Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs Trellix: 2026 Comparison & Alternative
Trellix DLP classifies sensitive data and controls its movement across endpoints and storage, while RedactSure, a Trellix 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 Trellix alternative for AI agents find that the two products answer different questions. Trellix DLP asks whether data a policy recognizes is leaving through a channel it watches. RedactSure asks what the agent's model is about to read on the page in front of it, and replaces sensitive values with tokens before the model receives that page.
This page quotes Trellix's documentation and uses one example throughout: an employee record in a human resources system the organization bought rather than built, holding a name, a home address, a salary and a national identification number, opened by an agent that works the system as an HR specialist would. What Is Render-Layer Tokenization? defines the approach, and Runtime Tokenization vs DLP for AI Agents explains why DLP acts only on a transfer it has recognized.
Key findings
- As a Trellix alternative for agent work, RedactSure replaces values on the HR page before any model reads it; the agent handles the employee as EMP_001 and the real value is released only at an approved destination (What Is Render-Layer Tokenization?).
- A model that received only tokens holds nothing real to disclose, the risk OWASP names: "Personal identifiable information (PII) may be disclosed during interactions with the LLM" (OWASP, LLM02, 2025).
- The record is a second difference: an AI Control Record keeps every screen as tokens, every action and every approval, in the customer's SIEM (What Is an AI Control Record?).
- Trellix's data sheet says "Trellix DLP protects the most common enterprise data loss vectors across endpoints, email, the web, networks, and data storage," a control on where data moves, not on what a model reads from a page (Trellix, Data Loss Prevention data sheet, 2026).
- For AI tools, the AI Data Risk Dashboard watches "copy/paste, clipboard sharing, and file uploads" "using existing DLP policies to ensure sensitive data is not leaked into" unauthorized platforms; an agent reading a page is none of those (Trellix, AI Data Risk Dashboard, 2026).
- The product page describes protection "from the keyboard to the cloud" that will "Prevent data exfiltration. Coach end users attempting to violate policy," and no page treats an AI agent as distinct from a user (Trellix, Data Loss Prevention product page, 2026).
How do the two compare side by side?
The table sets Trellix's wording beside what a governed environment does on each point, with the check a buyer can run.
| Dimension | Trellix, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | "Protects sensitive and proprietary data on Windows and macOS workstations and servers from data exfiltration," plus email and web gateways (Trellix, Data Loss Prevention data sheet, 2026) | On the screen the agent works in, before any model reads it | For one workflow, where a value is replaced or blocked |
| How coverage starts | "Find and classify sensitive data across more than 400 file types," then policies across the environment (Trellix, Data Loss Prevention product page, 2026) | The agent works inside the governed environment; the HR system is unchanged | Whether the HR system's pages fall under any policy today |
| What the model receives | Whatever a user shares that no policy blocks; the dashboard acts on "copy/paste, clipboard sharing, and file uploads" (Trellix, AI Data Risk Dashboard, 2026) | The page with tokens, such as EMP_001 and NID_001, consistent for the run | One page, exactly what the model received |
| How real values are restored | Blocked data stays put; encrypted data opens for "only authorized users" (Trellix, Data Encryption, 2026) | Released at an approved destination when a named person approves the action | Who authorizes it, and where it is recorded |
| Who approves an action | A policy, which will "Coach end users attempting to violate policy" (Trellix, Data Loss Prevention product page, 2026) | A named person approves every payment, submission and record change | A person's name, not a rule |
| The audit record | Incidents and "comprehensive activity monitoring to detect and address data events" (Trellix, Data Security Suites, 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 differ in the object each product governs. Trellix governs data its policies recognize, on the devices and channels they cover, so its answer to what a model saw is a list of transfers. RedactSure governs what an agent reads, and its answer is the page as it reached the model. They also differ on who restores a real value: a policy on Trellix's pages, a named person in RedactSure's design (What Is Supervised Delegation?).
What does Trellix protect, and where does it act?
Trellix DLP starts from classification; its data sheet says "organizations can find sensitive information and intellectual property, classify only the data that requires added protection, determine policies to be applied across the environment, and detect inappropriate activity for remediation" (Trellix, Data Loss Prevention data sheet, 2026). The Data Security Suites "extend protection across images, PDFs, and scanned document files" through OCR (Trellix, Data Security Suites, 2026), and Data Encryption keeps a lost device unreadable: "Data on devices and removable media is protected if lost or stolen" (Trellix, Data Encryption, 2026). A file that leaves by email, upload or removable drive meets a control that has already decided what it is.
Each benefit depends on two conditions: a policy recognizes the data, and the data moves through a place the suite inspects. The product page notes that Trellix DLP "extends protection to content in Google Chrome Enterprise, Microsoft Edge for Business, Firefox, and Island," which reaches what a person does in a browser; the pages do not say what happens when the reader is a model (Trellix, Data Loss Prevention product page, 2026). That is a boundary of the design, not a defect: a control built to stop data leaving has no step that changes what an application displays.
What does an agent read when it opens an employee record?
Consider the employee record. The HR system is licensed software, and the endpoint agent on the workstation may be watching every channel the policy names. The agent does not copy, paste or upload anything. It opens the page the way an HR specialist does and reads the name, the home address, the salary and the national identification number. Trellix's AI pages describe the exposure as a user action: "Organizations lack visibility into how and where users are sharing sensitive information with AI tools" (Trellix, Data Protection for AI Risk, 2026). Reading is not sharing, so the policy has nothing to match, and the model receives all four values.
The exposure sits in the model; OWASP names the result: "This can result in unauthorized data access, privacy violations, and intellectual property breaches" (OWASP, LLM02, 2025). An agent working from screenshots takes in everything visible, including fields the task never needs (What Does an AI Agent See in a Screenshot?). There is also an identity gap: the agent used the specialist's session, so the HR log names the specialist, and an agent can pass every permission check and still hold every record it was shown (What Is Least Exposure?).
Which control fits which work?
Keep Trellix for
- Data crossing its channels, where "Trellix DLP protects the most common enterprise data loss vectors across endpoints, email, the web, networks, and data storage" (Trellix, Data Loss Prevention data sheet, 2026).
- People pasting or uploading data into AI tools, where the dashboard works "against unauthorized data sharing, enabling your team to block" it (Trellix, AI Data Risk Dashboard, 2026).
- Lost laptops, since its encryption "products secure devices and removable media to ensure that only authorized users can access the information they contain" (Trellix, Data Encryption, 2026).
Add RedactSure for
- Agents reading pages in bought applications, where the model receives EMP_001 and NID_001, not the real values (What Is Render-Layer Tokenization?).
- A named person approving each pay change, submission or record update (What Is Supervised Delegation?).
- The record of what the model received on every page, with each action and approval, in the SIEM (What Is an AI Control Record?).
Most organizations running agents need both, and this block shows which product protects the screen.
How do you test it on one workflow?
Put both vendors in front of one real HR workflow for an afternoon and write every answer down.
Step 1. Choose a workflow the agent will run in the HR system, such as a pay change, and pick one page from it, the employee record, not a data-flow diagram.
Step 2. Ask each vendor to show that page exactly as it reached the model: either the real values or tokens standing in for them. That output answers the question on its own.
Step 3. List every application in the workflow, the HR system, payroll, the ticketing tool, and mark each as covered by an endpoint, gateway or discovery policy or not. The uncovered group is the first-day gap.
Step 4. Trace the identification number from the page to the action that used it: what released it to payroll, when, and whose name is in the record. If the only name is the specialist whose session the agent borrowed, the record cannot separate the two.
Step 5. Calculate first-day coverage. If the workflow touches five applications and two fall under a policy, coverage is two of five. Compare the costs that differ: policy work in each remaining application, and agent work delayed until it is done, so both figures sit beside the coverage count and are read together.
What does a governed environment change?
A governed environment is a category of control, not a product feature. For each task the planner proposes what to tokenize, starting from personal information, account numbers, credentials and record numbers.The agent works under its own session and identity, and it sees a different page from the specialist because values are replaced with consistent tokens before the model reads anything. The employee appears as EMP_001, with ADDR_001 and NID_001, on every page 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 control record exported to the SIEM.
OWASP explains why the control sits in front of the model: "Indirect prompt injections occur when an LLM accepts input from external sources, such as websites or files," and its listed 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 received NID_001 cannot be talked into disclosing a number it never held, so the control does not depend on the model's behavior (Does Prompt Injection Expose Customer Records?).
None of the 86 AI security products reviewed describes giving an agent tokens it can work with in place of the sensitive values it reads on a screen (How Many AI Security Products Control What an AI Agent Sees?).
What the record shows
Who decides what the model receives, or whose name is on the release? Render-layer tokenization replaces the employee's name, address, salary and identification number with tokens on the HR page before the model reads it, the job RedactSure, as the Trellix alternative for agent work, does for this record, with real values released only for an approved pay change and a record kept apart from the specialist's. Trellix's real coverage is data its policies recognize on the endpoints, gateways and storage they reach, plus what users paste and upload into AI tools; its pages describe no control over what a model reads on a licensed application's page. The test takes one employee record: open it with the agent, ask each vendor for the model's input from that page, then trace the identification number to the action that released it and the name that approved it.
Methodology and limitations
This page draws on six Trellix 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.
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 employee record is a record type, not an organization. Compliance determinations belong to counsel, assessors and the organization's security team; nothing here replaces a pilot of one workflow on its own screens.
Frequently asked questions
Does Trellix DLP protect data sent to an LLM?
Only where a user moves it. Trellix's AI Data Risk Dashboard applies existing DLP policies to copy and paste, clipboard sharing and file uploads into AI platforms, and its solution page frames the risk as users sharing data with AI tools. Both act on a transfer a policy recognizes. When an agent reads a page in the HR system, no such transfer occurs, so the data reaches the model unseen by the control.
Does Trellix cover what an agent reads on the HR system's page?
Not from the pages reviewed. Trellix's pages describe discovery and classification across file types, endpoint protection on Windows and macOS, email and web gateways, and inspection in managed browsers. Each acts on recognized data somewhere the suite inspects. A rendered page an agent reads in licensed software is not among those places, so it needs a control placed in front of the model.
Who approves the agent's action, a policy or a person?
A policy, on Trellix's pages, which say the product blocks exfiltration and coaches users attempting to violate policy. That decision is written in advance for a class of data and a channel. In RedactSure's design a named person approves each pay change, submission or record update when the agent proposes it, and the approval carries that name. A buyer should know which each product provides before an agent touches payroll.
Will Trellix's logs show that an agent read the employee record?
No. Reading a page is not an event the DLP pages describe, so the endpoint agent has nothing to log, and the HR system records the specialist whose session the agent used. The Data Security Suites monitor data events for incidents. A record of what the model received, separating the agent's work from the person's, comes from the environment the agent worked in: RedactSure's AI Control Record.
Does Trellix's OCR for images and scanned files extend to a rendered page?
The Suites data sheet does not say so. It describes OCR that extends content inspection to images, PDFs and scanned document files, so a scanned form attached to an email can be matched and a policy applied. It does not describe changing what an application displays; a model reading the employee record reads the values the application renders, not a file the suite scanned.
Does the AI Data Risk Dashboard treat an AI agent differently from a user?
No. The data sheet describes users sharing data with AI platforms and gives the security team a single view of that sharing across Windows and macOS, which it can block under existing policies. An agent working a licensed application under a person's session looks like that person to the endpoint. Separating the agent's session, reads and actions from the employee's own activity is a design choice the dashboard pages do not describe.
How RedactSure closes the screen-level gap in classification-based data loss prevention
Classification-based DLP acts on data a policy recognizes as it moves through a watched channel, so an agent reading a rendered HR page receives the employee record. Trellix's AI data sheet lists the activity it watches as "copy/paste, clipboard sharing, and file uploads."
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the employee record under the specialist's session, and the model receives the name, home address, salary and national identification number. Inside the governed environment: the same page shows EMP_001, ADDR_001 and NID_001, the real identification number reaches payroll only when the named specialist approves the pay change, and page, 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 Trellix. Bring one HR workflow and see, with test values, what the model receives and the pay change awaiting the specialist. Book a demo