Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs Strac: 2026 Comparison & Alternative
Strac redacts sensitive values in MCP tool calls, prompts and connected SaaS apps, while RedactSure, a Strac 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 Strac alternative for AI agents usually start with the same word, redaction, and find that the two products apply it in different places. Strac inspects an MCP tool call, a prompt typed into an AI tool or a record in a SaaS app it connects to, and redacts what its detectors recognize there. RedactSure replaces values on the screen the agent is about to read, so the model receives CUST_001 instead of the customer's name. Strac protects data in the channels it inspects, and RedactSure protects data where an agent reads it.
This page quotes Strac's documentation and uses one example throughout: a customer record in a support ticketing platform the organization bought rather than built, holding a name, an email address, a mailing address and a card number pasted into a ticket, worked by an agent the way a support representative would. What Is Render-Layer Tokenization? defines the approach RedactSure uses.
Key findings
- As a Strac alternative for agent work, RedactSure replaces values on the rendered ticket before any model reads it, so the agent handles CUST_001 and CARD_001 and the real value is released only at an approved destination (What Is Render-Layer Tokenization?).
- Tokens before any model reads the screen, and real values only at the approved destination, keep the customer record out of the model's context; OWASP lists "Personal identifiable information (PII) may be disclosed during interactions with the LLM" as a disclosure risk (OWASP, LLM02, 2025).
- A model that only ever received CUST_001 cannot be coerced into disclosing a value it never held, so the control does not depend on a detector recognizing the card number (Does Prompt Injection Expose Customer Records?).
- None of Strac's pages addresses what a rendered application screen contains when a model reads it; its endpoint page states that discovery runs with "no keystroke logging and no screenshots" (Strac, Endpoint DLP, 2026).
- Strac's MCP control "captures each MCP invocation, flags the sensitive data inside, remediates it before the model sees it, and logs it as audit evidence," which covers an agent that reaches an application through a Strac connector (Strac, MCP Integrations, 2026).
- For agents in a browser, Strac's blog puts the control on the human acts, "the sensor that sees the paste, the upload and the download is the only place a control can act," rather than on the page the agent reads (Strac, Protect AI Agents, 2026).
How do the two compare side by side?
The table sets Strac's own wording beside what a governed environment does on the same point, and names the check for each row.
| Dimension | Strac, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | "Strac sits inline on every connector" in front of each SaaS app, and in the browser as "the user types into an AI tool" (Strac, MCP Integrations, 2026; Strac, Protect AI Agents, 2026) | On the screen the agent works in, before any model reads it | For one workflow, the exact place a value is replaced |
| How coverage starts | "Strac connects to each SaaS app via OAuth" and "Works as a browser extension" (Strac, SaaS DLP, 2026) | The agent works inside the governed environment; the application is unchanged | Which of the agent's applications are connected today |
| What the model receives | Tool-call content after Strac can "detect, redact, tokenize, vault, block, or audit sensitive data" (Strac, SaaS DLP, 2026) | The ticket with tokens, such as CUST_001 and CARD_001, consistent for the whole run | One screen, shown exactly as the model read it |
| How real values are restored | "Customers can replace original sensitive data with fake data also called as pseudonyms" (Strac, ChatGPT DLP, 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 | "Customers can configure different modes like Audit, Alert, Block, Redact sensitive data for their AI websites" (Strac, ChatGPT DLP, 2026) | A named person approves every refund, submission and record change | A person's name, not a mode |
| The audit record | "Strac controls which records and fields each agent can touch, protects sensitive fields, and logs every action" (Strac, MCP Integrations, 2026) | Every screen as tokens, every action and every approval, exported to the customer's SIEM | Whether the record shows what the model saw |
Reading down the two middle columns, the difference is what each product takes as its object. Strac governs the content of a channel it inspects, an MCP call, a prompt or a connected app's records, so its account of what the model saw stops at those channels; RedactSure governs the screen itself. They also differ on who turns a token back into a real value: a configured mode on Strac's pages, a named person in RedactSure's design (What Is Supervised Delegation?).
That record gap is wider than Strac: 36 of the 86 AI security and data protection products reviewed describe a record of what the model was shown, each of those records a prompt, a conversation or a request payload captured at the product's own gateway or pipeline, and no product in the review describes recording the rendered screen an agent read (documentation, not tested behavior) (How Many AI Security Products Control What an AI Agent Sees?).
What does Strac protect, and where does it act?
Strac's pages describe a DLP platform for the SaaS apps and AI tools a company already uses. Its SaaS DLP page says "Strac safeguards the data stored in your SaaS apps against breaches and unauthorized access" and for a ticketing system will "Identify & Redact (Mask) sensitive personal data (PII & PHI) from Zendesk tickets" so that support teams "can work without seeing or leaking PII," each of these within the apps it connects to (Strac, SaaS DLP, 2026). For agents, the MCP page states that "Every Strac connector runs as a governance gateway in front of the SaaS," a control on the connector rather than on an application's own interface (Strac, MCP Integrations, 2026).
Each benefit depends on one condition: a channel the product inspects, with a detector that recognizes the value as sensitive. The connector control reaches an agent that calls the app through MCP, the SaaS control reaches records the policy for that connected app classifies, and the browser control reaches what a person or tool sends into a GenAI tool. An agent that opens the ticketing platform's own interface and reads the page is outside all three unless the stored record was already masked, a boundary of the design rather than a defect.
What does an agent read when it opens a support ticket?
Consider the customer record. The agent signs in to the ticketing platform as a support representative would, opens the ticket and reads the page: the name, the email address, the mailing address and the pasted card number. If the platform is connected to the DLP and its policy recognized the card number, the stored ticket may already carry a mask. If it is not connected, or the value sits in a field the policy did not classify, nothing stands between the page and the model, since a rule must first identify content as sensitive before it can allow, warn, block or redact (Runtime Tokenization vs DLP for AI Agents). No MCP gateway sits in this path, since the agent read the interface rather than calling a connector. An agent working from screenshots takes in every visible field and any text on the page, whoever put it there (What Does an AI Agent See in a Screenshot?).
Whatever reaches the model is the exposure. OWASP records that disclosure "can result in unauthorized data access, privacy violations, and intellectual property breaches" (OWASP, LLM02, 2025), and a page the agent reads is also an input: "Indirect prompt injections occur when an LLM accepts input from external sources, such as websites or files" (OWASP, LLM01, 2025). There is an identity gap as well: the agent used the representative's session, so the platform's log names the representative and not the agent (What Is an AI Control Record?).
Which control fits which work?
Keep Strac for
- Records stored in the SaaS apps it connects to, where it will "Identify & Redact (Mask) sensitive personal data (PII & PHI) from Zendesk tickets" (Strac, SaaS DLP, 2026).
- Agents that reach an app through its MCP connector, where Strac "remediates it before the model sees it, and logs it as audit evidence" (Strac, MCP Integrations, 2026).
- Prompts and uploads people send to GenAI tools, through "a secure Browser Extension (Chrome, Edge, Safari, Firefox)" with Audit, Alert, Block and Redact modes (Strac, ChatGPT DLP, 2026).
Add RedactSure for
- Agents reading screens in applications the organization bought, where the model receives CUST_001 and CARD_001 instead of the real values (What Is Render-Layer Tokenization?).
- A named person approving each refund, reply or record change the agent proposes (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 that run agents need both, and this block makes clear which product protects the screen.
How do you test it on one workflow?
Compare the two on one real support workflow, in an afternoon, with both vendors present and every answer written down.
Step 1. Choose a queue the agent will actually work and pick one screen from it, the ticket with the pasted card number, not a diagram of the data flow.
Step 2. Ask each vendor to show that ticket exactly as the model sees it: real values, a mask applied to the stored record, or tokens applied on the screen.
Step 3. List every application the queue touches, the ticketing platform, the payment processor, the shipping portal, and mark each as connected to the DLP through OAuth or MCP, or not. The unconnected group is where the model reads real values on day one.
Step 4. Trace the card number from what the model received to the refund that used it: what released the real number, when, and whose name appears in the record. Ask the DLP vendor where the pseudonym is reversed and which mode allowed it, and the render-layer vendor for the approval entry with the person's name.
Step 5. Calculate day-one coverage from Step 3: five applications with two connected is two of five. Compare the costs that differ: connecting and tuning policy for each remaining application, and the agent work that waits for it.
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 page from the representative because values are replaced with consistent tokens before the model reads anything: CUST_001, EMAIL_001, ADDR_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 refund, submission and record change, and every screen, action and approval is written to a control record that exports to the organization's SIEM.
OWASP explains why the approval sits with a person: it describes excessive agency as "the vulnerability that enables damaging actions to be performed in response to unexpected, ambiguous or manipulated outputs from an LLM" (OWASP, LLM06, 2025), so a refund is approved by a named person rather than by the model. A model that only ever saw CUST_001 has nothing real to disclose, the principle in What Is Least Exposure?.
What the record shows
The record this page follows is a customer record in a support ticketing platform the organization bought rather than built: a name, an email address, a mailing address and a card number pasted into a ticket. A render layer replaces those four values with tokens on the ticket before the model reads it, and that is the job RedactSure, as the Strac alternative for agent work, does for this record, with real values released only for approved refunds. Strac's real coverage is the channels it inspects, MCP tool calls through its connectors, prompts and uploads into GenAI tools, and records in the SaaS apps it connects to; its pages describe no control over what a model reads on the interface of an unconnected application. The test takes one queue and one ticket: have each vendor show it exactly as the model saw it, then trace the card number to the refund that used it and the person who approved it.
Methodology and limitations
This page draws on five Strac pages, three 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 own wording, and public documentation read in full gives a sound view of where each product applies its control.
Both products are described from documentation; neither was tested for this page, 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 support ticket is an illustrative record type, not an organization. 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 Strac protect data sent to an LLM?
Only in the channels it inspects. Strac's pages describe MCP DLP that inspects tool calls and responses and can redact, tokenize or block what it finds, and a browser extension that redacts prompts before they reach the model. Both apply where an agent calls a connector or a person types into an AI tool; a page an agent opens in a purchased application passes through neither.
Does Strac cover what an agent reads on a purchased application's screen?
Not from the pages reviewed. Strac's pages describe redaction of records stored in SaaS apps it connects to, and a browser sensor that acts on pastes, uploads and downloads. A ticket in a connected app may already carry a mask when the agent opens it; a ticket in an unconnected app, or a field the policy did not classify, reaches the model as displayed, so the screen needs a control in front of the model.
Who approves the agent's action, a policy or a person?
A policy mode, on Strac's pages, which describe Audit, Alert, Block and Redact modes for AI websites, and a blog that lists human approval on risky actions as a guardrail type without saying Strac provides it. In RedactSure's design a named person approves each refund, submission or record change when the agent proposes it, and the approval carries that person's name. Ask each vendor whose name appears beside an approved refund.
Will the ticketing platform's logs show that an agent acted?
No. When an agent reads a page, nothing passes through a connector, so the DLP has no invocation to log, and the platform records the representative whose session the agent used. Strac's MCP page documents a log of every connector action for agents that call the app through MCP. A record that separates the agent's work from the person's has to come from the environment the agent worked in; RedactSure's AI Control Record exports that to the SIEM.
Is redaction in a connected app the same as render-layer tokenization?
No. Redaction in a connected app changes the stored record under a policy, after a detector recognizes a value; everyone who opens it afterward sees the mask, and a value the detector missed stays visible. Render-layer tokenization substitutes on the screen before a model reads it, with one consistent token for the run, so the agent can follow CUST_001 across tickets while the record is unchanged.
Can a hidden instruction on the ticket make the agent disclose the card number?
Only if the model holds the number. Anyone can paste text into a ticket, and OWASP treats content from external sources as an indirect prompt injection path. A detector-based control acts when it recognizes an attack or a value; a model that received CARD_001 has nothing real to disclose, whatever the ticket says. Test it by planting a benign instruction in a ticket and reading what the model received.
How RedactSure closes the screen gap in channel-based DLP
Strac's controls act on the MCP call, the prompt and the connected app's records, so an agent reading a ticket page in a bought application sits outside them. Strac's blog puts the browser control on "the paste, the upload and the download" rather than on the page the agent reads.
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the ticket under the representative's session and the model receives the name, email address, mailing address and pasted card number. Inside the governed environment: the same page shows CUST_001, EMAIL_001, ADDR_001 and CARD_001, and the real card number reaches the payment processor only when the named representative approves the refund.
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 Strac. Bring one support workflow and see, with test values, the ticket as the model receives it and the refund awaiting approval. Book a demo