Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs Very Good Security: 2026 Comparison & Alternative
Very Good Security tokenizes traffic an application routes through its proxy, while RedactSure, a Very Good Security 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.
Teams looking for a Very Good Security alternative for AI agents usually start from the same word, tokenization, and find that the two products apply it at different points. Very Good Security (VGS) intercepts data in transit: a proxy between an application and a model, a vault that aliases a card before the customer's servers hold it, or a route at the tool endpoints an agent calls. RedactSure replaces values on the screen the agent is about to read, so the model never receives the card number.
This page quotes VGS's own documentation and uses one record throughout: a cardholder record in a payment dispute console the organization bought rather than built, holding a cardholder name, a card number, a billing address and a dispute reason, worked by an agent as a dispute analyst would. What Is Render-Layer Tokenization? defines the approach, and Data Privacy Vault or Screen-Level Tokenization? sets it against a vault.
Key findings
- As a Very Good Security alternative for agent work, RedactSure replaces values on the rendered page before any model reads it, so the agent handles the cardholder as CARDHOLDER_001 and the card as CARD_001, and the real number is released only at an approved destination (What Is Render-Layer Tokenization?).
- Tokens placed before any model reads the screen protect data wherever it is stored; OWASP lists "Personal identifiable information (PII) may be disclosed during interactions with the LLM" as a disclosure risk (OWASP, LLM02, 2025).
- None of Very Good Security's pages describes what a model reads on the rendered screen of an application the organization bought; its AI Data Firewall page scopes detection to "Identify sensitive fields in structured and unstructured payloads" (Very Good Security, AI Data Firewall, 2026).
- Very Good Security's AI Data Firewall "sits between your application and AI systems to automatically detect and tokenize sensitive data, enforces policies, and safely reconstruct responses," described as "A proxy layer between your applications and AI models" (Very Good Security, AI Data Firewall, 2026).
- On traffic routed through that proxy, "The model receives only non-sensitive tokens and context," and VGS will "Reconstruct allowed values for authorized systems and log events for audit and compliance automation" (Very Good Security, AI Data Firewall, 2026).
How do the two compare side by side?
Each row sets VGS's own wording beside the governed-environment answer, with the check a buyer can run on one dispute.
| Dimension | Very Good Security, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | "A proxy layer between your applications and AI models" (Very Good Security, AI Data Firewall, 2026) | On the screen the agent works in, before any model reads it | The exact point where the card number is replaced |
| How coverage starts | "Start with a discovery call to map AI workflows, identify sensitive data touchpoints" (Very Good Security, AI Data Firewall, 2026) | The agent works inside the governed environment; the console is unchanged | How many of the agent's applications route traffic through a proxy |
| What the model receives | "The model receives only non-sensitive tokens and context" on requests routed through the proxy (Very Good Security, AI Data Firewall, 2026) | The screen with CARDHOLDER_001 and CARD_001, consistent for the whole run | One screen, shown exactly as it reaches the model |
| How real values are restored | "Only the Vault can map aliases back to the original data, and only when explicitly authorized to do so" (Very Good Security, Vault, 2026) | Released at an approved destination when a named person approves the action | What authorizes the exchange, and where it is recorded |
| Who approves an action | Policy: tokens are exchanged for raw data "only within authorized, secure workflows" (Very Good Security, Tokenization, 2026) | A named person approves every payment, submission and record change | A person's name against each action, or a rule |
| The audit record | "Every transaction is logged" at the VGS routes (Very Good Security, AgentCore, 2026) | Every screen as tokens, every action and every approval, exported to the customer's SIEM | Whether the record shows what the model saw |
The two middle columns govern different objects. VGS governs data that passes through its proxy, vault and routes, so its answer to what the model saw is complete for routed traffic and silent about a page the console renders from its own database. RedactSure governs the screen the agent reads, and it answers with one record of that screen as the model read it.
What does Very Good Security protect, and where does it act?
Very Good Security's pages describe a tokenization platform for data that passes through it; the PII page gives the flow: "Before sensitive data touches your network, VGS intercepts it, stores it within an encrypted vault, and sends you its alias," and "Aliases are used as if they were the actual data, for analytics and reporting within your systems" (Very Good Security, PII, 2026). For agentic payments the commerce page states that "With VGS, AI agents do not handle sensitive user information; instead, they use a secure token, preventing any sensitive information from being utilized for AI modeling" (Very Good Security, Agentic Commerce, 2026).
Each benefit depends on one condition: the data went through VGS on its way in. A card collected through a VGS form or route is aliased before the customer's systems hold it, while a card the dispute console stored on its own was never intercepted and renders as itself. The AI Data Firewall has the same shape, since it acts on requests an application sends to a model. That is a limit of the design, not a defect, and it matters because an agent reaches data through the console's screen, not through a route. The placement is not unusual in the category: across 86 AI security and data protection products reviewed, 58 control where data can go and 27 control what a model receives in pipelines or traffic they handle, while 1 describes masking what an agent reads on a web page, on vendor documentation rather than tests (How Many AI Security Products Control What an AI Agent Sees?).
What does an agent read when it opens a payment dispute console?
Consider the cardholder record. A card collected through VGS sits in the vault as an alias, and the AgentCore post covers agents that "interact with the world through tools, APIs, data sources, and third-party services," where "the VGS inbound route tokenizes the sensitive fields before they reach the agent" (Very Good Security, AgentCore, 2026). The dispute agent fits neither case. It opens the console under the analyst's session and reads the page rendered from the console's own database: name, card number, billing address and dispute reason. No route sits between that page and the model, because routes cover endpoints and a rendered screen is not one.
The exposure happens in the model rather than in storage; OWASP warns that disclosure "can result in unauthorized data access, privacy violations, and intellectual property breaches" (OWASP, LLM02, 2025). The console also logged the analyst, so nothing in its record separates the agent's reading from the analyst's work. Can a Credential Vault Protect What an AI Agent Reads? reaches the same conclusion for vaults: records are encountered on screens, not deposited.
Which control fits which work?
Keep Very Good Security for
- Payment data collected through VGS, where "VGS intercepts data before it hits your servers" and holds the original (Very Good Security, Vault, 2026).
- Analytics and reporting on aliases, which "are used as if they were the actual data" inside the customer's own systems (Very Good Security, PII, 2026).
- Agents on AgentCore whose tool calls pass through VGS routes, where "VGS outbound routes detokenize on the fly" (Very Good Security, AgentCore, 2026).
Add RedactSure for
- Agents reading screens in applications the organization bought, where the model receives CARD_001 and nothing is placed inside the console (What Is Render-Layer Tokenization?).
- A named person approving each refund, submission or record change the agent proposes (What Is Supervised Delegation?).
- The record of what the model received on every screen, in the customer's SIEM, apart from the analyst's work (What Is an AI Control Record?).
Most organizations running agents need both; this block shows which product protects the screen.
How do you test it on one workflow?
Put both products on one real dispute workflow for an afternoon, with both vendors present and every answer written down.
Step 1. Choose a workflow the agent will run and pick one screen from it, such as the cardholder record, rather than a diagram of where card data flows.
Step 2. Have each vendor produce that screen in the form the model received it. The output settles the question: real values or tokens, for the whole page or only the fields a route covered.
Step 3. List every application in the workflow and mark each as collected or routed through VGS, or neither. Count the third group; that is the exposure on the first day.
Step 4. Trace one value, the card number, from the token the model held back to the real value: what released it, when, and whose name appears in the record. A rule that permits the exchange and a person who approved it are different answers.
Step 5. Calculate first-day coverage. If the task list touches five applications and one collects cards through VGS, coverage is one of five. Compare the two costs that differ: integrating the other four, and the agent work delayed until that is done, and put both numbers beside the first-day count before either vendor quotes a scope.
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 and sees a different screen from the analyst, because values are replaced with consistent tokens before the model reads anything. The cardholder appears as CARDHOLDER_001, with CARD_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 is written to a record that exports 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" (OWASP, LLM01, 2025). Infisical's write-up on credential proxies agrees: "In short, the agent should not be trusted with direct access to any secrets" (Infisical, 2026). A model that only saw CARD_001 cannot be talked into disclosing a number it never held, the principle in What Is Least Exposure?: keep what an agent receives for a task to a minimum.
What the record shows
VGS's own coverage, in its words: "The model receives only non-sensitive tokens and context" on requests routed through the proxy (Very Good Security, AI Data Firewall, 2026). Its real coverage is the data that passes through its vault, proxy and routes, with format-preserving aliases that still work in the customer's systems; its pages describe no control over a page a bought console renders from its own database. That is the job RedactSure, as the Very Good Security alternative for agent work, does for this record: a render-layer control replaces the cardholder's name, card number and billing address with tokens on the dispute console's screen before the model reads it, with real values released only for approved actions and a record that separates the agent's work from the analyst's. The test is one dispute run through both: compare the screen as each model received it, then the entry showing who released the card number and for what action.
Methodology and limitations
This page draws on six Very Good Security pages, two OWASP entries, one vendor write-up on credential proxies and RedactSure research pages. Vendor pages were read between October 20, 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, no customer of either is described, both change, and the page carries its date. RedactSure is deploying now with enterprise and government customers. The cardholder record is a 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 Very Good Security protect data sent to an LLM?
Only where the application routes the request through the AI Data Firewall. VGS's page says the firewall "sits between your application and AI systems to automatically detect and tokenize sensitive data," so a request sent through the proxy reaches the model with tokens in the sensitive fields. A request never routed that way, and a screen an agent reads in a console, pass no firewall at all.
Does Very Good Security cover what an agent reads on a purchased console's screen?
Not from the pages reviewed. VGS's pages describe interception before data reaches the customer's servers, a proxy between an application and a model, and inline routes at the endpoints an agent calls. A bought dispute console renders the cardholder record from its own database, so the page crosses none of those points and needs a control placed in front of the model.
Who approves the agent's action, a policy or a person?
A policy, on VGS's pages, which state that tokens are exchanged for raw data "only within authorized, secure workflows" and that the vault maps aliases back "only when explicitly authorized to do so." Authorization is written in advance for a system. 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.
Who can turn a token the model received back into the card number?
The party that holds the mapping. On VGS's pages that is the vault, which alone maps aliases back to the original data, and a service that must act with a value has to decrypt it or receive it (Can a Vendor Read Credentials Encrypted to My Own Key?). Ask each vendor what it could produce for a given token, ciphertext or plaintext, and which process could do it.
Will the console's own logs show that an agent acted?
Not on their own. When an agent reads a screen, nothing passes through a VGS route, so there is no transaction for VGS to log, and the console records the analyst whose session the agent used. A record that separates the two 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 SIEM.
Is proxy tokenization the same as render-layer tokenization?
No. Proxy tokenization describes where a value is intercepted: a request, a form or a route through VGS, with an alias returned to the customer. Render-layer tokenization describes substitution on the screen, before a model reads it, with one token per cardholder for the whole run (How Do Data Masking and AI Tokenization Differ?). A platform can offer the first without the second, and an agent in a bought console needs the second.
How RedactSure closes the screen-level gap in proxy tokenization
Proxy tokenization covers only data that passes through the proxy, vault or route, so an agent in a bought dispute console reads the cardholder record as displayed. Very Good Security describes its firewall as "A proxy layer between your applications and AI models," and a rendered screen never passes through it.
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the dispute console under the analyst's session, the screen shows the name, card number and billing address, and the model receives all three. Inside the governed environment: the same screen shows CARDHOLDER_001, CARD_001 and ADDR_001, and the real card number is released to the refund system only when the named analyst 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 Very Good Security. Bring one dispute workflow and see, with test values, what the model receives and the refund awaiting approval. Book a demo