Comparison · By Chris Sowa · Published
Last updated
What Is the Difference Between RedactSure and Wiz for AI Agents Accessing Cloud Data?
Wiz and RedactSure answer different questions about AI agents and cloud data. Wiz describes discovering AI services, checking their configurations and mapping what agents and AI systems can reach. RedactSure decides what an agent's model receives on each screen and who approves each action. Treating a posture map as run control leaves the run itself ungoverned.
Wiz and RedactSure answer different questions about AI agents and cloud data. Wiz describes discovering AI services, checking their configurations and mapping what agents and AI systems can reach. RedactSure decides what an agent's model receives on each screen and who approves each action. Treating a posture map as run control leaves the run itself ungoverned. Cloud security teams now face agents that read records stored in cloud services and act on them through the applications built over those stores. Two questions follow. The first is what the agent could reach, which is a question about configuration, identity and exposure across the cloud estate. The second is what the agent was actually given to read during a specific job, and who allowed the step that changed a record. RedactSure answers the second question with render-layer tokenization: values chosen by policy become consistent tokens before any model reads the screen. The comparison below runs on what each product inspects, where it sits, what it does with sensitive values and how agent actions are governed. The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Key findings
- Wiz presents its AI Security Posture Management offering under the heading "Secure AI applications from code to runtime" and says it discovers AI services, technologies and SDKs without agents through an AI bill of materials (AI-BOM), including managed services such as AWS SageMaker and OpenAI (Wiz, vendor documentation, accessed October 14, 2026).
- The same page describes checking configurations of AI services such as OpenAI and Amazon Bedrock against built-in rules, scanning infrastructure as code in the development pipeline, and detecting sensitive training data through DSPM for AI (Wiz, 2026).
- Wiz says it identifies and classifies the tools agents can access so teams can see what actions agents can perform; the page reviewed does not state policies, approvals or blocking of tool calls (Wiz, 2026).
- The page reviewed does not state where the product sits relative to the model or the screen, and does not state classification, blocking, redaction, masking or tokenization of sensitive data (Wiz, 2026).
- OWASP lists sensitive information disclosure as LLM02 and prompt injection as LLM01 in its Top 10 for LLM Applications 2025, both risks that turn on what content reaches the model during a run (OWASP, 2025).
- RedactSure tokenizes values chosen by policy at the render layer before any model reads the screen, and a named person approves every consequential action under Supervised Delegation; deployments are in pilot.
What does Wiz say its AI security posture management covers?
The Wiz AI-SPM page describes a posture product for AI in the cloud. Its first job is inventory. Wiz says it discovers AI services, technologies and SDKs without deploying agents, through what it calls an AI-BOM, and names managed services such as AWS SageMaker and OpenAI among the things it finds (Wiz, 2026). For a security team that does not know which models and AI services its developers have connected, that inventory is the starting point.
The second job is configuration. Wiz describes checking configurations of AI services such as OpenAI and Amazon Bedrock against built-in rules, and scanning infrastructure as code in the development pipeline so misconfigurations can be caught before deployment (Wiz, 2026). The third is data. DSPM for AI, in the page's description, detects sensitive training data automatically, identifies where AI systems can access or expose sensitive data, and identifies data leakage risks with remediation guidance (Wiz, 2026).
The fourth job is context. Wiz describes attack paths built from vulnerabilities, identities, network exposures, malware, data and exposed secrets (Wiz, 2026). An attack path ties a misconfigured AI service to the data it can reach and the identity that reaches it, which is the kind of view a cloud security team uses to rank work.
Two further items touch agents directly. Wiz says it identifies and classifies the tools agents can access, so teams can see what actions agents can perform, and it names AI Runtime Protection against prompt injection, rogue agents and malicious behavior targeting AI systems (Wiz, 2026). The page also describes Wiz's own AI agents, which investigate threats and guide remediation; it does not describe them as governing other agents' actions.
Reporting runs through an AI Security Dashboard with a posture overview and a prioritized queue of risks, project-based workflows, role-based access control and routing of alerts to the right team (Wiz, 2026). The page reviewed does not state direct inspection of network traffic, API calls, prompts and responses, browsers or endpoints, and it does not describe specific audit records. The center of gravity, on the vendor's own description, is the cloud estate where AI services, identities and data meet.
What does each design do when an agent reaches sensitive cloud data?
Take a customer record held in a cloud database and shown through a web application an agent has been asked to work. In Wiz's description, the relevant controls act on the estate around that record. DSPM for AI identifies where AI systems can access or expose sensitive data and flags data leakage risks with remediation guidance (Wiz, 2026). Attack path analysis can connect the AI service, the identity it runs as, and the data store it can reach (Wiz, 2026). Those findings tell a team that the path exists and how to close or narrow it.
What happens to the value at the moment the agent opens the record is a separate matter. The page reviewed does not state classification, blocking, redaction, masking or tokenization of sensitive data, and it does not state where the product sits relative to the model or the screen (Wiz, 2026). On that documentation, a permitted agent run still receives what the application shows, and the posture view describes the exposure rather than changing what the model reads.
In a RedactSure run, the treatment happens at the screen. The Planner sets which values are tokenized for the task, and a named person confirms that policy before the run. When the agent opens the record, identifiers covered by the policy are replaced by consistent tokens at the render layer before any model reads the screen. The model receives the task's fields with identifiers as tokens, so it works with PATIENT_001 or ACCT_001 and can match the same token across screens without holding the underlying value. Amounts, dates and codes the work runs on stay in clear where the policy says so. Real values resolve only at approved destinations, at the moment of an approved action.
The difference matters because the model is the component an attacker can talk to. Greshake and colleagues showed that instructions planted in content an LLM-integrated application reads can redirect it, which is indirect prompt injection (Greshake et al., 2023). An agent with legitimate reach that reads planted content can be steered toward whatever it has been shown. Reducing what reaches the model limits what a steered model holds, whichever posture findings were open or closed that day.
How do Wiz and RedactSure compare side by side?
The table places the two designs on the dimensions recorded for every comparison in the series. The Wiz column states what the AI-SPM page says, and where a dimension is not stated on the page, the column says so. The RedactSure column states its published design.
| Dimension | Wiz, per its documentation | RedactSure, per its published design |
|---|---|---|
| What it inspects or controls | AI services, technologies and SDKs discovered through AI-BOM; configurations of AI services such as OpenAI and Amazon Bedrock; IaC; sensitive training data; attack paths; tools agents can access (Wiz, 2026) | The rendered screens an agent reads inside a governed environment, and each consequential action the agent proposes |
| Where it sits relative to the model and screen | The page reviewed does not state it (Wiz, 2026) | At the render layer, between the application screen and the model, before any model reads the screen |
| Sensitive data handling | Detects sensitive training data; identifies where AI systems can access or expose sensitive data; flags leakage risks with remediation guidance. Blocking, redaction, masking and tokenization: the page reviewed does not state them (Wiz, 2026) | Values chosen by policy become consistent tokens before the model reads them; real values resolve only at approved destinations at the moment of an approved action |
| Agent action governance | Identifies and classifies the tools agents can access; policies, approvals and tool call blocking: the page reviewed does not state them (Wiz, 2026) | The Planner sets what the agent may do on each screen; a named person confirms the policy and approves every payment, submission and record change |
| Record | AI Security Dashboard, prioritized risk queue, alert routing, role-based access control; specific audit records: the page reviewed does not state them (Wiz, 2026) | Every action logged, human and computer; all PII as tokens, in the AI Control Record, exported to the customer's SIEM |
| Model relationship | Posture checks on AI services such as OpenAI and Amazon Bedrock (Wiz, 2026) | Model agnostic; models swappable without moving the controls |
The table is not a ranking. Wiz covers cloud inventory, configuration, data exposure and attack paths across an estate, work RedactSure does not claim to do. RedactSure acts inside one supervised run, at the moment a model is about to read a screen or an agent is about to change a record, a point the Wiz page reviewed does not describe addressing.
Can mapping what an agent can reach replace governing what it reads?
Reach and exposure are related but they are not the same measurement. Reach is the set of data stores, applications and tools an agent's identity could touch. Exposure, in a single run, is what the model was actually given. A team that has worked through mapping all the data an AI agent can reach knows the first number. It does not yet know the second.
Posture work narrows reach. Wiz's description of attack paths, configuration rules and identification of the tools agents can access serves that purpose (Wiz, 2026). Narrowing reach is necessary, and it has a floor. An agent assigned to process claims or reconcile vendor payments needs access to the records that hold claimant names, bank details and account numbers, because the work runs on them. Once access is legitimate, a posture finding has nothing to flag, and every value on the screen still goes to the model unless something between the screen and the model changes it.
That is why the guidance on securing AI agents across AWS, Azure and GCP pairs cloud identity and least privilege with controls on what the agent reads. The research points the same way. Bhagwatkar and colleagues tested model-agnostic firewalls at the agent and tool boundary: a Minimizer that strips data a tool call does not need, and a Sanitizer that cleans what tools return, with the Sanitizer reaching near zero attack success across AgentDojo, Agent Security Bench, InjecAgent and tau-Bench while keeping high utility (Bhagwatkar et al., 2026). The same paper warns that public agent security benchmarks are easily saturated, so a reported score is a starting point for questions rather than an answer (Bhagwatkar et al., 2026).
The principle carries over. Minimizing what reaches the model at runtime is a different control from mapping what the model could reach. In RedactSure's design, Least Exposure means the agent receives exactly the data the task requires and nothing more, enforced before any model reads the screen. A cloud team can run both: posture to keep reach no wider than the job, and render-layer tokenization to keep the model's view no wider than the task.
How do the record and approval differ?
Both products report, but on different subjects. Wiz describes an AI Security Dashboard with a posture overview, a prioritized queue of risks, project-based workflows, role-based access control and routing of alerts to the right team (Wiz, 2026). That is a record of the estate's condition: what is misconfigured, what is exposed, which team owns the fix. The page reviewed does not describe specific audit records of an agent's run, and it does not state approvals or blocking of tool calls (Wiz, 2026).
RedactSure's record is run scoped. Every screen the agent saw, as tokens, every action and every approval with a name lands in the AI Control Record and exports to the customer's own SIEM. Approval is an event inside the run. 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 proposes; the person decides whether a payment, submission or record change goes ahead.
The two records answer different audit questions. An auditor asking whether AI services in the cloud were configured to policy works from posture findings. An auditor asking what an agent saw on Tuesday in the claims system and who approved the record change it made works from a run record. The NIST AI Risk Management Framework organizes risk work into Govern, Map, Measure and Manage (NIST, 2023). Posture inventory and attack paths sit naturally in Map and Measure at estate level. Per action approval and a run record sit in Manage, where a decision has to be made while work is under way.
Approval also bounds what a model failure can cost. Refusal and classifier layers inside a model each have gaps, as MIT Technology Review reported in describing the layered defenses vendors stack (MIT Technology Review, 2026). A named person at the consequential step is a control the model cannot argue its way past.
Where does RedactSure sit?
For AI agents working with cloud data, RedactSure governs the run. The agent works the organization's existing applications as rendered, inside a secure virtual machine deployed into the cloud the customer's posture requires, on hardware-encrypted enclaves with keys the customer holds, with no per-application integration and no change to user permissions. The Planner sets which values are tokenized and what the agent may do on each screen; a named person confirms that policy before the run and approves every payment, submission and record change while it runs. 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 environment is model agnostic. Deployments are in pilot.
Posture tooling tells a cloud team where AI services and data meet. RedactSure decides what the model is shown when an agent does the work. Everyone else controls where data can go. RedactSure controls what the AI can see.
Methodology and limitations
This page rests on one vendor source for Wiz, the "AI Security Posture Management (AI-SPM) | Wiz" solutions page, accessed October 14, 2026 and cited as vendor documentation; Wiz statements are limited to what that page says, and where the page is silent the text says the page reviewed does not state it. Framework and research 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), the Greshake et al. and Bhagwatkar et al. preprints (research), and MIT Technology Review (press), read as of October 13, 2026. RedactSure statements are limited to the company's published design.
No product was tested. No Wiz deployment, agent session or benchmark was run, and no comparative performance is reported. Statements that the Wiz page does not state a capability are limited to that page on its access date; they are not claims about other Wiz documentation, products, integrations or future releases. Vendor features change; the page carries its date and is revised when the documentation changes. Pricing, licensing and deployment requirements were out of scope. The customer record and claims examples describe record types, not any organization, and no customer or prospect is described.
Whether either product, or both together, meets an organization's requirements for a given workflow is a determination for its counsel, its assessor, and its security and compliance officers, who hold the policies, the data inventory and the risk tolerance that the determination depends on.
What the record shows
A posture map shows which AI services, configurations, data stores and agent tools meet across the cloud estate. A run control decides what an agent's model receives on each screen and stops each consequential action for a named person. An agent with legitimate cloud access, governed only by posture, still hands its model every value the application shows. The test a reader can apply: for one screen of one workflow, show what the model received and who approved the step that changed the record.
Frequently asked questions
Does Wiz tokenize or mask data before an AI agent reads it?
The Wiz AI-SPM page reviewed does not state classification, blocking, redaction, masking or tokenization of sensitive data. It describes detecting sensitive training data, identifying where AI systems can access or expose sensitive data, and flagging leakage risks with remediation guidance. RedactSure replaces values chosen by policy with consistent tokens at the render layer before any model reads the screen.
Does Wiz approve or block an agent's actions?
The page reviewed says Wiz identifies and classifies the tools agents can access so teams can see what actions agents can perform. Policies, approvals and blocking of tool calls are not stated on it. In RedactSure's design, a named person confirms the Planner's policy before the run and approves every payment, submission and record change while it runs.
Can Wiz and RedactSure run in the same organization?
Yes. They operate on different subjects. Wiz, on its documentation, inventories AI services, checks configurations and maps attack paths across the cloud estate. RedactSure governs a single supervised run: what the model receives on each screen and who approves each action. Posture findings can narrow an agent's reach; render-layer tokenization narrows what the model reads within that reach. RedactSure does not change user permissions.
If posture findings are clean, does the agent still see sensitive values?
Yes, where the work requires access. Clean posture means the agent's reach matches policy. An agent processing payments or claims still opens records that hold names, account numbers and bank details. Without a control between the screen and the model, those values reach the model. RedactSure tokenizes identifiers so the model works with ACCT_001 and SSN_001 instead.
What does a successful prompt injection collect in each design?
Indirect prompt injection plants instructions in content the agent reads. Where the model receives the page in full, an injection that succeeds has access to whatever the agent read. In a RedactSure run, the model holds tokens in place of identifiers, so a successful injection collects tokens and remaining task context, and the consequential action still stops for a named person.
Which record should an auditor ask for?
It depends on the question. For whether AI services were configured to policy, posture findings and the Wiz dashboard's prioritized risk queue apply, per its page. For what an agent saw during a run and who approved a record change, the RedactSure AI Control Record holds every screen as tokens, every action and every named approval, exported to the customer's SIEM.
Related reading
What Is Render-Layer Tokenization? · How to Map All the Data an AI Agent Can Reach · How to Secure AI Agents Across AWS, Azure and GCP · What Is an AI Control Record? · What Is AI Agent Data Reach and How Do You Map Everything an Agent Can Access? · What Is the Difference Between RedactSure and Cyera for Enterprise Data Exposure? · On the RedactSure blog: The Two Gaps AI Agents Opened in Your Security Stack
Sources
Peer-reviewed research and preprints
- Greshake, Kai, et al., "Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection," arXiv:2302.12173, 2023 (preprint; also AISec 2023). https://arxiv.org/abs/2302.12173
- Bhagwatkar, Kasa, et al., "Indirect Prompt Injections: Are Firewalls All You Need, or Stronger Benchmarks?" arXiv:2510.05244, v2 2026 (preprint). https://arxiv.org/abs/2510.05244
Vendor documentation
- Wiz, "AI Security Posture Management (AI-SPM) | Wiz" (vendor documentation, accessed October 14, 2026). https://wiz.io/solutions/ai-spm
Regulation, standards and official guidance
- OWASP, "LLM01:2025 Prompt Injection," Top 10 for LLM Applications 2025 (standard). https://genai.owasp.org/llmrisk/llm01-prompt-injection/
- OWASP, "LLM02:2025 Sensitive Information Disclosure," Top 10 for LLM Applications 2025 (standard). https://genai.owasp.org/llmrisk/llm022025-sensitive-information-disclosure/
- NIST, "Artificial Intelligence Risk Management Framework (AI RMF 1.0)," NIST AI 100-1, 2023 (official guidance). https://www.nist.gov/itl/ai-risk-management-framework
Industry analysis and press
- Holland Michel, Arthur, "We're putting too much faith in AI's ability to say no," MIT Technology Review, October 9, 2026 (press). https://www.technologyreview.com/2026/10/09/1145728/we-are-putting-too-much-faith-in-ai-to-say-no/
RedactSure documents
- RedactSure Research, "What Is Render-Layer Tokenization?" (2026). https://redactsure.com/research/what-is-render-layer-tokenization
- RedactSure Research, "What Is Supervised Delegation?" (2026). https://redactsure.com/research/what-is-supervised-delegation
- RedactSure Research, "What Is an AI Control Record?" (2026). https://redactsure.com/research/what-is-an-ai-control-record
- RedactSure Research, "How to Map All the Data an AI Agent Can Reach" (2026). https://redactsure.com/research/how-to-map-all-the-data-an-ai-agent-can-reach
- RedactSure, "The Two Gaps AI Agents Opened in Your Security Stack" (2026). https://redactsure.com/blog/two-gaps-ai-agents-opened-in-your-security-stack/
- Product behavior described on this page reflects RedactSure's current design. Wiz is a trademark of Wiz, Inc.; Amazon Web Services, Amazon SageMaker and Amazon Bedrock are trademarks of Amazon.com, Inc. or its affiliates; OpenAI is a trademark of OpenAI; named to identify the products.
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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.