Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs Safetica: 2026 Comparison & Alternative
Safetica applies DLP policies to files, pasted content and prompts as employees share them; RedactSure, a Safetica alternative built for AI agents, tokenizes the rendered 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 buyer weighing a Safetica alternative for AI agent data protection asks where the policy sits, and the answer splits at the screen. Safetica applies DLP policies to files, pasted content and prompts as employees share them from a managed endpoint. RedactSure replaces the identifiers on the rendered page with consistent tokens before any model reads it, so the model never holds the patient's name.
Safetica's own pages describe DLP policies on files, pasted content and prompts, and the record that tests that coverage is a patient billing record in a revenue cycle platform the organization bought rather than built, holding a patient name, a date of birth, a member number and an open balance, opened by an agent that works the platform as a billing specialist does. What Is Render-Layer Tokenization? defines that approach, and What Does an AI Agent See in a Screenshot? describes what a computer-use agent takes in.
Key findings
- As a Safetica alternative for agent work, RedactSure replaces values on the rendered billing screen before any model reads it, so the agent works with PATIENT_001 and real values resolve only at an approved destination (What Is Render-Layer Tokenization?).
- A model that only ever received a token has nothing real to disclose, and OWASP lists "Personal identifiable information (PII) may be disclosed during interactions with the LLM" as the risk (OWASP, LLM02, 2025).
- The pages reviewed describe no replacement of values on a rendered screen; the control point is what an employee shares, "pasted text, prompts, and other data shared with ChatGPT, Claude, and Microsoft Copilot" (Safetica, AI Protection, 2026).
- Safetica's DLP page states the policy object: the product "protects sensitive data from accidental and intentional exposure with granular policies based on the data, user, application, and action involved" (Safetica, Data Loss, 2026).
- Its AI Protection page says Safetica will "Apply DLP policies to control sensitive files, pasted content, prompts, and risky user actions before data reaches AI" (Safetica, AI Protection, 2026).
- The platform page states that "All sensitive data operations are automatically monitored and logged," a log of operations rather than a record of what a model read (Safetica, Platform, 2026).
How do the two compare side by side?
The table sets Safetica's wording beside what a governed environment does, with the check to run on this record.
| Dimension | Safetica, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | "Apply DLP policies to control sensitive files, pasted content, prompts, and risky user actions" (Safetica, AI Protection, 2026) | On the rendered screen of the billing platform, before any model reads it | For one workflow, the exact step where a value is replaced |
| How coverage starts | "Maintain protection across supported desktop applications and Chromium-based browser experiences" (Safetica, AI Protection, 2026) | The agent works inside the governed environment; the platform is unchanged | How many of the agent's applications the endpoint agent reaches |
| What the model receives | "monitor and control sensitive file attachments, pasted text, prompts, and other data shared with ChatGPT, Claude, and Microsoft Copilot" (Safetica, AI Protection, 2026) | The screen with tokens, PATIENT_001, DOB_001 and MEMBER_001, consistent for the whole run | One screen, shown exactly as the model received it |
| How real values are restored | Security teams "can monitor, restrict, or block risky activity" (Safetica, Data Loss, 2026); restoring a value is not described | At the approved destination, when a named person approves the action | Who authorizes the release, and where it is recorded |
| Who approves an action | "Monitor activity, restrict sensitive data sharing, limit access for specific users or groups" (Safetica, AI Protection, 2026) | A named person approves every payment, submission and record change | A person's name on the balance adjustment, not a policy decision |
| The audit record | "Compile forensic evidence about user activities and company policy violations" (Safetica, Audit, 2026) | Every screen as tokens, every action and approval, in the AI Control Record and the customer's SIEM | Whether the record shows what the model saw |
Read down the two middle columns: Safetica governs what an employee shares with a supported assistant, complete for a file, a paste or a prompt its policy reaches and silent about what a screen shows the model. RedactSure governs what an agent reads on the rendered page, and its answer is the page the model was given.
Of the 86 AI security products reviewed, 13 are data loss prevention and insider risk products, and within that group 7 describe recording prompts or request payloads and 7 describe a SIEM export of AI activity, which places the audit record on what crossed a policy rather than on the screen an agent read (How Many AI Security Products Control What an AI Agent Sees?).
What does Safetica protect, and where does it act?
The compliance page calls Safetica "a data loss prevention and insider risk platform" (Safetica, Compliance, 2026), which the platform page fills in: "Data discovery and classification, data loss prevention, insider risk management, device control, and compliance reporting" (Safetica, Platform, 2026). For AI assistants the AI Protection page narrows the field to the data an employee shares: it will "Apply DLP policies to monitor and control sensitive file attachments, pasted text, prompts, and other data shared with ChatGPT, Claude, and Microsoft Copilot," with protection "across supported desktop applications and Chromium-based browser experiences on macOS and Windows" (Safetica, AI Protection, 2026).
Each benefit depends on one condition: the data crosses a place the endpoint agent, the browser extension or a connector can see, and a policy has already identified it. An agent that opens the revenue cycle platform is inside that reach only if the platform renders in one of those browsers, the policy covers that assistant, and the data passes the control. The pages reviewed name files, pasted content, prompts and sharing; a read of a rendered page is not among them.
What does an agent read when it opens a patient billing record in a revenue cycle platform?
The agent opens the record under the billing specialist's session: patient name, date of birth, member number and open balance. A computer-use agent takes in the whole page (What Does an AI Agent See in a Screenshot?), so the task needs the balance and the model receives the identifiers beside it.
No Safetica control on the pages reviewed sits between that screen and the model. The endpoint agent sees the browser process, and the AI Protection policy acts on what an employee pastes, uploads or types into a supported assistant (Safetica, AI Protection, 2026). Reading a rendered page is neither, because nothing leaves the browser: there is no transfer to classify or block. Runtime Tokenization vs DLP for AI Agents sets out the timing, since a detection control must identify content before it can act. Prompt inspection of what the agent sends is not described in the pages reviewed.
The exposure is the record itself. OWASP lists "unauthorized data access, privacy violations, and intellectual property breaches" among the outcomes of a disclosure during an LLM interaction (OWASP, LLM02, 2025), and a model holding the name and member number can repeat both into a summary. HIPAA's minimum-necessary standard requires that a covered entity "make reasonable efforts to limit protected health information to the minimum necessary" (Cornell Law School, 2026), and an agent that receives four fields for a task that needs one has not limited anything.
Which control fits which work?
Keep Safetica for
- Files and endpoints, where Safetica "protects data consistently across endpoints, email, and major cloud services" (Safetica, Platform, 2026).
- Employees pasting or typing into ChatGPT, Claude or Copilot, where the AI Protection policy acts "before data reaches AI" (Safetica, AI Protection, 2026).
- Insider investigations, where the audit page will "Compile forensic evidence about user activities and company policy violations" (Safetica, Audit, 2026).
Add RedactSure for
- Agents reading screens in applications the organization bought, where the model receives PATIENT_001 instead of the patient name (What Is Render-Layer Tokenization?).
- A named approver on every payment, submission and record change the agent proposes, with the name in the record (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 estates run both, one for what employees share, one for what an agent reads.
How do you test it on one workflow?
The test runs on one workflow, over an afternoon, with both vendors present and answers written down.
Step 1. Choose one screen from a workflow the agent will run, such as the patient billing record opened for a balance review, rather than a diagram of where data flows.
Step 2. Have each vendor produce the model input for that screen as received, not a summary of it. A policy report describes intent; the input shows whether the model held MEMBER_001 or the member number.
Step 3. List every application in the workflow and mark each as reached by the endpoint agent, by the browser extension, or by neither. The third group is the exposure on the first day, so do that count internally.
Step 4. Trace one value, such as the member number, from token back to its real value: what released it, when, and whose name appears in the record.
Step 5. Work out coverage. If the first task list touches five applications and two render in a supported browser with a policy written for that assistant, first-day coverage is two of five. Compare the two costs that differ: policy work for each new application, and agent work held back, and put both numbers beside the first-day count before either vendor quotes a scope.
What does a governed environment change?
A governed environment is a class of control, defined by where it acts: the agent runs under its own session, under its own identity. Before any model reads the billing screen, values are replaced with consistent tokens, so the patient appears as PATIENT_001 with DOB_001 and MEMBER_001 on every screen of the run. Real values resolve only at the approved destination at the moment of an approved action, a named person approves each balance adjustment, and every screen, action and approval goes to an exported record.
Research explains why the control sits in front of the model. OWASP states that "Indirect prompt injections occur when an LLM accepts input from external sources, such as websites or files" (OWASP, LLM01, 2025). The mitigation listed beside it is to "Restrict the model's access privileges to the minimum necessary for its intended operations" (OWASP, LLM01, 2025). A model that received PATIENT_001 has no name to repeat, which is the principle What Is Least Exposure? applies to the screen.
Jim Sinkoff (Deputy CEO and CFO, Sun River Health) described the adoption curve on a HAL podcast: "It's here to stay. It's part of the landscape. It's accelerating. And in that acceleration is also a great deal of tripwires." (HAL Transcript: Jim Sinkoff)
What the record shows
On the billing screen, tokens replace the patient's name, date of birth, member number and open balance before the model reads the page, and the real values appear only when a named person approves the action, with a record that separates the agent's read from the specialist's. Safetica's real coverage is data leaving through a channel its endpoint control watches, where its insider risk page "blocks data exfiltration through diverse channels, including cloud services, email, websites" (Safetica, Insider, 2026). Take one balance review on a test record, ask each vendor for the model input from that screen, then trace the member number to the action that released it and the name written on the approval.
Methodology and limitations
This page rests on five Safetica pages, two OWASP Top 10 for LLM Applications entries, one regulation 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. Public documentation read in full gives a sound view of where each product applies its control, and "not described" 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. Documentation describes intended design, both products change, and the page carries its date. RedactSure is deploying now with enterprise and government customers. The patient billing record is an illustrative record type. Compliance determinations belong to counsel, assessors and the organization's security team, and nothing here replaces a pilot of one workflow.
Frequently asked questions
Does Safetica protect data sent to an LLM?
Only where the data crosses something Safetica inspects. Its AI Protection page applies DLP policies to file attachments, pasted text and prompts sent to an assistant, and its insider risk page blocks exfiltration over cloud services, email and websites. Both act on a transfer. An agent that reads a billing screen builds its prompt from the rendered page, so the record is inside that prompt before any transfer the policy can see.
Does Safetica cover what an agent reads on a revenue cycle platform's screen?
Not from the pages reviewed. The five pages describe protection for files, email and cloud sharing, and for data an employee pastes or types into a supported assistant. An agent reading a rendered page in a platform the organization bought takes none of those paths. A browser extension sits in the Chromium session, but its policy object is still what the employee sends.
Who approves the agent's action, a policy or a person?
A policy, on Safetica's pages. The DLP page describes granular policies based on the data, user, application and action, and the AI Protection page sets controls by application, data sensitivity, employee and department. That decision is written in advance for a category of activity. In RedactSure's design a named person approves each payment, submission or record change, and the approval carries that name.
What record shows that an agent, and not the billing specialist, read the patient record?
None on Safetica's pages for a page read. The platform page says all sensitive data operations are monitored and logged, and the audit page compiles forensic evidence about user activities. A page read under the specialist's session leaves the specialist's name in the platform log. RedactSure's AI Control Record holds every screen as tokens and every action under the agent's own identity.
Can a hidden instruction in a billing note make the agent leak the record?
Only if the model holds something real to leak. A note field accepts text from anyone who can write to the record, so a billing screen is an input path for injection. A model that received PATIENT_001 and MEMBER_001 can be steered and still has nothing real to send. What the page showed the model before the note arrived decides the answer.
Is a browser extension the same control as the screen an agent reads?
No. A browser extension governs what a person, or an agent driving that browser, does inside a Chromium session: what it pastes, uploads or types into a supported assistant, the activity the AI Protection page describes. A screen-level control governs what the model receives from the rendered page, whether that page is a chat window or a billing platform. Ask each vendor which one its product is; Enterprise Browser or Render-Layer Tokenization? draws the distinction.
How RedactSure closes the screen-level gap in endpoint data protection
An endpoint policy covers what an employee shares with an assistant, so an agent that reads a billing screen hands the model the whole record. Safetica's AI Protection page names files, pasted content and prompts as what its policies control.
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the platform under the specialist's session, and the model receives the patient name, date of birth, member number and balance. Inside the governed environment: the same screen shows PATIENT_001, DOB_001 and MEMBER_001, the balance reaches the account only when the named specialist approves it, and screen, action and approval land in the AI Control Record.
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 Safetica. Bring one billing workflow and see, with test values, the record the model receives and the approval behind the change. Book a demo