Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs ChatGPT Work: 2026 Comparison & Alternative
ChatGPT Work controls the enterprise workspace and its administrator settings, while RedactSure, a ChatGPT Work alternative built for AI agents, replaces values on the rendered screen before a model reads them. 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.
An accounts-payable clerk has a new supplier record open, the bank account and tax ID filled in, and an agent waiting to read the same screen, which is why the team weighs a ChatGPT Work alternative. ChatGPT Work governs the workspace the run happens in: the sites an administrator allows and the account a person signs in with. RedactSure governs the rendered page, where the account number becomes ACCT_001 before any model reads it.
This page quotes ChatGPT Work's overview and administrator FAQ and follows one record type: a new supplier in a procurement portal the organization bought, holding a legal name, a tax ID and a bank account. What Is Render-Layer Tokenization? defines the substitution the record depends on.
Key findings
- As a ChatGPT Work alternative for agent work, RedactSure replaces the values on a rendered supplier record with consistent tokens before any model reads it, so the agent works with ACCT_001 and the real account is released only at an approved destination (What Is Render-Layer Tokenization?).
- An agent that only ever received ACCT_001 cannot be argued into repeating the account number, and injections that ask agents to leak personal data seen during a task reach "average attack success rates (ASR) around 20 percent" (Alizadeh et al., 2025).
- The controls sit outside the model, so a model from one vendor can run inside the environment today and another tomorrow, with the same policy, approvals and record (Can AI Models Change Without Changing Their Controls?).
- ChatGPT Work's documentation states that with connected apps the agent "can access sensitive data like emails, files, or account settings," and that screenshots are not captured during takeover, which "helps protect passwords" (OpenAI, ChatGPT agent, 2026).
- The admin FAQ says the Compliance Logs Platform "retains data for 30 days" and that its records "don't establish a complete audit trail" (OpenAI, Admin FAQ, 2026).
- Its overview states that "Allowing a website doesn't approve every action on that site," and that "Website sign-in through the cloud browser isn't available in Enterprise or Edu workspaces" (OpenAI, Work Overview, 2026).
How do the two compare side by side?
Each row sets ChatGPT Work's wording beside the governed-environment answer and the buyer's check.
| Dimension | ChatGPT Work, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | The workspace and its administrator settings: allowed sites, connected apps, the signed-in account | On the rendered page, before the model reads it | Which layer holds the account number |
| How coverage starts | An administrator allows a site or connects an app; "Allowing a website doesn't approve every action on that site" (OpenAI, Work Overview, 2026) | The agent opens the portal in the governed environment | How many applications are covered on day one |
| What the model receives | The page as the portal renders it; with connected apps the agent "can access sensitive data like emails, files, or account settings" (OpenAI, ChatGPT agent, 2026) | The same page with VENDOR_001, TAX_001 and ACCT_001, consistent for the run | One screen, shown field by field |
| How real values are restored | No substitution step is described: the model reads the value the page displays (OpenAI, Work Overview, 2026) | Released at the approved destination on a named person's approval | What releases a value, and when |
| Who approves an action | A site allowed in advance, and a person who can take over a run, which "helps protect passwords" (OpenAI, ChatGPT agent, 2026) | A named person approves every payment, submission and record change | A person's name, not a site rule |
| The audit record | Compliance Logs "retains data for 30 days," and records "don't establish a complete audit trail" (OpenAI, Admin FAQ, 2026) | Every screen as tokens, every action and approval, exported to the customer's SIEM | Whether it shows what the model read |
The two middle columns describe different objects of control. ChatGPT Work governs the workspace the run happens in, so its answer is a site allowance, the signed-in account and a log with an expiry date. RedactSure governs what the page shows the model, so its answer is a record of the screen as tokens and a release that carries a person's name, as What Is Supervised Delegation? sets out. The decision is who turns a token back into a real account number.
What does ChatGPT Work protect, and where does it act?
ChatGPT Work is an enterprise workspace with an agent inside it, and its documented controls sit on the workspace rather than on the page. An administrator allows sites and connects apps, and the overview is explicit that "Allowing a website doesn't approve every action on that site" (OpenAI, Work Overview, 2026).
The same pages state the boundary. "Website sign-in through the cloud browser isn't available in Enterprise or Edu workspaces," so portal work that needs an authenticated session sits outside the browser feature in the workspaces most regulated buyers run (OpenAI, Work Overview, 2026).
Every benefit depends on one condition: the work stays inside the workspace, through a site an administrator allowed. A supplier record in a portal the organization bought is rendered with its own fields, and no page reviewed describes a step that replaces the account number before the model reads it.
What does an agent read when it opens a supplier record?
The record is a new supplier in a procurement portal: legal name, tax ID, bank account, remittance email, and a first payment queued behind an approval. The agent opens the page under the clerk's session and reads the same fields.
A site allowance decides whether the agent may open the portal, not what it returns. The portal renders the bank account and tax ID as it stores them, so the model receives both for the rest of the run. Columbia DAPLab records the shape of the outcome: "A zero-click ChatGPT vulnerability silently exfiltrated Gmail data from within OpenAI's own cloud infrastructure" (Columbia DAPLab, 2026). The same gap runs through the field: of 15 AI agents and 86 AI security products, none describes replacing the values an agent reads on a screen with tokens it can work with (Do AI Agents Ask Before Making a Payment?, How Many AI Security Products Control What an AI Agent Sees?).
A second gap: the agent acts under the clerk's session, so the portal logs the clerk, and the release is a click on a screen the agent prepared. That is the split What Is a Tethered Agent? draws between authority and capability, where the log names the person even when the agent did the reading. The arrangement that closes it gives the agent its own session and replaces the fields before the view is built, which What Is a Protected Browser for AI Agents? describes.
Which control fits which work?
Keep ChatGPT Work for
- Work inside the workspace the team already governs, where an administrator allows the sites and apps the agent may use (OpenAI, Work Overview, 2026).
- A run where a person should be able to take over, since screenshots are not captured during takeover and passwords are protected when a person types (OpenAI, ChatGPT agent, 2026).
- Browser work folded into the same enterprise terms, administrator controls and compliance logs as the rest of the workspace (PPC Land, 2026).
Add RedactSure for
- Agents reading screens in applications the organization bought, where the model receives ACCT_001 instead of the account number (What Is Render-Layer Tokenization?).
- A named person approving each payment, submission or record change before the agent acts (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?).
An organization running agents usually needs both, and the split above is about which product protects the screen. Harry Ghuman, CEO, Alpha Decisions, said on the podcast that the process comes first: "consider all the possibilities that could happen and you put effective controls around" (What Is the AI Delegation Frontier? Harry Ghuman on Who Gave the AI Its Authority).
How do you test it on one workflow?
Run both arrangements against the same supplier task in an afternoon, with each vendor present and the answers written down.
Step 1. Pick the screen the agent will open, the new supplier form in the portal, and note the four fields that matter: legal name, tax ID, bank account and remittance email.
Step 2. Ask each vendor to replay the input its model received for that screen, field by field, and mark each as the real value or a stand-in. The replay is the evidence; an architecture diagram is not.
Step 3. List the applications the task touches, including the portal, the mail client and any finance system the agent writes to, and mark each as covered or not. The uncovered ones are the first-day exposure, so count them.
Step 4. Trace the bank account from the record to the payment: what released the real value, at what moment, and whose name sits beside it. If the answer is a site rule written last month, the approval belongs to nobody in particular.
Step 5. Work out first-day coverage. With eight applications in the task and three covered, coverage is three of eight. Compare the two costs that differ: the setup each control needs, and the agent work that waits.
What does a governed environment change?
A governed environment changes who holds the value while the agent works. For each task the planner proposes what to tokenize, starting from personal information, account numbers, credentials and record numbers.The agent runs in its own session under its own identity, and reads a different page because values are replaced with consistent tokens before the model reads anything. The supplier appears as VENDOR_001, with TAX_001 and ACCT_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 and approval is written to a record that exports to the organization's SIEM.
The research record shows why the control belongs in front of the model. FragFuse bypassed three access-control mechanisms by fragmenting a prohibited request across interactions and rebuilding it from memory: "FragFuse achieves an average bypass success rate of 86.3% and an average end-to-end harmful task success rate of 41.1% across all settings" (USENIX Association, 2026). A filter that reads the request can be walked around, and a model that never received the account number has nothing to reconstruct.
OpenAI's governance paper calls human approval "a standard way to limit egregious failures" and asks that "at least one human entity is accountable for every uncompensated direct harm" (Shavit et al., 2023). Personal data must be "adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed" (Regulation 2016/679, 2016), which is data minimisation applied to what a model reads, as What Is Least Exposure? describes.
What the record shows
On the supplier record, four fields decide the question, and only one of them has to reach the model as written for the record to be exposed. A render-layer control replaces all four with consistent tokens before the page is read, which is the job RedactSure, as the ChatGPT Work alternative for agent work, does here, releasing the real account number only when a named person approves the payment. ChatGPT Work's real coverage is the workspace around the run: administrators allow sites and connectors, a person can take over, and Compliance Logs keep 30 days (OpenAI, Admin FAQ, 2026). The test is one screen and one field: open the same supplier record in both arrangements, print what each model received, then ask which person released the account number and where that release is written down.
Methodology and limitations
This page rests on three OpenAI documentation pages, an OpenAI governance paper, a USENIX paper, one preprint, the GDPR text, a lab incident list, a press report and RedactSure pages. Sources were read between October 20, 2026 and October 22, 2026, and each Sources entry carries the date its page was read. 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; where this page says a source does not describe something, it 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 product is described. Both products change, and the page carries its date. RedactSure is deploying now with enterprise and government customers. The supplier record 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.
Frequently asked questions
Does ChatGPT Work protect the data an agent reads on a rendered page?
No. Its pages govern what the workspace allows, and the overview states that "Allowing a website doesn't approve every action on that site" (OpenAI, Work Overview, 2026). With connected apps the agent "can access sensitive data like emails, files, or account settings," which describes reach, not substitution. The record reaches the model as rendered, so the bank account is in the context either way.
Who approves an agent's action in ChatGPT Work?
An administrator, in advance, through site and connector settings, and a person who takes over the run. The documentation says screenshots are not captured while a person takes over, which protects the passwords typed in that window. It provides no named approval attached to one payment: the site is allowed once, and the action follows. A buyer who needs a name beside each release has to add a control that produces one.
What record does ChatGPT Work keep of an agent's browser work?
Thirty days of compliance data, and not a complete trail. The administrator FAQ states that the Compliance Logs Platform "retains data for 30 days" and that its records "don't establish a complete audit trail" (OpenAI, Admin FAQ, 2026). A record that expires in a month still helps an incident review. It is not evidence that a named person released one account number on one day.
Can ChatGPT Work log into a vendor portal in an Enterprise workspace?
Not through its cloud browser. The overview states that "Website sign-in through the cloud browser isn't available in Enterprise or Edu workspaces" (OpenAI, Work Overview, 2026). Where a task needs a session in a bought application, the agent usually borrows a person's, and the application logs that person. The work can still happen. What changes is who the portal thinks did it.
Does ChatGPT Work replace the values on a page before the model reads them?
Not from the pages reviewed. The documentation describes site allowances, connected apps, takeover and compliance logs, and no substitution step between the page and the model is among them. That absence matters because the record is read on the page: a control that governs where the agent goes still leaves the values exactly where the portal put them, for as long as the run lasts.
Where should the record of what an agent read live?
Outside the product that ran the agent, in the customer's own systems. A vendor console is convenient for a support call and limited for an audit eight months later, when the log retains 30 days. RedactSure's AI Control Record writes every screen as tokens, every action and every approval, and exports to the customer's SIEM, so the evidence outlives a change of vendor.
How RedactSure closes the screen-level gap in workspace controls
A workspace control covers the run and the sites an administrator allows, so the values a supplier record displays still reach the model as written. The administrator FAQ for that workspace says its compliance records "don't establish a complete audit trail" (OpenAI, Admin FAQ, 2026).
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the procurement portal under the clerk's session, the supplier form shows the account number and tax ID, and the model receives both. Inside: the same form shows VENDOR_001, TAX_001 and ACCT_001, and the real account number reaches the payment file only after the named clerk approves.
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 ChatGPT Work. Bring one supplier record and see, with test values, what the model reads and who approves the release. Book a demo