Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs Aurascape: 2026 Comparison & Alternative
Aurascape inspects prompts, responses and gateway-routed tool calls inline, while RedactSure, an Aurascape alternative built for AI agents, tokenizes the screen before any 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 an Aurascape alternative for AI agents usually start with one question, where the control sits, and get two answers. Aurascape places its control on AI traffic: an AI Proxy that inspects prompts and responses, and an MCP Gateway that verifies the tool calls routed through it. RedactSure places its control on the screen an agent is about to read, so the model never receives the customer's real name or account number.
This page quotes Aurascape's own pages and uses one example throughout: a customer account record in a CRM the organization bought rather than built, holding a name, date of birth, phone number, account number and open case notes, worked by a computer use agent the way a service representative would. What Is Render-Layer Tokenization? defines the approach, and What Does an AI Agent See in a Screenshot? shows what it takes in.
Key findings
- As an Aurascape alternative for agent work, RedactSure replaces values on the rendered CRM screen before any model reads it, so the agent handles the customer as CUST_001 and the real value is released only at an approved destination (What Is Render-Layer Tokenization?).
- Data a model receives is an exposure wherever the control sits; OWASP warns that "Personal identifiable information (PII) may be disclosed during interactions with the LLM" (OWASP, LLM02, 2025).
- None of Aurascape's pages describes what a computer use agent reads on the rendered screen of a bought CRM; the discovery post ties inspection to a "Decoder Bot" per AI application and says "perfect coverage is not guaranteed" (Aurascape, Discovery, 2026).
- Aurascape's solution page says the platform "governs the model conversation and gateway-routed tool execution in real time, across the agents employees use and the agents your teams build," a control on the conversation and the routed call, not the screen behind them (Aurascape, Secure Agentic AI, 2026).
- On the model side, "Prompts, responses, and instructions are inspected and policy-enforced in real time through the AI Proxy," with guardrails for "PII filtering, credential guard, code-injection checks, and output sanitization," applied to traffic the proxy decodes (Aurascape, Secure Agentic AI, 2026).
- The launch post states the limit of any gateway: "A standalone MCP gateway can only govern the traffic explicitly routed through it" (Aurascape, Launch, 2026).
How do the two compare side by side?
The table sets Aurascape's wording beside what a governed environment does on the same point, with the check a buyer can run.
| Dimension | Aurascape, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | On AI traffic, "combining the Aurascape MCP Gateway and the Aurascape AI Proxy" (Aurascape, Launch, 2026) | On the screen the agent works in, before any model reads it | Where a value is replaced |
| How coverage starts | A decoder per AI application; for tools, "the gateway deploys in minutes by prepending one prefix to existing MCP server URLs" (Aurascape, Secure Agentic AI, 2026) | The agent works inside the governed environment; the CRM is unchanged | How many of the agent's applications are covered |
| What the model receives | Inspected prompts: "Inspect full prompts and responses to stop risky behavior" (Aurascape, Product, 2026) | The screen with consistent tokens, CUST_001 and ACCT_001, for the whole run | One screen, exactly as the model saw it |
| How real values are restored | "A cloud access key deep within a prompt is blocked or masked before it is sent to the model" (Aurascape, Coding, 2026); no resolve step is described | Released at an approved destination when a named person approves the action | Who authorizes the release, and where it is recorded |
| Who approves an action | A policy: "Block or redirect risky actions before they complete in governed deployments" (Aurascape, Governance, 2026) | A named person approves every payment, submission and record change | A person's name, not a rule |
| The audit record | "Audit-ready logs of agent conversations, tool calls, and data exchanges, with data lineage across systems" (Aurascape, Secure Agentic AI, 2026) | Every screen as tokens, every action and every approval, exported to the customer's SIEM | Whether it shows what the model saw |
Read down the two middle columns and the difference is the object each product governs. Aurascape governs AI traffic through its proxy and gateway, so its answer to what the model saw covers decoded and routed paths and not a CRM screen. RedactSure governs what an agent reads, so its answer is the screen itself, recorded as the model saw it. They differ too on who restores a masked value: a policy on Aurascape's pages, a named person in RedactSure's design (What Is Supervised Delegation?).
What does Aurascape protect, and where does it act?
Aurascape acts on AI traffic. Its product page promises "inline visibility and control across AI apps, MCP tools, and custom agents," and the ability to "Protect sensitive data across prompts, responses, and tool use" (Aurascape, Product, 2026). For coding assistants it will "Redact keys, tokens, and credentials in flight before an assistant or an unapproved model ever receives them" (Aurascape, Coding, 2026). A prompt carrying an access key, or a tool call bound for an external system, meets a control on the way.
Each benefit depends on one condition: the traffic has to pass through a path the platform decodes or a gateway it fronts. The governance page describes "applying policy before an agent reaches an external system on supported paths," and that phrase, supported paths, is the condition (Aurascape, Governance, 2026). It is a limit of the design rather than a defect, and it matters because a computer use agent reaches the customer record through the CRM screen, neither a decoded prompt nor a signed tool call; the pages reviewed describe no control over that rendered screen.
Twenty-seven of the 86 products reviewed control what a model receives in the traffic they handle, and Aurascape is one of them; its public documentation describes audit-ready logs of agent conversations, tool calls and data exchanges, while the same review records no product where a token stands in for a value on a screen and the agent keeps working (How Many AI Security Products Control What an AI Agent Sees?).
What does an agent read when it opens a customer account record?
The agent opens the CRM the way a service representative does, in a browser session, and reads the screen it displays: name, date of birth, phone number, account number and open case notes. No proxy decodes that page. The discovery post explains that a "Decoder Bot enables real-time conversation inspection, data loss prevention, and access control enforcement" for each AI application, and adds that "perfect coverage is not guaranteed" (Aurascape, Discovery, 2026). A CRM is not an AI application, and a screen read is not a tool call routed through a gateway. The governance page names the objects it covers as "The sensitive or regulated data in the prompt, upload, response, or generated code" (Aurascape, Governance, 2026); a rendered screen is none of those four.
The model receives the record as shown, and an agent working from screenshots takes in everything visible (What Does an AI Agent See in a Screenshot?). The agent also used the representative's session, so the CRM's log records the representative. Both gaps close at the rendered screen, where values can become consistent tokens before the model reads them.
Which control fits which work?
Keep Aurascape for
- Employee AI use, enforcing "which apps, tenants, licenses, and models can be used with enterprise data" (Aurascape, Governance, 2026).
- Agents built on MCP, where tool calls "are verified, signed, and controlled before reaching an external system" (Aurascape, Secure Agentic AI, 2026).
- Replacing URL-layer policy "blind to what is actually flowing through prompts, responses, and agent tool calls" (Aurascape, Launch, 2026).
Add RedactSure for
- Agents reading screens in bought applications such as the CRM, where the model receives CUST_001 (What Is Render-Layer Tokenization?).
- A named person approving each payment, submission or record change it proposes in the CRM (What Is Supervised Delegation?).
- The record of what the model received on every screen, in the customer's SIEM (What Is an AI Control Record?), kept when the model changes (Can AI Models Change Without Changing Their Controls?).
An organization that runs agents usually needs both; what this block settles is which product protects the screen.
How do you test it on one workflow?
Put the two side by side on one real workflow, with both vendors in the room and every answer written down.
Step 1. Choose a workflow the agent will run, such as updating a phone number after a call, and pick one screen from it, the customer account record, not a diagram of AI traffic.
Step 2. Ask each vendor to show that screen exactly as the model saw it: for Aurascape, which decoder or gateway the CRM screen passed through; for the render-layer vendor, the screen with its tokens in place. The output settles whether the model saw a real date of birth or DOB_001.
Step 3. List every application in the workflow and mark each as decoded by the proxy, fronted by the gateway, or neither. The third group, usually the bought applications worked through the screen, is the first-day exposure.
Step 4. Trace the account number from the model back to the action that used it: what released it, when, whose name is in the record, and whether the entry shows the screen the model read or only the call.
Step 5. Calculate first-day coverage: five applications in the task list and one MCP server behind the gateway is one of five. Compare the costs that differ: decoding and routing the remaining applications, and the agent work delayed until then.
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, DOB_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, action and approval is written to a record exported to the organization's SIEM.
OWASP shows why the control sits in front of the model rather than on the traffic: it 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" (OWASP, LLM01, 2025). A CRM screen with open case notes is such a source, and a model that only saw CUST_001 cannot be coerced into disclosing a number it never received. That is the principle in What Is Least Exposure?, and the test AI Gateway, DLP or Tokens: What Controls AI Exposure? applies to each layer.
What the record shows
Who decides what the model receives, and whose name is on the release? The render layer replaces the customer's name, date of birth and account number with tokens on the CRM screen before the model reads it, and that is the job RedactSure, as the Aurascape alternative for agent work, does for this record, with real values released only for approved actions and a record that separates the agent's work from the representative's. Aurascape's real coverage is the AI traffic its proxy decodes and the tool calls its gateway fronts; its pages describe no control over what a model reads on a bought application's screen. The test takes one agent and one CRM task: have each vendor show the account screen the model read, then trace the account number back to the action that released it and the person who approved it.
Methodology and limitations
This page draws on six Aurascape pages, two OWASP entries 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; a source said not to describe something refers to the pages reviewed.
Both products are described from their documentation and neither was tested for this page; no customer of either is described, and the page carries its date. RedactSure is deploying now with enterprise and government customers. The customer account record is a record type, not an organization. Determinations belong to the organization's counsel, assessors and security team, and nothing replaces a pilot of one workflow on its own screens.
Frequently asked questions
Does Aurascape protect data sent to an LLM?
Only on the paths it decodes or fronts. Aurascape's pages describe an AI Proxy that inspects prompts and responses, with PII filtering, and an MCP Gateway that verifies tool calls routed through it. When a computer use agent reads a customer record on a CRM screen, that read is neither a decoded prompt nor a routed tool call, so the data reaches the model past both.
Does Aurascape cover what an agent reads on a CRM screen?
Not from the pages reviewed. The product page lists coverage across AI apps, MCP tools and custom agents, and the discovery post ties inspection to a decoder built for each AI application. A bought CRM is not an AI application and has no decoder, so the screen the agent reads sits outside those paths, and a control for it has to sit in front of the model.
Who approves the agent's action, a policy or a person?
A policy, on Aurascape's pages, which say risky actions are blocked or redirected before they complete in governed deployments and that a coding command can be held for confirmation without naming who confirms. That decision is a rule written in advance. In RedactSure's design a named person approves each payment, submission or record change when the agent proposes it, and the approval carries the person's name.
Does Aurascape's audit record show what the model saw on the screen?
Only for the conversations and tool calls it inspected. The solution page describes audit-ready logs of agent conversations, tool calls and data exchanges, a full record for proxied or routed traffic. A screen read by a computer use agent produces no entry, because nothing passed through either. RedactSure's AI Control Record keeps every screen as the model saw it, with each action and approval, in the customer's SIEM.
Does an MCP gateway govern a computer use agent's reads?
No. Aurascape's own launch post says a standalone MCP gateway can only govern the traffic explicitly routed through it, which is why it pairs the gateway with the AI Proxy. A computer use agent that works a CRM screen sends no tool call to a gateway; it reads pixels or page elements and acts through the browser, so the control has to sit in that browser session.
Is Copilot Unlearning the same as never showing the model the value?
No. Aurascape's product page describes Copilot Unlearning as removing content from memory and visibility after sensitive data is exposed, which is remediation: the model held the value first. Render-layer tokenization works before the exposure, so the model receives CUST_001 and there is nothing to unlearn. The two can coexist; a buyer should ask which incidents each would have prevented.
How RedactSure closes the screen-level gap in inline AI traffic inspection
Inline inspection covers the prompts, responses and tool calls routed through it, so an agent working a bought CRM reads the customer record as displayed. The vendor's own launch post says "A standalone MCP gateway can only govern the traffic explicitly routed through it" (Aurascape, Launch, 2026).
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 screen shows the name, date of birth, phone number and account number, and the model receives all four. Inside the governed environment: the same screen shows CUST_001, DOB_001 and ACCT_001, and the real value is written back to the account only when the named representative approves the record 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 Aurascape. Bring one CRM task and see, with test values, the screen the model receives and the change awaiting approval. Book a demo