Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs DTEX: 2026 Comparison & Alternative
DTEX watches agent behavior from endpoint telemetry and scores it, while RedactSure, a DTEX alternative built for AI agents, replaces values on the screen before the 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 DTEX alternative for AI agents are comparing two jobs. DTEX collects endpoint activity, attributes each action to a person or an agent, scores the risk and lets a security team step in. RedactSure changes what the agent is shown: on the screen it is about to read, names, identifiers and amounts become consistent tokens, so the model never receives the real value.
This page quotes DTEX's documentation and uses one example throughout: an employee record in a human resources system the organization bought, holding a name, a home address, a Social Security number and a salary, opened by a computer use agent that works the system as an HR coordinator would. What Is Render-Layer Tokenization? defines the approach, and Should AI Models Be Treated Like Insider Risks? explains why an agent needs controls outside the model.
Key findings
- As a DTEX alternative for agent work, RedactSure replaces the values on the rendered screen before any model reads it, so the agent handles the employee as EMP_001 and the real Social Security number is released only at the approved destination (What Is Render-Layer Tokenization?).
- Real values resolve only at the approved destination, at the moment of an approved action, so a model that received only tokens has no personal data to disclose; OWASP states that "Personal identifiable information (PII) may be disclosed during interactions with the LLM" (OWASP, LLM02, 2025).
- A named person approves every payment, submission and record change the agent proposes, so each approval carries a name, not a risk score (What Is Supervised Delegation?).
- DTEX's control is observation at the endpoint: it will "Trace every AI agent action back to the originating prompt, reasoning chain, and human instruction that initiated it," and "DTEX differentiates human and AI activity at the behavioral telemetry layer," a record of what the agent did, with no change to what it was shown (DTEX, AI Agent Oversight, 2026).
- For agents that operate screens, DTEX will "Recursively trace computer-use AI agent activity across applications, processes, and workflows to clearly distinguish human actions from autonomous agent behavior," while the model reads the screen as displayed (DTEX, AI Agent Oversight, 2026).
- DTEX AI Risk Management "connects human behavior, AI activity, and data exposure to uncover emerging risk, accelerate investigations, and enable safe AI adoption at enterprise scale," in a private preview (DTEX, AI Risk Management, 2026).
How do the two compare side by side?
The table sets DTEX's wording beside what a governed environment does on each point, with the check a buyer can run.
| Dimension | DTEX, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | "DTEX differentiates human and AI activity at the behavioral telemetry layer" (DTEX, AI Agent Oversight, 2026) | On the screen the agent works in, before any model reads it | The point at which a value is replaced rather than observed |
| How coverage starts | A collector that "Captures continuous user and system activity from endpoints and servers" (DTEX, Insider Risk, 2026) | The agent works inside the governed environment; the HR system is unchanged | Whether the agent's endpoint has the collector |
| What the model receives | The pages describe capturing "AI prompts, uploads, and sensitive interactions before encryption or transmission to external AI platforms" (DTEX, Shadow AI, 2026) | The screen with tokens, such as EMP_001 and SSN_001, consistent for the whole run | One screen, exactly as the model receives it |
| How real values are restored | Pseudonymization covers DTEX's own activity data; a team can "reverse pseudonymization when needed" (DTEX, Pseudonymization, 2026) | Released at an approved destination when a named person approves the action | Who authorizes it, and where that is recorded |
| Who approves an action | A security team that can "investigate, validate, and intervene when agent behavior deviates from expected workflows or crosses enterprise risk thresholds" (DTEX, AI Agent Oversight, 2026) | A named person approves every payment, submission and record change | A name on the action before it runs, not an alert after |
| The audit record | "DTEX collects more than 500 metadata elements to correlate activities across devices into one risk score" (DTEX, Platform, 2026) | Every screen as tokens, every action and 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. DTEX governs behavior collected from endpoints, so its answer to what the model saw is a record of access, not of content. RedactSure governs what the agent reads, so its answer is the screen itself, as the model receives it. They also differ on who turns a token back into a real value: a security team reversing pseudonymization in DTEX's own data, or a named person approving an action in RedactSure's design (What Is Supervised Delegation?).
What does DTEX protect, and where does it act?
DTEX is an insider risk and behavioral intelligence platform. Its platform page states that "DTEX collects activity 24/7, on and off network, whether it is interesting at the time or not" (DTEX, Platform, 2026). Its DLP is built on the same telemetry: the page says it will "Natively classify data based on behavior, not inspection" and "Apply interventions based on user intent" (DTEX, Risk-Adaptive DLP, 2026).
Each benefit depends on one condition: telemetry from an endpoint the collector covers, read by a security team that scores it and intervenes, a limit of the design rather than a defect. The pseudonymization page is explicit, "Focus on activities, not content," and the identifiers it masks are those of monitored people in DTEX's own data (DTEX, Pseudonymization, 2026). A change to the values an agent reads on an application screen is not described in the pages reviewed, and the screen is the only way an agent reaches the employee record.
Of the 86 security and data protection products reviewed, 36 describe recording what the model was shown. All of those records are prompts, conversations or request payloads at the product's own gateway or pipeline, and none of the 86 describes recording the rendered screen an agent read (How Many AI Security Products Control What an AI Agent Sees?).
What does an agent read when it opens an employee record?
The computer use agent signs in to the HR system the way a coordinator does and reads the page it displays: a name, a home address, a Social Security number and a salary. On an endpoint with the collector installed, DTEX can attribute the process to an agent rather than to the coordinator, and nothing on its pages alters the screen (DTEX, AI Agent Oversight, 2026). The model receives the four values as displayed, and an agent working from screenshots takes in everything visible, including fields the task never needed (What Does an AI Agent See in a Screenshot?).
The exposure sits in the model from that point on; OWASP warns that "This can result in unauthorized data access, privacy violations, and intellectual property breaches" (OWASP, LLM02, 2025). There is also an identity gap: the agent used the coordinator's session, so the HR system's own log records the coordinator, and DTEX's attribution at the endpoint does not change the application's record of who changed the salary.
Which control fits which work?
Keep DTEX for
- Workforce insider risk, where the platform "collects activity 24/7, on and off network, whether it is interesting at the time or not" (DTEX, Platform, 2026).
- Separating agent activity from human activity, where DTEX "Identifies processes spawned by AI agents and attributes every action to the correct human or autonomous actor" (DTEX, AI Agent Oversight, 2026).
- Unsanctioned AI use, where the product will "Capture AI prompts, uploads, and sensitive interactions before encryption or transmission to external AI platforms" (DTEX, Shadow AI, 2026).
Add RedactSure for
- Agents reading screens in applications the organization bought, where the model receives EMP_001 and SSN_001 instead of the real values (What Is Render-Layer Tokenization?).
- A named person approving the salary change, and every other payment, submission or record change the agent proposes (What Is Supervised Delegation?).
- The record of what the model received on every screen, with each action and approval, in the customer's SIEM (What Is an AI Control Record?).
An organization that runs agents will usually need both; this block shows which one protects the screen.
How do you test it on one workflow?
Run the comparison on one real HR workflow in an afternoon, with both vendors in the room and every answer written down.
Step 1. Choose a workflow the agent will run in production and pick one screen from it, such as the employee record, rather than a diagram.
Step 2. Have each vendor show the employee record exactly as the model receives it. DTEX's pages describe tracing agent activity "across applications, processes, and workflows," so set that trace beside the screen content and compare the two (DTEX, AI Agent Oversight, 2026).
Step 3. List every application in the workflow and mark each as a screen where values are replaced before the model reads them, a screen where only endpoint telemetry records the agent's activity, or neither; the last two groups are the first-day content exposure.
Step 4. Trace the Social Security number from the token back to its real value: what released it, when, and whose name appears in the record. Ask DTEX how an intervention reaches the agent mid-task and what the model had read by then.
Step 5. Calculate first-day coverage. If the task list touches five applications and none of the five screens is altered, screen coverage is zero of five while activity coverage may be five of five. Compare the costs that differ: investigation after an alert, and the records a model has read before anyone intervenes.
What does a governed environment change?
A governed environment is a category of control rather than 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 sees a different screen from the coordinator, because values are replaced with consistent tokens before the model reads anything: EMP_001, ADDR_001 and SSN_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 goes to a control record exported to the organization's SIEM.
OWASP notes that "Indirect prompt injections occur when an LLM accepts input from external sources, such as websites or files," and its mitigation is to "Restrict the model's access privileges to the minimum necessary for its intended operations" (OWASP, LLM01, 2025). Anthropic's simulated corporate environments found that "In at least some cases, models from all developers resorted to malicious insider behaviors," and "We have not seen evidence of agentic misalignment in real deployments," the case for controls outside the model (Anthropic, 2025). A model that only ever received SSN_001 cannot be manipulated into disclosing a number it never held, the principle in What Is Least Exposure?. A gateway on the tool call path (RedactSure vs Nightfall AI: 2026 Comparison & Alternative) sanitizes tool results; a screen read directly needs the control on the screen.
What the record shows
The record this page follows is an employee record in a human resources system the organization bought, and it carries a name, a home address, a Social Security number and a salary. A render-layer control replaces those four values with tokens on the HR screen before the model reads it, and that is the job RedactSure, as the DTEX alternative for agent work, does for this record, with real values released only for approved actions. DTEX's real coverage is the activity its collector sees on an endpoint: it attributes and traces the agent's actions, scores the risk and lets a security team intervene, and its documentation describes no change to what a model reads on a bought application's screen. The test takes one workflow and one screen: have each vendor show the record as the model receives it, then trace the Social Security number back to the action that released it and the person who approved it.
Methodology and limitations
This page draws on seven DTEX pages, two OWASP entries, one Anthropic report 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.
Both products are described from documentation; none was tested for this page, no customer of either is described, both 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 replaces a pilot of one workflow on its own screens.
Frequently asked questions
Does DTEX protect data an agent sends to an LLM?
Only as visibility at the endpoint. DTEX's shadow AI page says it will "Capture AI prompts, uploads, and sensitive interactions before encryption or transmission to external AI platforms," and its DLP applies interventions based on user intent. Both see a transfer and act on the person or process behind it; when a computer use agent reads an employee record on screen, the values are in the model before any transfer occurs.
Does DTEX cover what a computer use agent reads on a rendered screen?
Not from the pages reviewed. DTEX's agent oversight page describes tracing computer use agent activity across applications and processes to distinguish human actions from agent behavior, which is a record of what the agent did; the screen it read reaches the model unchanged. Activity answers who acted; the screen as received answers what the model holds.
Who approves the agent's action, a risk score or a person?
A security team acting on a score, on DTEX's pages, which say teams "investigate, validate, and intervene when agent behavior deviates from expected workflows or crosses enterprise risk thresholds." That is a response after the behavior. In RedactSure's design a named person approves each payment, submission or record change when the agent proposes it, before it runs, and the approval carries that person's name.
Does DTEX Pseudonymization mask what the model reads?
No. DTEX's Pseudonymization masks the identities of monitored employees in its own activity data, so analysts "Focus on activities, not content," and investigators can reverse it when an inquiry warrants, a design built for workforce privacy. The employee record on the HR screen is content, and the pages do not describe changing it before an agent's model reads it.
Will the HR system's own logs show that an agent acted?
Not on their own. The agent used the coordinator's session, so the HR system records the coordinator as the person who changed the salary. DTEX's attribution of the agent lives in its own data rather than in the application's record. RedactSure's AI Control Record gives the agent its own session and writes every screen, action and approval to the organization's SIEM.
What record of the agent's work does DTEX keep?
A behavioral one. The platform page says DTEX "collects more than 500 metadata elements to correlate activities across devices into one risk score," and its agent oversight page traces each action to its prompt. That record shows what the agent did and why, not the screen the model received, so a review of what personal data entered the model needs a second record.
How RedactSure closes the screen-level gap in behavioral telemetry
Behavioral telemetry records what an agent did on an endpoint after the fact, so an agent in a bought HR system reads the employee record as displayed. DTEX's own pages describe surfacing anomalous agent behavior before sensitive data moves, a signal about activity rather than a change to the screen.
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the HR system under the coordinator's session, the screen shows the name, address, Social Security number and salary, and the model receives all four. Inside the governed environment: the same screen shows EMP_001, ADDR_001 and SSN_001, and the real salary reaches the HR record only when the named coordinator 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 DTEX. Bring one HR workflow and see, with test values, the screen the model receives and the change awaiting the coordinator's approval. Book a demo