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Comparison · By Chris Sowa · Published
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What Is the Difference Between RedactSure and Cyera for Enterprise Data Exposure?

Cyera Agent Guardian inventories AI assets and blocks risky agent operations against policy. RedactSure works on data in use: what an AI agent's model receives from each screen and who approves each action. The Cyera page reviewed does not state redaction, masking or tokenization.

Application records are classified on a data panel before an access gate releases selected destinations.

Cyera Agent Guardian inventories AI assets and blocks risky agent operations against policy (Cyera, 2026). RedactSure works on data in use: what an AI agent's model receives from each screen and who approves each action. The Cyera page reviewed does not state redaction, masking or tokenization. The question matters because exposure used to mean a misconfigured bucket or an overshared folder, and it now also means a model reading a customer record while it completes a task. An inventory can tell a security team that 40 systems hold Social Security numbers. It cannot tell the team which of those numbers an agent read at 2 p.m. on Tuesday, because that happens at the moment a screen is rendered, which is where render-layer tokenization operates. The comparison below runs on starting point, layer, treatment of a sensitive value before a model sees it, and control over agent actions. The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.

Key findings

  • Cyera Agent Guardian, an AI agent security platform, inventories AI assets, examines prompts, tool calls and data access, and can block risky operations and quarantine agents (Cyera, 2026). The page reviewed does not state redaction, masking, tokenization or approval workflows.
  • OWASP lists sensitive information disclosure as LLM02 in its Top 10 for LLM Applications 2025, a risk that turns on what data a model is given in the first place (OWASP, 2025).
  • OWASP lists prompt injection as LLM01, the first risk in the same list (OWASP, 2025), and indirect injection arrives through content an application reads, not only through what a user types (Greshake et al., 2023).
  • Researchers at ServiceNow, Mila and partner institutions report that a model-agnostic Minimizer, which strips data a tool call does not need, and a Sanitizer on tool outputs reduce attack success to near zero on four agent benchmarks (Bhagwatkar et al., 2026).
  • In Accenture's Technology Vision 2025 survey, 38 percent of executives expect agents to access data from across the organization within three years, and trust is defined as accuracy, predictability, consistency and traceability (Accenture, 2025).
  • RedactSure operates at the render layer of a governed environment: values chosen by policy become consistent tokens before any model reads the screen, and a named person approves each consequential action under Supervised Delegation, with deployments in pilot.

What problem does each product start from?

Cyera starts from the AI estate. Its Agent Guardian page describes inventorying AI assets across cloud, SaaS, browser apps and employee workstations, then examining prompts, tool calls, data access, identities and agent configurations (Cyera, 2026). It detects and classifies sensitive data, alerts on risky behavior and blocks dangerous actions. The unit of work is the AI asset and the interactions it produces.

That starting point answers a question every security program faces before it can govern AI: which agents are running and what they touch. An inventory gives the team a map, and policy enforcement on tool calls and data access acts on it. The page reviewed does not state where the product sits relative to the model or the screen; it lists an AI Gateway Plugin, Cyera Browser Shield and Cyera Endpoint as deployment options.

RedactSure starts from the agent at work. Its supervised AI co-workers complete workflows across an organization's existing applications, an ERP, a claims platform, an EHR or a student information system, inside a governed environment, with no per-application integration and no change to user permissions. The unit of work is the task: one invoice, one claim, one budget transfer. The question RedactSure answers is narrower and later in time than an inventory. For this piece of work, on this screen, what should the model receive, and who signs off on the step that changes a record?

The two starting points produce different definitions of success. For an inventory and enforcement control, success is a known AI estate and risky operations stopped. For a render-layer run control, success is a model that received only what the task required, and a record of every step with a named approver. Both are forms of reducing exposure. They act on different objects, a detected operation versus the values a model receives, which is why the comparison is about layer, not about which product is better.

What does enterprise data exposure mean once an agent does the work?

AI agent data exposure is the set of sensitive values an agent's model actually receives while it works, as distinct from everything the agent could reach. The distinction matters because agents read through applications. When an agent opens a vendor record in an accounts payable screen, the application renders the vendor name, the bank account, the tax identifier and the remittance address together. The agent's permissions are the user's permissions, and the screen shows what the user would see. Nothing about the inventory changes. The exposure happens anyway.

Estate controls measure reach: which identities could read which stores. Mapping all the data an AI agent can reach is the right first exercise, and an estate platform is the natural place to run it. Reach, though, is an upper bound. An agent assigned to match 200 invoices to purchase orders needs amounts, dates, invoice numbers and vendor identity as a stable reference. It does not need the vendor's bank account number in clear to do the matching. Whether the model receives that number is decided at the moment of reading, not at the moment of classifying.

Accenture's survey points to the scale of the question. Executives expect agents to draw on data from across the organization within a few years (38 percent within three years), and they define trust in AI operationally as accuracy, predictability, consistency and traceability (Accenture, 2025). Traceability is the operative word for exposure. An organization that cannot show what a given agent read on a given run cannot show that the agent read only what it needed.

The security research says the same thing from the attacker's side. Indirect prompt injection places instructions inside content an application retrieves, so whatever an agent reads can steer it (Greshake et al., 2023). Microsoft's AI Red Team catalogues agent failure modes in which agents act on poisoned inputs or leak data across boundaries (Microsoft, 2025). In each case the damage depends on what the model holds when the attack lands. An estate can be well classified and still hand a model every identifier on a screen.

How do the two compare side by side?

The table places the two on six dimensions. The Cyera column reflects the Cyera Agent Guardian page reviewed (Cyera, 2026); where that page does not state a point, the cell says so.

Dimension Cyera (per its own description) RedactSure (per its published design)
What it is AI agent security platform: "Secure everything your AI can see and do" (Cyera, 2026) AI agent controls, governance and data protection for supervised AI co-workers working existing applications
Primary object AI assets and their prompts, tool calls, data access, identities and agent configurations One agent run on one task, screen by screen
Where it sits Not stated relative to the model or screen on the page reviewed; deployment options include AI Gateway Plugin, Browser Shield and Endpoint The render layer inside a governed virtual environment, before any model reads the screen
Sensitive value before a model sees it Detects, classifies, alerts and blocks; redaction, masking and tokenization not stated on the page reviewed Values chosen by policy become consistent tokens such as SSN_001 and ACCT_001; real values resolve only at approved destinations at the moment of an approved action
Agent actions and approval Real-time policy evaluation of tool invocations and data access, blocking and agent quarantine; approval workflows not stated on the page reviewed The Planner sets policy, a named person confirms it, and a named person approves every payment, submission and record change
Record Timestamped validation runs; AI Activity Monitoring with audit trail and file lineage; AI Compliance with automated evidence and reporting Every action logged, human and computer; all PII as tokens, in the AI Control Record, exported to the customer's SIEM

The table is not a ranking. Cyera covers AI assets across cloud, SaaS, browsers and endpoints, a breadth no single agent run covers, and RedactSure does not claim to discover or classify an organization's data at rest. RedactSure works at a moment the page reviewed does not describe: the instant a model is about to read a rendered screen during a workflow. Where a cell says the page does not state something, the right next step is Cyera's own documentation or a demonstration, not an inference.

Two rows carry most of the difference. The fourth row is about what reaches the model. Classification tells a team that a field holds a bank account number; tokenization at the render layer decides that the model will see ACCT_001 instead. The fifth row is about who acts. Cyera's page describes blocking and agent quarantine; it does not state approvals. A run control governs an actor that moves through screens and can change records, and it puts a named person at each consequential step.

What happens to a sensitive value between the data store and the model?

Take one vendor bank account number stored in an ERP. In an estate-first program, the number is found, classified as a financial account identifier and associated with the store, the table and the identities that can read it. That knowledge drives decisions about access reviews, retention and remediation. The value itself stays the value in the source, and any identity with access to the screen sees it in clear. When an agent opens the vendor record under a user's permissions, the application renders the number like any other field.

In a RedactSure run, the Planner sets which values are tokenized for the task, and a named person confirms that policy before the run starts. When the agent opens the vendor record, the account number is replaced at the render layer with a consistent token, ACCT_001, before any model reads the screen. The environment hands the model the task's fields rather than a picture of the screen. Consistent means the same number appears as the same token throughout the run, so the agent can match ACCT_001 on the vendor master to ACCT_001 on a payment proposal without receiving the underlying digits. Amounts, dates and invoice numbers stay in clear where the policy says so, because the work runs on them. The real account number resolves only at an approved destination, at the moment a named person approves the action that needs it.

This is the principle of Least Exposure: for each piece of work, the AI receives exactly the data the task requires and nothing more, enforced before any model reads the screen. The research on tool firewalls supports the same direction. Bhagwatkar and colleagues report that minimizing the data sent to tools, together with sanitizing tool outputs, drove attack success to near zero on AgentDojo, Agent Security Bench, InjecAgent and tau-Bench, and they caution that public benchmarks are easily saturated and need stronger attacks (Bhagwatkar et al., 2026). Minimizing what crosses a boundary is a different control from detecting what sits behind it.

The consequence for prompt injection is concrete. An injection hidden in a vendor's remittance note that succeeds against a RedactSure agent collects tokens and remaining task context, and the payment it tries to start still stops for a named person. Cyera's page describes blocking risky operations in real time (Cyera, 2026) but does not state redaction, masking or tokenization, so it does not state what values a model receives when that screen is rendered.

How do approval and the record differ?

Estate platforms record the state of data: what was found, how it was classified, which exposures were opened and closed. That record answers an auditor who asks whether the organization knows where its regulated data sits and who can reach it. It is a record about stores and identities over time. Cyera's page also describes timestamped validation runs, an AI Activity Monitoring audit trail with file lineage, and AI Compliance evidence and reporting (Cyera, 2026).

RedactSure's record is scoped to the run. The AI Control Record holds every screen the agent saw as tokens, every action, and every approval with a name, and it exports to the customer's own SIEM. Approval is an event inside the run, attached to a person and an action. Under Supervised Delegation, a named person grants the access, chooses the applications, confirms what is tokenized, can watch and pause the run, and approves every consequential action, on the record. The agent never pays, settles or submits on its own. The human operator can watch the same tokenized stream, pause it and take over.

The reason for putting approval outside the model is that model-internal controls have gaps. Arthur Holland Michel describes vendor safety layers as a stack of imperfect filters and argues that a model's ability to refuse is treated as more dependable than it is (MIT Technology Review, 2026). Anthropic's research on agentic misalignment found models in simulated settings taking harmful actions when their goals were threatened (Anthropic, 2025). A named person at the consequential step is a control the model cannot argue its way past.

In the terms of the NIST AI Risk Management Framework, estate discovery and classification serve Map and Measure, while per-run tokenization, named approval and a run record serve Manage (NIST, 2023). Least privilege bounds reach; it does not bound what a model receives during a task the user is allowed to do. Permissions stay exactly as they are; what changes is what the AI can see.

Where does RedactSure sit?

For enterprise data exposure during agent work, RedactSure sits at the point between the application and the model. The agent works the organization's existing applications as rendered, inside a governed environment deployed into the cloud the customer's posture requires, with keys the customer holds. The Planner sets which values are tokenized and what the agent may do on each screen, and a named person confirms that policy before the run. Values chosen by policy become consistent tokens at the render layer before any model reads the screen, the model receives the task's fields with identifiers as tokens, and real values resolve only at approved destinations at the moment of an approved action. Every screen as tokens, every action and every named approval lands in the AI Control Record and exports to the customer's SIEM. The model can be Claude, GPT, Gemini or an open-weights model, swappable without moving the controls. Deployments are in pilot. Everyone else controls where data can go. RedactSure controls what the AI can see.

Methodology and limitations

This page rests on the competitor's own product documentation, accessed October 14, 2026, and on named external sources. For Cyera, the page reviewed describes Cyera Agent Guardian, an AI agent security product, and statements about Cyera are limited to what it states. External sources are the OWASP Top 10 for LLM Applications 2025 entries for LLM01 and LLM02 and the NIST AI Risk Management Framework (standards and official guidance); preprints by Greshake and colleagues and by Bhagwatkar and colleagues (peer-reviewed research and preprints); and Accenture's Technology Vision 2025, Microsoft's AI Red Team taxonomy, Anthropic's agentic misalignment research and an MIT Technology Review essay (industry analysis and press). RedactSure statements are limited to the design facts the company publishes about itself.

No product was tested. No Cyera deployment, scan or agent session was run, and no benchmark is reported. Cells that say the page reviewed does not state something are open questions, not statements that Cyera lacks a capability; the vendor may document masking, tokenization or approval controls elsewhere. Vendor features change, and the page carries its date and is revised when documentation is reviewed. Pricing, licensing and deployment requirements were out of scope. The vendor record and invoice examples describe record types, not any organization; no customer or prospect is described.

Whether either product, or the two together, meets an organization's obligations for a given workflow is a determination for its counsel, its assessor, and its security and compliance officers, who hold the data inventory, the policies and the risk tolerance that determination depends on.

What the record shows

Estate posture controls and run controls reduce exposure at different moments: one across the estate before work begins, the other inside each agent run as work happens. An organization that relies only on estate controls knows where its regulated data lives and still hands an agent's model whatever each screen renders. An organization that relies only on run controls writes token policies without a full map of what it holds. The test a reader can apply is one request to each product: for one screen of one workflow, show what the model received and who approved the step that changed the record.

Frequently asked questions

Is Cyera a competitor to RedactSure or a complement?

Cyera Agent Guardian, per its page, inventories AI assets and examines and enforces policy on prompts, tool calls and data access (Cyera, 2026). RedactSure works on data in use during an agent run. The two answer different exposure questions, and an organization can run both, with Cyera's classification informing which values a RedactSure policy tokenizes.

Does classifying sensitive data stop an AI agent from seeing it?

No. Classification identifies what a value is and where it sits. An agent working under a user's permissions sees what the application renders on screen, classified or not. Stopping the model from receiving a value requires a control at the moment of reading, such as replacing it with a consistent token like SSN_001 before the model reads the screen.

Does Cyera tokenize or mask data before an AI model reads it?

The page reviewed does not state it. It describes detecting and classifying sensitive data, alerting and blocking dangerous actions (Cyera, 2026). Buyers should ask Cyera directly whether and where it masks or tokenizes values, and whether that happens on screens an agent reads.

What does RedactSure need from an estate inventory?

It benefits from one. Knowing which fields hold card numbers, account numbers or patient identifiers gives the Planner the vocabulary for a token policy. RedactSure does not discover or classify data at rest, does not vault databases and does not change permissions. It decides what a model receives on each screen of a run.

Who approves an agent's actions in each design?

In RedactSure's design, a named person confirms the policy before the run and approves every payment, submission and record change while it runs, and the approval lands in the AI Control Record. Cyera's page describes real-time policy evaluation of tool invocations, blocking and agent quarantine (Cyera, 2026), and does not state approval workflows.

What does a prompt injection collect in each design?

Where a model reads screens in clear, a successful injection has access to whatever the agent read. In a RedactSure run the model holds tokens such as ACCT_001 in place of identifiers, so a successful injection collects tokens and remaining task context, and the consequential action still stops for a named person.

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About the author

Christopher Sowa
Chris SowaCo-Founder, President and Chief Commercial Officer, RedactSure

Chris Sowa is a founder of RedactSure and a former CEO of an AI company; he started it years before ChatGPT existed. He previously led AI at Accenture, served as Global VP of Strategy & Innovation at Schneider Electric, was CCO of Sovos, and spent more than a decade at Oracle, with earlier roles at SAP and IBM.

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