Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs Cyberhaven: 2026 Comparison & Alternative
Cyberhaven governs data flows through an endpoint agent, browser extension and cloud connectors, while RedactSure, a Cyberhaven 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 weighing a Cyberhaven alternative for AI agents are choosing between two places to put a control. Cyberhaven watches data move: its endpoint agent, browser extension and cloud connectors trace a field from its origin to the point where it leaves, and a policy acts on that flow. RedactSure acts earlier, on the screen the agent is about to read, replacing each identifier with a token so the model never receives the real value.
This page quotes Cyberhaven's product pages and datasheet and uses one example throughout: a customer record in a CRM the organization bought rather than built, holding a name, an email address, a phone number and an account number, opened by an agent that works the CRM as a service representative would. What Is Render-Layer Tokenization? defines the approach.
Key findings
- As a Cyberhaven alternative for agent work, RedactSure replaces the values on the CRM page before any model reads it, so the agent works the customer as CUST_001 and the real account number is released only at an approved destination (What Is Render-Layer Tokenization?).
- What a model receives from a screen is an exposure in its own right, whichever channel later carries it; OWASP lists "Personal identifiable information (PII) may be disclosed during interactions with the LLM" as a disclosure risk (OWASP, LLM02, 2025).
- None of Cyberhaven's published pages states that values on a rendered screen are replaced before a model reads them; they describe guardrails "at the prompt and response level" and policies on data flows (Cyberhaven, Autonomous Agents, 2026).
- Cyberhaven governs data movement once its sensors recognize the content: "Cyberhaven applies risk-based policies at the data level, blocking or monitoring data flows based on context" (Cyberhaven, Autonomous Agents, 2026).
- It sees data through "Three deployment modes that together give full visibility and control over data," cloud connectors, an endpoint agent and a browser extension, each watching data leave (Cyberhaven, Lineage, 2026).
- For agents, its datasheet says the platform "records agent activity (prompts, tool calls, file reads, and responses) with full data context," a record of what the agent did rather than of what its model received (Cyberhaven, Datasheet, 2026).
How do the two compare side by side?
The table sets Cyberhaven's own wording beside what a governed environment does on the same point, and names the check a buyer can run.
| Dimension | Cyberhaven, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | On data in motion: an endpoint footprint "to see and enforce policies for data leaving to the cloud," a browser extension and cloud connectors (Cyberhaven, Lineage, 2026) | On the screen the agent works in, before any model reads it | Where a value is blocked or replaced |
| How coverage starts | Sensors are deployed; "an endpoint agent to continuously inventory AI tools as they appear across the organization" (Cyberhaven, Autonomous Agents, 2026) | The agent works inside the governed environment; the CRM is unchanged | Which of the agent's applications the sensors reach |
| What the model receives | Whatever the page carried; it can "block, warn, or redact in real time based on data context" once it recognizes a flow (Cyberhaven, Autonomous Agents, 2026) | The screen with tokens, CUST_001 and ACCT_001, consistent for the whole run | One CRM screen, exactly as the model read it |
| How real values are restored | Redaction is a control "that operate across the full agentic workflow"; its form is not stated (Cyberhaven, Autonomous Agents, 2026) | Released at an approved destination when a named person approves the action | Who turns a token back into a value, and where that is recorded |
| Who approves an action | A policy; when one is triggered, "employees see a plain-English explanation of what happened and why, with the option to revise or proceed" (Cyberhaven, Secure AI Usage, 2026) | A named person approves every payment, submission and record change | A person's name on the approval, not a rule name |
| The audit record | "The platform reconstructs the full execution lifecycle of every agent interaction: what data was accessed, what tools were invoked, what actions resulted" (Cyberhaven, Secure AI Usage, 2026) | Every screen as tokens, every action and every approval, exported to the customer's SIEM | Whether the record shows what the model received |
Read down the two middle columns and each product governs a different object. Cyberhaven governs the movement of data its sensors observe, so its answer to what the model saw is a reconstruction of the flow, assembled after the data has passed. RedactSure governs what an agent reads, and its answer is the CRM page the model received, tokens included.
What does Cyberhaven protect, and where does it act?
Cyberhaven's pages describe a data security platform, now presented as Flow. Its datasheet says Flow "unifies DLP, DSPM, IRM, and AI security," and the product page says it "connects lineage, identity, and behavior to secure data as it moves across endpoints, browsers, and the cloud" (Cyberhaven, Datasheet, 2026; Cyberhaven, DSPM, 2026). The lineage page explains the sensors: cloud connectors to sanctioned applications, a browser extension that "collects telemetry for web-based cloud applications not available from other sources," and an endpoint agent that sees data "before it's encrypted to travel via the internet" (Cyberhaven, Lineage, 2026).
Each benefit depends on one condition: the data must pass through a sensor and be recognized there, the detection property (Runtime Tokenization vs DLP for AI Agents) describes. An agent that opens the CRM and reads the customer record creates no flow to classify until it does something with the data, and by then the model has read the page. What the model received from the screen before it acted lies outside that design, a limit rather than a defect.
What does an agent read when it opens the customer's CRM record?
The agent signs in to the CRM the way a service representative does, opens the account and reads the page: name, email address, phone number and account number, in the clear, because the CRM renders them for whoever is logged in. If the agent then pastes the account number into an outside tool, Cyberhaven's endpoint agent sees a flow and a policy can act on it. Reading creates no flow, and the model has received all four values the moment the page loads, as What Does an AI Agent See in a Screenshot? describes. A benchmark of web agents found that they "are prone to inadvertent use of unnecessary sensitive information" (Zharmagambetov et al., 2025), and an agent that holds the whole record can use any part of it.
The one obfuscation the pages describe applies to Cyberhaven's own console: "Ensures sensitive information displayed in the dashboard is obfuscated based on user roles," the analyst's view rather than the CRM screen (Cyberhaven, DSPM, 2026). Substitution of values on an application screen before a model reads it is not described in the pages reviewed. The agent also used the representative's session, so the CRM's log records the representative and no record names who answers for the agent's action.
That gap is general rather than Cyberhaven's alone: of the 86 AI security products reviewed, 36 describe recording prompts or request payloads and 33 describe a SIEM export of AI activity, and the rendered page an agent read is not among the records any of them describes (How Many AI Security Products Control What an AI Agent Sees?).
Which control fits which work?
Keep Cyberhaven for
- Exfiltration by people, where it will "Stop sensitive data from leaving the organization across email, web, cloud storage, USB" (Cyberhaven, Datasheet, 2026).
- Policies based on where data came from, as when "The product blocks the exfiltration of customer data based on its origin in Salesforce" (Cyberhaven, Lineage, 2026).
- An inventory of AI use, since it will "Discover every AI tool and agent across endpoints and the browser, score each for risk" (Cyberhaven, Datasheet, 2026).
Add RedactSure for
- Agents reading screens in applications the organization bought, where the model receives CUST_001 and ACCT_001 instead of the customer's identifiers (What Is Render-Layer Tokenization?).
- A named person approving every change the agent proposes to the customer record, and every payment it initiates (What Is Supervised Delegation?).
- The record of what the model received on every screen, exported to the customer's SIEM (What Is an AI Control Record?).
Most organizations running agents will keep both; this block shows which product protects the screen the agent reads.
How do you test it on one workflow?
Compare the two on one real workflow, over an afternoon, with both vendors present and every answer written down.
Step 1. Choose a workflow the agent will run in the CRM, such as updating contact details after a call, and pick one screen from it, the customer record, rather than a diagram of the data flow.
Step 2. Have each vendor produce the customer record exactly as its model received it. The output settles whether the model saw the four values or tokens in their place; a description of the policy that would have fired is different evidence.
Step 3. List every application in the workflow, the CRM, the ticketing tool, the email client, the payment page, and mark each as reached by this vendor's sensors or not. The second group is the screens the agent can read on day one with nothing in front of the model.
Step 4. Trace the account number from the token the model held back to the real value: what released it, at what destination, when, and whose name appears in the record beside the release.
Step 5. Calculate day-one coverage from Step 3: five applications with two reached is two of five. Compare the costs that differ: the sensors to deploy, and the agent work delayed until the uncovered screens are handled.
What does a governed environment change?
A governed environment is a category of control rather than a product feature. The agent works under its own session, recorded under its own identity, and sees a different screen from the representative because values are replaced with consistent tokens before the model reads anything: CUST_001, EMAIL_001, TEL_001 and ACCT_001, the same 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 control record that exports to the organization's SIEM.
The control belongs in front of the model rather than on the channel behind it. OWASP notes that "Indirect prompt injections occur when an LLM accepts input from external sources, such as websites or files" and lists restricting "the model's access privileges to the minimum necessary for its intended operations" as a mitigation (OWASP, LLM01, 2025). A model that only ever saw CUST_001 cannot be coerced into disclosing an account number it never received (Does Prompt Injection Expose Customer Records?), the principle of What Is Least Exposure?: keep what an agent receives for a task to the minimum.
What the record shows
Cyberhaven's own coverage is data in motion: "Three deployment modes that together give full visibility and control over data," cloud connectors, an endpoint agent and a browser extension, watching the endpoints, browsers and cloud applications its sensors reach, and its pages describe no control over what a CRM page shows a model. The render layer replaces the customer's name, email address, phone number and account number with tokens on the CRM page before the model reads it, the job RedactSure, as the Cyberhaven alternative for agent work, does for this record, with real values released only for an approved action and a record under the agent's own identity. The test runs on one agent and one customer record: ask each vendor for the model's input from that screen, then compare whose name sits beside the release of the account number.
Methodology and limitations
This page draws on five Cyberhaven pages and its datasheet, two OWASP entries, one peer-reviewed benchmark 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. Vendor statements are quoted in the vendor's own wording, and public documentation read in full gives a sound view of where each product applies its control; "not described" refers to the pages reviewed.
The products on this page are described from documentation; none was tested for this page, both products change, and the page carries its date. RedactSure is deploying now with enterprise and government customers. The customer record is an illustrative record type. Compliance determinations belong to the organization's counsel, assessors and security team, and nothing here replaces a pilot of one workflow on its own screens.
Frequently asked questions
Does Cyberhaven protect data sent to an LLM?
Only where the data passes through its sensors. Cyberhaven's pages describe runtime guardrails "at the prompt and response level" and block, warn and redact controls applied by data context and the risk score of the tool. Those act when a person or an agent moves data into an AI tool the sensors can see; a model that reads a CRM page directly holds the record before any flow exists.
Does Cyberhaven cover what an agent reads on a CRM screen?
Not from the pages reviewed. Cyberhaven's browser extension collects telemetry from web applications and its endpoint agent tracks what data each agent accesses and what actions it takes. None of its pages says the values on the page are replaced before the model reads them. In a purchased CRM, that leaves the model holding the full record on every screen it opens.
Who approves the agent's action, a policy or a person?
A policy, on Cyberhaven's pages, which say that when a policy is triggered employees see a plain-English explanation with the option to revise or proceed. That decision is written in advance for a class of data. In RedactSure's design a named person approves each payment, submission or record change when the agent proposes it, and the approval carries that person's name. Ask each vendor which of the two it provides.
What record shows which screens the agent's model received?
Cyberhaven's record covers activity: its datasheet says the platform records prompts, tool calls, file reads and responses with data context, and its use-case page says it reconstructs each agent interaction. That answers what the agent did. A record of the screen the model received comes from the environment the agent worked in; RedactSure's AI Control Record holds every screen as tokens, every action and every approval, exported to the organization's SIEM.
Does a cloud API connector protect the record while the agent works?
No. A connector to a sanctioned application such as Office 365 or Google Workspace gives visibility into content created and shared in that application, a view of stored and shared data. It does not sit between the CRM page and the model while the agent reads, and the CRM here is not among the applications the connectors name. Reading needs a control in front of the model.
Can a hidden instruction on a page make the agent disclose the account number?
Only if the model received the account number. OWASP describes indirect prompt injection as a model accepting input from external sources such as websites or files, and a note on a customer record is such an input. Cyberhaven's guardrails can block or redact the resulting flow once they recognize it. A model that read ACCT_001 instead of the real number has nothing to disclose, whichever instruction it followed.
How RedactSure closes the screen-level gap in data lineage protection
Lineage and flow controls act once data moves, so an agent that opens the CRM reads the customer record exactly as the page displays it. Cyberhaven's pages place its guardrails "at the prompt and response level," after the model has already read the page.
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the CRM under the representative's session, the page shows the name, email address, phone number and account number, and the model receives all four. Inside the governed environment: the same page shows CUST_001, EMAIL_001, TEL_001 and ACCT_001, and the real account number is released to the payment page only when the named representative 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 Cyberhaven. Bring one CRM task and see, with test values, what the model receives and who approves the account number's release. Book a demo