing console is drawn from the application's own database, and the console shows the customer name, the email address, the phone number and the last four card digits in the ticket and the sidebar. The agent reads the screen the way a support analyst does: the fields the task needs, the fields it does not, the notification banner and the reply box (What Does an AI Agent See in a Screenshot?). None of that is submitted to Comprehend, though a summary a person pastes into a ticket may be, and the screen itself is not.
The card digits are the pointed case. Comprehend "recognizes credit or debit card numbers when only the last four digits are present" (Amazon Web Services, PII entities, 2026), so a team that sends ticket text through the API can redact them there. On the console the digits are rendered for the analyst, and the model reads them with the rest of the page. Redaction in the copy removes the value for good, while a consistent token keeps the record matchable across screens (Runtime Tokenization vs Redaction for AI Agents).
Which control fits which work?
Keep Amazon Comprehend for
- Preparing a corpus the team controls, where the documented pattern is to "redact the text before you index the documents in the search solution" (Amazon Web Services, Features, 2026).
- Bulk document sets, since "you can run real-time analysis for small workloads or you can start asynchronous analysis jobs for large document sets" (Amazon Web Services, What is, 2026).
- A redacted release of a document, choosing "Replace with PII entity" or "Replace with character" (Amazon Web Services, 2020).
Add RedactSure for
- Agents reading screens in applications the organization bought, where the model receives CUST_001 instead of the real value (What Is Render-Layer Tokenization?).
- A named approver for every payment, submission and 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: Comprehend for submitted text, a screen control for the console.
How do you test it on one workflow?
Run the comparison on one real workflow, over an afternoon, with both vendors present.
Step 1. Choose a task the agent will run, such as answering a billing question, and pick one screen from it, the customer record as the console renders it.
Step 2. Ask each vendor to show the screen exactly as the model read it, and write down what comes back. The output settles the question, because it shows whether the model read real values or tokens; How Do Data Masking and AI Tokenization Differ? explains why a followed token differs from a masked value.
Step 3. List every application in the workflow, the console, the billing system and the payment page, and mark each as covered by the vendor's control or not; the rest are the first-day exposure.
Step 4. Trace one value, the card's last four digits or the customer's email address, from token back to its real value: what released it, when, and whose name is in the record. On Comprehend's pages that step belongs to the calling application's code; on a render-layer control it is a person.
Step 5. Calculate first-day coverage. Five applications touched and one wired to the API is one of five. Compare the costs that differ, the integration work per application and the delay to the agent's work.
What does a governed environment change?
A governed environment is a class of control rather than a feature of one product. The agent works under its own session and identity, and it sees a different screen from the analyst because values are replaced with consistent tokens before the model reads anything. The customer appears as CUST_001, with EMAIL_001 and CARD_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 payment, submission and record change, and every screen, action and approval is written to a record that exports to the organization's SIEM.
The research record explains why the control sits in front of the model. OWASP says "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 CUST_001 cannot be talked into disclosing a phone number it never received, which is the principle in What Is Least Exposure?.
What the record shows
The customer's name, email address, phone number and card digits become tokens on the console screen before the model reads them, and that is the job RedactSure, as the Amazon Comprehend alternative for agent work, does for this record, with real values released only for an approved action and a record kept apart from the analyst's session. Amazon Comprehend's real coverage is the text and documents that reach its API, where a batch job returns "a copy of the input text with redactions for each PII entity" (Amazon Web Services, PII entities, 2026); its pages describe no control over what a model reads on a screen the service never receives. Put the test on one ticket and one screen: ask each vendor to show the record the model was given, then follow the card's last four digits back to the step that restored them.
Methodology and limitations
This page draws on seven Amazon Comprehend pages, two OWASP entries and 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. 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 customer 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 Amazon Comprehend protect the data an agent sends to a model?
Only in the workflow that sends the data to it. Comprehend's pages describe PII detection on text and documents submitted to its API or read from Amazon S3, with redaction run as a batch job. When the model is an agent reading a screen in a purchased application, the data never passes through that call. The protection covers the pipeline a team built and stops at the edge of the screen.
Does Amazon Comprehend cover what an agent reads on a purchased application's screen?
Not from the pages reviewed. The service works on text and documents supplied to it; a support console draws its own screen from its own database, and that rendering is not a document the customer submits. A ticket summary that a person types into a field may reach the API. The gap is the screen an agent reads before anything is written to a file or sent to an API.
Who approves the agent's action, the API caller or a person?
The calling application, on Comprehend's pages. A detect call returns entity types, scores and offsets to the calling code, and a batch job writes a redacted copy to the S3 location the caller named; no approval step sits between that output and what happens next. In RedactSure's design a named person approves each payment, submission or record change the agent proposes. Ask each vendor whose name appears beside a restored value.
Will our application logs show that an agent read a customer record?
Only as the analyst whose session the agent used. The console logs the signed-in user, and a service that never saw the screen has nothing to record about it. A record that separates the agent's actions from the person's has to come from that environment. RedactSure's AI Control Record holds every screen as tokens, every action and every approval, exported to the organization's SIEM.
Is redaction the same as render-layer tokenization?
No. Redaction removes the value from the copy, and the copy that comes back from a batch job carries a marker where the value stood. Render-layer tokenization substitutes a consistent token on the screen, so two mentions of the same customer stay linked for the model without the identifier. A team can do the first well and still need a control on the screen.
Do the last four digits of a card count as sensitive data for Comprehend?
Yes. Comprehend's documentation lists card numbers as a PII type it recognizes, including the case where only the last four digits are present, so a ticket summary that quotes them can be redacted through the API. On the support console the digits are rendered for the analyst, and an agent reading the screen receives them with the rest of the record. The control that covers both is applied before a value reaches a model.
How RedactSure closes the screen-level gap in text redaction
Comprehend's control covers the text and documents its API receives, so an agent reading a support console never passes through it. Its own documentation says that to redact PII "you must use an asynchronous batch job."
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the console under the analyst's session, the ticket shows the name, the email address, the phone number and the card digits, and the model receives all four. Inside the governed environment: the same ticket shows CUST_001, EMAIL_001 and CARD_001, and the card digits reach the billing page only on the named analyst's approval.
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 Amazon Comprehend. Bring one ticket workflow and see, with test values, the record as the model receives it and the pending approval. Book a demo