Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
Published
Last updated
RedactSure vs Skyhigh Security: 2026 Comparison & Alternative
Skyhigh Security scans prompts, uploads and files through its proxy and AI APIs, while RedactSure, a Skyhigh Security alternative built for AI agents, tokenizes the screen before a 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.
The case for a Skyhigh Security alternative starts on a screen an employee already uses: an HR console that shows a name, a Social Security number, a home address and a salary. Skyhigh Security scans prompts, uploads and files as they cross its proxy or an API connection to a sanctioned AI application. RedactSure replaces those identifiers on the rendered page before a model reads it, so the model never holds the salary figure.
One employee record sits in a cloud HR application, holding a name, a Social Security number, a home address and a salary, opened by an agent that works the console as an HR specialist does. Skyhigh's own documentation places its controls on traffic through a proxy and on API connections to sanctioned AI applications. What Is Render-Layer Tokenization? defines the substitution, and What Does an AI Agent See in a Screenshot? describes what a model takes from a rendered page.
Key findings
- As a Skyhigh Security alternative for agent work, RedactSure replaces values on the rendered HR screen before any model reads it, so the agent works with EMP_001, SSN_001 and ADDR_001 and real values resolve only at an approved destination (What Is Render-Layer Tokenization?).
- A model that received only tokens cannot be steered into repeating a record it never held, and OWASP lists "Personal identifiable information (PII) may be disclosed during interactions with the LLM" as the disclosure risk that starts with what a model is given (OWASP, LLM02, 2025).
- None of Skyhigh Security's pages reviewed describes an agent reading a rendered application screen or a substitution of values on it; its GenAI DLP is "delivered through two distinct modes," an inline proxy and direct API connectivity (Skyhigh Security, DLP for Gen AI, 2026).
- The GenAI page puts its reach at "over 1,900 AI applications" and says the proxy mode provides "real-time pre-scans for web-based applications" (Skyhigh Security, DLP for Gen AI, 2026).
- Where a violation surfaces in a sanctioned AI application, remediation can be retroactive: the ChatGPT page says Skyhigh "deletes the entire conversation (prompt and response) that includes keywords specified in the policy" (Skyhigh Security, ChatGPT, 2026).
How do the two compare side by side?
Each row sets a Skyhigh Security statement from its own pages beside what a render-layer control does on the same point, with the check a buyer runs on one HR task.
| Dimension | Skyhigh Security, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | Cloud traffic "is redirected to this proxy and filtered according to the rules of your DLP policies" (Skyhigh Security, Data Loss Prevention, 2026) | On the screen the agent works in, before any model reads it | The point where the Social Security number is replaced |
| How coverage starts | "The policy is applied to all traffic by default," with API connections for sanctioned AI applications (Skyhigh Security, Shadow/Web DLP, 2026) | The agent works inside the governed environment; the HR application is unchanged | How many of the agent's applications are covered today |
| What the model receives | "DLP on prompts and responses for messages is enforced through Copilot integration" (Skyhigh Security, Microsoft 365 Copilot, 2026) | The screen with EMP_001, SSN_001 and ADDR_001, consistent for the whole run | One HR screen, shown exactly as the model received it |
| How real values are restored | "The file is blocked from Copilot access" when sensitive information is detected (Skyhigh Security, Microsoft 365 Copilot, 2026) | Released at the approved destination when a named person approves the action | What releases the salary figure to payroll, and whose name is recorded |
| Who approves an action | "Skyhigh enforces remediation actions such as deleting content or sending notification emails" (Skyhigh Security, ChatGPT, 2026) | A named person approves every payment, submission and record change | A person's name on the record change, not a policy match |
| The audit record | "Generate incidents for policy violations" (Skyhigh Security, 7.1.2 Release Notes, 2026) | Every screen as tokens, every action and approval, exported to the customer's SIEM | Whether the record shows what the model saw, under the agent's identity |
Read down the two middle columns and the difference is the object each product governs: data crossing a path Skyhigh inspects, against the screen an agent reads. They also differ on what turns a token back into a real value, a policy action on Skyhigh's pages against a named person's approval in RedactSure's design (What Is Supervised Delegation?).
What does Skyhigh Security protect, and where does it act?
Skyhigh Security's GenAI DLP is aimed at the data a person or an application sends to an AI tool, and its own description names "two distinct modes": an inline proxy that provides "real-time protection of sensitive data during file uploads" and an API mode built on "direct API connectivity" (Skyhigh Security, DLP for Gen AI, 2026). The proxy mode covers web forms and uploads; the API mode reaches sanctioned AI applications directly.
The web policy is broad by design: it is "applied to all traffic by default" and can "detect and block all Personally Identifiable Information (PII) within a specific Shadow cloud service's UI" (Skyhigh Security, Shadow/Web DLP, 2026). Copilot is covered through an API integration on prompts and responses, and a sensitive file is blocked from Copilot and from indexing.
Each benefit depends on the data crossing a path Skyhigh inspects: a proxy, an API connection, a managed file store, or a prompt typed into a sanctioned application. Traffic from users working in the cloud "is redirected to this proxy and filtered according to the rules of your DLP policies" (Skyhigh Security, Data Loss Prevention, 2026), and the pages describe detection, blocking and deletion, not replacement (Runtime Tokenization vs DLP for AI Agents). That is a boundary of a traffic control, not a flaw: a rule cannot act on a page that never leaves the browser.
What does an agent read when it opens an employee record in a cloud HR application?
Consider the employee record. The copy in the HR platform's database may be protected, but the agent never goes there; it opens the console the way an HR specialist does and reads the screen it displays. A proxy inspects the traffic that draws that page, and when the page is allowed, the model receives it as rendered; no rule replaces a value in it. It is the application's own rendering, assembled from a session the platform issued. The API integrations act on prompts and responses sent to Copilot, ChatGPT, Claude or Gemini, which is after the agent has read the record.
The exposure happens in the model: OWASP warns that disclosure "can result in unauthorized data access, privacy violations, and intellectual property breaches," which no traffic verdict reaches once the page has been read (OWASP, LLM02, 2025). There is also an identity gap. The agent used the specialist's session, so the HR platform logs the specialist, and an incident in the proxy would name the person whose account carried the request. Neither record answers what an auditor asks first: what did the model see.
The review of 86 AI security products places Skyhigh among the 58 that control where data can go; its public documentation describes a DLP incident record that copies the content behind a violation and a SIEM export, and none of the 86 describes swapping the values an agent reads on a rendered page for tokens it can work with (How Many AI Security Products Control What an AI Agent Sees?).
Which control fits which work?
Keep Skyhigh Security for
- Web uploads and Shadow services, where the policy "is applied to all traffic by default" (Skyhigh Security, Shadow/Web DLP, 2026).
- Prompts and responses in Copilot, ChatGPT, Claude and Gemini, where Skyhigh will "inspect prompts and responses as they occur" (Skyhigh Security, 7.1.2 Release Notes, 2026).
- Access control over which AI applications a person can reach, since Skyhigh can "restrict access to approved, low-risk AI applications" (Skyhigh Security, Skyhigh AI, 2026).
Add RedactSure for
- Agents reading screens in applications the organization bought, where the model receives EMP_001 instead of the real value (What Is Render-Layer Tokenization?).
- An approval by name for every payment, submission and record change the agent puts forward (What Is Supervised Delegation?).
- The record of what the model received on each screen, exported to the customer's SIEM (What Is an AI Control Record?).
Most organizations running agents will want both controls, one for the traffic and one for the screen.
How do you test it on one workflow?
Compare the two on one real HR workflow, over an afternoon, with both vendors present and every answer written down.
Step 1. Choose a workflow the agent will run, such as onboarding or a salary change, and pick one screen from it, the employee record, not a diagram of the data flow.
Step 2. Have each vendor replay the model's input for that screen, field by field. The output settles the question: real values or tokens, and what the model actually read.
Step 3. List every application in the workflow and mark each as covered by the proxy, reached through an API connection, or neither. The third group is the first day's exposure; do the count internally.
Step 4. Trace one value, the Social Security number, from the screen to the payroll system: what released it, when, and whose name is in the record. A policy that blocked or deleted a prompt is a rule written in advance; ask which product records a named approver.
Step 5. Calculate first-day coverage. If the workflow touches six applications and the proxy or an API connection covers two, the agent's reads are covered two of six. Compare the costs that differ: policy tuning for an agent's traffic, and the agent work delayed until it is done.
What does a governed environment change?
A governed environment is a class of control defined by where it acts. For each task the planner proposes what to tokenize, starting from personal information, account numbers, credentials and record numbers.The agent runs under its own session, recorded under its own identity, and sees a different screen from the HR specialist: each identifier becomes a consistent token before the model reads anything, so the employee is EMP_001, with SSN_001 and ADDR_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 listed mitigation is to "Restrict the model's access privileges to the minimum necessary for its intended operations," which for an agent means the page as well as the tools (OWASP, LLM01, 2025). A model that only ever received SSN_001 has no number to send, which is the principle in What Is Least Exposure? and the argument in AI Gateway, DLP or Tokens: What Controls AI Exposure?.
What the record shows
The HR console shows tokens in place of the employee's name, Social Security number, home address and salary before the model reads the screen, and the real values appear only for an approved payroll change, with a record that separates the agent's read from the specialist's. Skyhigh Security's real coverage is the traffic, uploads, prompts and files its proxy and API connections inspect, and the incidents those inspections generate; its pages describe no control over what a model reads on the screen of a bought application. Pick one salary change for one employee on a test record, ask each vendor to replay the model's input from that screen, and follow the Social Security number to the payroll record that names the approver.
Methodology and limitations
This page rests on seven Skyhigh Security pages, two OWASP Top 10 for LLM Applications 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. 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, 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 here replaces a pilot of one workflow on its own screens.
Frequently asked questions
Does Skyhigh Security protect what a model receives from an AI application?
Only where the data crosses something Skyhigh inspects. Its API integrations cover Copilot, ChatGPT, Claude and Gemini, and the Copilot page says "DLP on prompts and responses for messages is enforced through Copilot integration" (Skyhigh Security, Microsoft 365 Copilot, 2026). An agent opening a bought HR console receives the record before any prompt is sent, so inspection begins after the read.
Does Skyhigh Security cover what an agent reads on an HR console screen?
Not from the pages reviewed. Skyhigh's controls sit on traffic through the inline proxy, on uploads, and on API connections to sanctioned AI applications; the GenAI page describes "two distinct modes," neither of which acts on a rendered page (Skyhigh Security, DLP for Gen AI, 2026). The HR console is a bought application the agent reads directly, so no sensitive content moves until the model has it.
Who approves the agent's record change, a policy or a person?
A policy, on Skyhigh Security's pages, which state that "Skyhigh enforces remediation actions such as deleting content or sending notification emails for the incident" (Skyhigh Security, ChatGPT, 2026). That decision was written before the agent acted. In RedactSure's design a named person approves each payment, submission or record change when the agent proposes it, and the approval carries a name.
What record shows that the agent, not the HR specialist, read the employee record?
An incident record, not a screen record. Skyhigh generates an incident for a policy violation and can delete the content, and those incidents follow a policy match in traffic or an API connection. The HR application logs the specialist whose session was used, so neither record separates the agent's read from the person's on its own.
Can a hidden instruction in a page make the agent leak the record?
Only if the model holds a real value to leak. OWASP notes that "indirect prompt injections occur when an LLM accepts input from external sources, such as websites or files" (OWASP, LLM01, 2025), and an HR console is such a source. A model that read SSN_001 can be steered and still has no number to send, while a model that read the Social Security number has one.
Is web DLP the same as render-layer tokenization?
No. Web DLP reads a transfer or a prompt, matches it against a classification rule and blocks, deletes or reports what it finds. Render-layer tokenization changes what the screen contains before a model reads it, so the value never enters the model to be matched later. One controls a path through the network, the other controls the content of a page, and a program can use one without the other.
How RedactSure closes the screen-level gap in traffic-based DLP
DLP acts on a transfer or a prompt its policy recognizes, so an agent opening a bought HR console receives the whole record before any rule sees it. Skyhigh's ChatGPT control deletes a conversation that matches a policy, after the model has 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 HR console under the specialist's session, the screen shows the name, Social Security number, home address and salary, and the model receives all four. Inside the governed environment: the screen shows EMP_001, SSN_001 and ADDR_001, the real number reaches payroll only when the named specialist approves the change, and the approval lands in the 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 Skyhigh Security. Bring one HR task and see, with test values, the record the model reads and the approval that releases it. Book a demo