Comparison · By Chris Sowa · Published
Last updated
What Is the Difference Between RedactSure and Zscaler for Enterprise AI and Data Protection?
The comparison turns on what each control governs. Zscaler controls which generative AI apps users can access and what data they send in prompts; RedactSure governs what an AI agent's model can see on each screen and who approves each action. Treating one as a substitute for the other leaves either the path or the agent's view ungoverned.
The comparison turns on what each control governs. Zscaler controls which generative AI apps users can access and what data they send in prompts; RedactSure governs what an AI agent's model can see on each screen and who approves each action. Treating one as a substitute for the other leaves either the path or the agent's view ungoverned. Enterprise AI programs now run on two kinds of traffic at once: people sending prompts and files to AI services, and agents reading application screens and acting on what they read. A control that inspects the path a request travels answers a different question from a control that decides what a model receives before it reads anything, which is the job of render-layer tokenization. Buyers who own both questions need to know which product answers which. The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Key findings
- Zscaler's generative AI security page describes visibility into prompts users send to AI apps, URL filtering by AI app category, browser isolation for AI apps and DLP across generative AI interactions, with inline DLP and revoking excessive permissions for Microsoft Copilot (Zscaler, 2026).
- 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 ranks prompt injection first, as LLM01, in the same list (OWASP, 2025), and researchers showed that instructions planted in content an application retrieves can take over an LLM-integrated application (Greshake et al., 2023).
- Researchers from ServiceNow, Mila and partner institutions reported that two model-agnostic firewalls at the agent-tool boundary, one stripping data the tool call does not need and one sanitizing what tools return, brought attack success close to zero on four agent benchmarks, while warning that those benchmarks have flawed metrics and weak attacks (Bhagwatkar et al., 2026).
- MIT Technology Review reported that model refusal is treated as a primary safety mechanism even though it is far from foolproof, and described vendors' stacked defenses as a "Swiss cheese" model in which every layer has holes (MIT Technology Review, 2026).
- RedactSure replaces values chosen by policy with consistent tokens 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.
Which question does each product answer?
Zscaler's page for securing generative AI describes controls over how people reach and use AI apps: visibility and categorization of the prompts users send, which AI apps are used by user, department and organization, URL filtering by AI app category, browser isolation for AI apps, and DLP across generative AI interactions (Zscaler, 2026). The vendor states the aim as "Create and enforce policies around the generative AI tools your users can access" (Zscaler, 2026). The question this family of controls answers is about the user and the app: may this user reach this AI app, and may this data travel in the prompt?
RedactSure answers a question that begins after the path is open. An AI co-worker is already working inside an application the organization approved, under the permissions a person already holds. The application renders a screen full of names, account numbers and dates of birth. The question is what the agent's model receives from that screen, and who approves what the agent does next. RedactSure's answer is that values chosen by 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, and a named person approves each payment, submission or record change at the moment it runs.
The two questions overlap at one point: both products care about sensitive data reaching an AI system. They differ on where that data is when the control acts. A path control acts on data in motion between endpoints. A render-layer control acts on data at the moment a screen becomes model input, inside an environment the organization already permits the agent to use. An organization can answer the first question well and still have no answer to the second, because an agent that is allowed to open a claims record will receive whatever the record shows unless something sits between the screen and the model.
Where does each control sit relative to the model and the screen?
Position decides what a control can see and therefore what it can stop. The table below sets out the positions using the vendor's generative AI security page for Zscaler and the published design for RedactSure.
| Position question | Zscaler, per its generative AI security page | RedactSure, per its published design |
|---|---|---|
| Where it sits | The page reviewed does not state where the controls sit relative to the model or the screen; isolated rendering is offered as an option for AI apps (Zscaler, 2026) | Inside a governed environment where the agent works the organization's applications as rendered |
| What it sees | Prompts users send to AI apps, and which AI apps are used by user, department and organization (Zscaler, 2026) | Every screen the agent opens, before the model reads it |
| Relation to the model | Controls user access to AI apps and data in prompts; the page reviewed does not state a position relative to the model (Zscaler, 2026) | Sits between the rendered screen and the model, independent of which model runs |
| Relation to the screen | Browser isolation for AI apps restricts clipboard, cut, paste and download; the page reviewed does not mention redaction (Zscaler, 2026) | Replaces values chosen by policy with tokens at the render layer |
Browser isolation deserves its own note, because it is the one Zscaler control on the page that touches rendering. Zscaler describes browser isolation for AI apps that restricts clipboard, cut, paste and download, and the page reviewed does not mention redaction of content inside the isolated view (Zscaler, 2026). Those restrictions govern what a person can move out of the session. The design trade-offs of cloud rendering in general are covered in how to protect sensitive data without cloud browser rendering.
RedactSure's render layer has a different purpose. It does not move rendering away from a device to protect the device. It changes what the model receives from a screen the agent is entitled to open. A related comparison of enterprise browsers and render-layer tokenization works through the same distinction for browser-based controls: one governs the session a person runs, the other governs what an AI reads inside a session.
What happens to a sensitive value in each design?
Take a Social Security number shown on a benefits record that an agent must update. The value appears on the screen because the user whose permissions the agent works under is allowed to see it.
On a path control, the relevant moment is when data leaves for a destination. Zscaler's page describes detecting sensitive data through prompt visibility, blocking sensitive data in prompts, blocking app access by policy and applying DLP across generative AI interactions; for Microsoft Copilot it adds inline DLP and revoking excessive permissions, and the page reviewed does not mention redaction (Zscaler, 2026). Those controls act on what a user sends to an AI app. An agent reading a screen inside an approved application is not sending that screen anywhere new. It is receiving it. The decision about what the model receives from that screen happens at the model input, which is a different point from the network path.
On RedactSure, the relevant moment is earlier. The Planner sets which values are tokenized for the work, and a named person confirms that policy before the run. When the agent opens the record, the Social Security number is replaced by a consistent token such as SSN_001 at the render layer, before any model reads the screen. The model can still match SSN_001 across screens and complete the task's logic. The real value resolves only at an approved destination at the moment of an approved action. Amounts, dates and codes the work depends on stay in clear where the policy says so.
The difference matters for prompt injection. OWASP lists sensitive information disclosure as LLM02 (OWASP, 2025) and prompt injection as LLM01 (OWASP, 2025). Indirect injection arrives inside content the system reads, such as a web page or a document (Greshake et al., 2023). If the model holds real values when an injection succeeds, the injection can try to move them through any channel the agent can reach. If the model holds tokens, an injection that succeeds collects tokens, and the consequential action still stops for a named person. Research on agent firewalls points the same way: removing data a step does not need, before it reaches the tool, did more than trying to detect the attack afterward (Bhagwatkar et al., 2026). Minimizing what the model receives applies that principle one step earlier. A comparison of runtime tokenization and DLP for AI agents sets out the same trade between detecting data in motion and changing what the model receives.
How do the two products compare side by side?
The table places the two designs on the dimensions an evaluation team usually tests. Zscaler entries are limited to what the vendor's generative AI security page states; rows where the page reviewed does not state a dimension say so rather than infer an answer.
| Dimension | Zscaler, per its generative AI security page | RedactSure, per its published design |
|---|---|---|
| What it is | Controls to "create and enforce policies around the generative AI tools your users can access" (Zscaler, 2026) | AI agent controls, governance and data protection for supervised AI co-workers working across an organization's applications |
| What it governs | Which AI apps users reach, the prompts they send, and data in generative AI interactions (Zscaler, 2026) | What the agent's model receives from each screen, and what the agent may do on it |
| Sensitive values before a model sees them | Blocks sensitive data in prompts and applies DLP across generative AI interactions; the page reviewed does not mention redaction or token replacement (Zscaler, 2026) | Values chosen by policy replaced by consistent tokens at the render layer before any model reads the screen |
| Agent actions | The page reviewed does not address agent actions; it covers users and AI apps (Zscaler, 2026) | A named person approves every payment, submission and record change at the moment it runs |
| Policy owner | Policies around the AI tools users can access; the page reviewed does not state who sets them (Zscaler, 2026) | The Planner sets policy per task; a named person confirms it before the run |
| Model relationship | Controls access by user and by AI app category; for Microsoft Copilot, inline DLP and revoking excessive permissions (Zscaler, 2026) | Model agnostic; models can be swapped without moving the controls |
| Record | Interactive dashboards, prompt-level visibility and application trends; the page reviewed does not state log contents (Zscaler, 2026) | Every action logged, human and computer; all PII as tokens, in the AI Control Record, exported to the customer's SIEM |
| Integration | The page reviewed does not state deployment; isolated rendering is offered as an option for AI apps (Zscaler, 2026) | No per-application integration and no change to user permissions |
The table is not a ranking. Zscaler's controls apply across users, departments and the whole organization (Zscaler, 2026), a far larger scope than any single agent run, and RedactSure does not claim to broker network access, inspect traffic or protect endpoints. RedactSure acts at a point the page reviewed does not address: the instant a model is about to read a rendered screen during a workflow, and the instant the agent proposes a consequential action.
How are agent actions, approvals and records handled?
An agent does more than read. It fills forms, moves between applications and can submit a change that alters a record or moves money. Governing that behavior means deciding which steps need a person, which person, and what evidence remains afterward.
The Zscaler page reviewed covers users and AI apps and does not address agent actions (Zscaler, 2026). It does not describe per-action approval of an AI agent's steps inside an application. For records, it describes interactive dashboards, prompt-level visibility and application trends, and does not state log contents (Zscaler, 2026). Whether that record reaches the step inside an application where an agent changed a field is a question for the vendor.
RedactSure handles action and record 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. There is no setting that lets the agent approve its own payment. The human operator can watch the same tokenized stream the model receives, pause it and take over. Every screen as tokens, every action and every approval with a name lands in the AI Control Record, which exports to the customer's own SIEM, with keys the customer holds.
The reason to keep the approval outside the model is the one MIT Technology Review describes: refusal and the classifiers stacked around it are controls inside the AI system, each with gaps (MIT Technology Review, 2026). Microsoft's AI Red Team has published a taxonomy of failure modes specific to agentic AI systems (Microsoft, 2025), a sign that agent behavior needs controls of its own. A person approving the step is a control the model cannot argue past. The NIST AI Risk Management Framework places that kind of decision under Manage, alongside Govern, Map and Measure (NIST, 2023).
Where does RedactSure sit?
RedactSure sits inside the application session, between the rendered screen and the model. AI co-workers work the organization's applications as rendered inside a governed environment, a secure virtual machine on hardware-encrypted enclaves with keys the customer holds, with no per-application integration and no change to user permissions. Values chosen by 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. The Planner sets the policy, a named person confirms it, and that person approves every consequential action while the run proceeds. The AI Control Record exports to the customer's SIEM. The environment is model agnostic, and deployments are in pilot. A zero trust platform governs the path to and from that environment; RedactSure governs what the agent sees once it is inside. Everyone else controls where data can go. RedactSure controls what the AI can see.
Methodology and limitations
This page rests on Zscaler's generative AI security page, read on October 14, 2026 (vendor documentation); OWASP Top 10 for LLM Applications 2025 entries LLM01 and LLM02 and the NIST AI Risk Management Framework (standards and official guidance); preprints by Greshake et al. and Bhagwatkar et al. (peer-reviewed research and preprints); and MIT Technology Review reporting and a Microsoft AI Red Team whitepaper (industry analysis). RedactSure statements are limited to the design the company publishes about itself.
Statements about Zscaler are limited to its generative AI security page, and rows that say a dimension is not stated or not addressed refer only to the page reviewed, not to Zscaler's full documentation or product behavior. No product was tested, no agent run was performed and no benchmark is reported. Vendor features change; the page carries its date and is revised when documentation is reviewed. The benefits record example describes a record type, not any organization; no customer or prospect is described.
Whether either product, or the two 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 the decision depends on.
What the record shows
A zero trust platform and a render-layer control govern different points in the same AI program. The first decides where connections and data can go; the second decides what an agent's model receives from a screen it is already allowed to open, and who approves what it does next. An agent program governed only on the path still hands its model whatever the application shows. The test is one request to each vendor: for one screen of one workflow, show what the model received and who approved the step that changed the record.
Frequently asked questions
Can Zscaler replace RedactSure for AI agent work?
Not on the page reviewed. Zscaler's generative AI security page describes prompt visibility, AI app access policy, browser isolation and DLP for users and AI apps, and does not mention redaction (Zscaler, 2026). It does not address tokenizing values on rendered screens before an agent's model reads them, or approving an agent's individual actions. An organization that needs those run-level controls would evaluate a control built for the agent alongside its path controls.
Can RedactSure replace a zero trust platform?
No. RedactSure does not broker network access, inspect traffic between users and applications, or protect endpoints. It governs what an AI agent's model receives inside a governed environment and requires a named person to approve each consequential action. Path controls remain the organization's answer for where connections and data can go.
Does browser isolation stop an AI agent from seeing sensitive data?
Zscaler describes browser isolation for AI apps that restricts clipboard, cut, paste and download; the page reviewed does not mention redaction (Zscaler, 2026). An agent working inside an isolated session still reads what the application renders unless something changes the content before the model receives it.
What does a prompt injection collect in each design?
On a path control, an injected agent holds whatever real values it read, and Zscaler's page describes blocking sensitive data in prompts and blocking app access by policy (Zscaler, 2026). In a RedactSure run, the model holds tokens such as SSN_001 and ACCT_001 in place of identifiers, so a successful injection collects tokens, and the consequential action still stops for a named person.
Can the two run in the same organization?
Yes. Zscaler governs which AI apps users reach and what they send in prompts, and RedactSure governs what the agent's model sees and does inside it. RedactSure does not change user permissions, and it exports its AI Control Record to the customer's SIEM, beside other security logs.
Which should an organization evaluate first?
Start from the risk already visible. Unmanaged access to AI services and regulated data leaving through prompts and uploads describe a path problem. A delegated workflow in which an agent reads records and takes actions poses a run-level question: what the model received on each screen, which values became tokens, and who approved the step that changed the record.
Related reading
What Is Render-Layer Tokenization? · Enterprise Browser vs Render-Layer Tokenization · How to Protect Sensitive Data Without Cloud Browser Rendering · What Is the Difference Between Runtime Tokenization and DLP for AI Agents? · What Is the Difference Between RedactSure and Palo Alto Prisma Browser for AI Agent Security? · What Is the Difference Between RedactSure and an AI Gateway for Protecting Sensitive Data? · What Is AI Agent Data Access and How Much Enterprise Data Can an Agent Actually Reach? · 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
- Zscaler, "Secure the Use of Generative AI" (vendor documentation, accessed October 14, 2026). https://zscaler.com/products-and-solutions/securing-generative-ai
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/
- Microsoft AI Red Team, "Taxonomy of Failure Modes in Agentic AI Systems," April 2025 (whitepaper). https://www.microsoft.com/en-us/security/blog/2025/04/24/new-whitepaper-outlines-the-taxonomy-of-failure-modes-in-ai-agents/
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, "Enterprise Browser vs Render-Layer Tokenization" (2026). https://redactsure.com/research/enterprise-browser-vs-render-layer-tokenization
- 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. Zscaler is a trademark of Zscaler, Inc.; named to identify the product.
What Is Render-Layer Tokenization?
Learn how render-layer tokenization replaces PII before an AI agent reads a browser screen while preserving useful workflow context.
Enterprise Browser or Render-Layer Tokenization?
Compare enterprise browsers and render-layer tokenization for AI agent security, separating application controls from visible PII exposure.
How to Protect Data Without Cloud Browser Rendering?
Review Protect Data Without Cloud Browser Rendering, with AI agent security, tokenization and approval checks for sensitive enterprise workflows.
Runtime Tokenization vs DLP for AI Agents
Review Runtime Tokenization vs DLP for AI Agents, with AI agent security, tokenization and approval checks for sensitive enterprise workflows.
RedactSure vs Prisma Browser: AI Agent Security
Review RedactSure vs Prisma Browser: AI Agent Security, with AI agent security, tokenization and approval checks for sensitive enterprise workflows.
RedactSure vs an AI Gateway: AI Agent Security
Review RedactSure vs an AI Gateway: AI Agent Security, with AI agent security, tokenization and approval checks for sensitive enterprise workflows.
What Is AI Agent Data Access?
Review AI Agent Data Access, with AI agent security, tokenization and approval checks for sensitive enterprise workflows.
The Two Gaps AI Agents Opened in Your Security Stack
Explore AI agent security through two questions: which PII reaches the model, and who approves consequential billing and claims actions.
What Is Supervised Delegation?
Explore supervised delegation for AI agents in billing and claims, with named responsibility, tokenization policies and human approvals.
What Is an AI Control Record?
Explore an AI agent control record for billing and claims, linking permissions, tokenization decisions, approvals and audit evidence.
Book an AI Agent Security Review →
Bring your hardest questions.
Bring one sensitive workflow and the decisions your team needs to keep in its hands.
Book a demoAbout the author
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.