Comparison · By Chris Sowa · Published
Last updated
What Is the Difference Between RedactSure and SentinelOne Prompt Security for Enterprise AI Usage and Agent Workflows?
Prompt Security, part of SentinelOne, enforces policy on data that employees, developers and agents send to and receive from AI services; RedactSure governs what an AI agent's model is shown while it works a task, and who approves what it does. Treating one as the other leaves either the AI exchange or the rendered screen and the consequential action without a control.
Prompt Security, part of SentinelOne, enforces policy on data that employees, developers and agents send to and receive from AI services; RedactSure governs what an AI agent's model is shown while it works a task, and who approves what it does. Treating one as the other leaves either the AI exchange or the rendered screen and the consequential action without a control. The question matters because enterprise AI usage now runs on two tracks at once. Employees paste text into chat assistants, and agents open business applications and act on what they read. A control built for the first track inspects a prompt a person typed. A control built for the second has to decide what a model reads on a screen it opened itself, which is the job of render-layer tokenization, and has to stop a consequential action until a named person approves it. The comparison below runs on where each control sits, what it does with a sensitive value, and how each handles an acting agent. The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Key findings
- Prompt Security, part of the SentinelOne AI Security Platform, covers employee and developer use of AI tools, including code assistants, unsanctioned shadow AI, homegrown AI apps and agents with MCP servers, and redacts sensitive data and blocks data leakage in real time; the page reviewed does not state approval workflows or tool-call blocking for agents (SentinelOne, 2026).
- SentinelOne presents Prompt Security as part of its AI Security Platform (SentinelOne, 2026).
- OWASP lists sensitive information disclosure as LLM02 in its Top 10 for LLM Applications 2025, a risk that turns on what data reaches a model in the first place (OWASP, 2025).
- OWASP lists prompt injection as LLM01, and research on indirect prompt injection shows that instructions planted in content an application retrieves, rather than in what a user types, can redirect a model (OWASP, 2025; Greshake et al., 2023).
- A 2026 study of agent defenses found that a model-agnostic firewall at the agent and tool boundary, which strips data the tool call does not need and cleans what comes back, reached near-zero attack success on four public benchmarks, while warning that those benchmarks are weak and easily saturated (Bhagwatkar et al., 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, with deployments in pilot.
What does Prompt Security say it does?
Prompt Security describes itself in one line: "Prompt Security protects your entire AI ecosystem without slowing innovation" (SentinelOne, 2026). The product page, under the SentinelOne AI Security Platform, lists employee and developer use of AI tools, including code assistants, unsanctioned shadow AI, homegrown AI apps and agents with MCP servers, and says the product enforces policy on data sent to and returned from AI services. It redacts sensitive data, scrubs sensitive outputs and blocks data leakage in real time, with dashboards of prompt counts, violations prevented and shadow AI applications.
That positioning locates the control on the exchange with an AI service. The page says policies are enforced at the point of interaction, not after the fact. The page reviewed does not state where the product sits relative to the model or the screen, whether it inspects network traffic, browsers, endpoints, data stores or identities, or which detection, classification or tokenization methods sit behind its redaction (SentinelOne, 2026).
This addresses a real and visible problem. Security teams that ask what controls stop employees from feeding sensitive data to public AI are asking the question a control on the AI exchange answers. A ban tends to push usage out of view, and discovery of unsanctioned tools is the first step most programs take when they handle shadow AI usage. An inspection layer on the prompt path lets the organization allow the tools while removing identifiers from what is sent.
For agents, the page describes least-privilege access within a defined scope, discovery of shadow MCP servers and an audit log of agent actions (SentinelOne, 2026). The page reviewed does not state whether a person approves agent actions one by one or whether the product blocks tool calls; buyers should ask SentinelOne.
Where does each control sit relative to the model and the screen?
The two designs intervene at different points in the path from data to model, and that placement decides what each can see and change.
A control on the AI exchange sits between a person, or an application, and the model. It sees what is sent to the AI service and what comes back. If an employee pastes a customer record into a chat window, the control can find the account number in the pasted text and redact it before the request leaves. The control acts on content a human chose to send.
An agent that works through applications changes the source of the content. Nobody pastes the record; the agent opens the record and reads the screen. The data the model receives is whatever the application rendered on that screen, plus whatever the agent's tools retrieved. Research on indirect prompt injection makes the same point from the attack side: the dangerous content arrives through what the system retrieves, not through what the user types (Greshake et al., 2023). A control that inspects only the typed prompt does not see that content unless the agent's traffic also passes through it.
RedactSure sits at the render layer of the screens the agent reads, inside a governed environment. The environment is a secure virtual machine in the cloud the customer's posture requires, on hardware-encrypted enclaves, with keys the customer holds. Every value chosen by policy is replaced by a consistent token before any model reads the screen, and the environment hands the model the task's fields rather than a picture of the screen. The agent works the organization's existing applications as rendered, with no per-application integration and no change to user permissions.
| Point in the path | What sits there | What it sees |
|---|---|---|
| AI tool use | Prompt Security (SentinelOne, 2026) | Data sent to and returned from AI tools, code assistants and homegrown AI apps; the page reviewed does not state where the control sits |
| Agents and MCP servers | Prompt Security (SentinelOne, 2026) | Agent scope under least-privilege access, shadow MCP servers and unsanctioned agent deployments |
| Rendered screen, before the model | RedactSure render layer | Every screen the agent opens, tokenized by policy before the model reads it |
| Consequential action | RedactSure Supervised Delegation | The proposed payment, submission or record change, held for a named person |
How do the two products compare side by side?
The table places the two designs on the dimensions a buyer would test. The competitor column draws on the vendor's product page; where a cell says the page reviewed does not state a point, that is a statement about the page, not a statement that the product lacks a feature.
| Dimension | Prompt Security (SentinelOne), per its product page | RedactSure, per its published design |
|---|---|---|
| What it is | AI security for employee and developer AI use, homegrown AI apps and agents; "protects your entire AI ecosystem without slowing innovation" (SentinelOne, 2026) | AI agent controls, governance and data protection for supervised AI co-workers working inside a governed environment |
| What it inspects or controls | Data sent to and returned from AI services; the page reviewed does not state coverage of network traffic, browsers, endpoints, data stores or identities (SentinelOne, 2026) | What the model receives from each rendered screen, and every consequential action the agent proposes |
| Where it sits | The page reviewed does not state it; policies are enforced at the point of interaction, not after the fact (SentinelOne, 2026) | At the render layer, before any model reads the screen, inside a secure virtual machine with customer-held keys |
| Sensitive data | Redacts sensitive data, scrubs sensitive outputs and blocks data leakage in real time; the page reviewed does not state detection, classification or tokenization methods (SentinelOne, 2026) | Values chosen by policy replaced with consistent tokens such as SSN_001 and ACCT_001; real values resolve only at approved destinations at the moment of an approved action |
| Shadow AI | Discovers unsanctioned shadow AI, shadow MCP servers and unsanctioned agent deployments (SentinelOne, 2026) | Not a discovery product; agents are tethered to the applications a named person connected |
| Agent actions and approval | Least-privilege access keeps agents within their defined scope; the page reviewed does not state approval workflows or tool-call blocking (SentinelOne, 2026) | The Planner sets policy and a named person confirms it; a named person approves every payment, submission and record change |
| Record | Searchable audit log of agent actions, decisions and enterprise system interactions; dashboards of prompt counts, violations prevented and shadow AI applications (SentinelOne, 2026) | Every action logged, human and computer; all PII as tokens, in the AI Control Record exported to the customer's SIEM |
| Model | The page reviewed does not state model coverage (SentinelOne, 2026) | Model agnostic; models swap without moving the controls |
The table is not a ranking. The two products answer different questions, and an organization with heavy employee use of chat assistants and a separate agent program can have a use for both. The useful reading is row by row: which risk in the organization's own inventory each row addresses, and which rows have no control today.
What happens to a sensitive value in each design?
Take a customer account number in a billing record. In the employee track, a person copies the record into a chat assistant to draft a reply. A control on the AI exchange, such as Prompt Security's redaction of sensitive data, removes the account number before the request reaches the model (SentinelOne, 2026). The model drafts the reply without the number. For drafting, that is often enough, because the task never needed the number.
Redaction removes or masks the value. It does not keep a consistent stand-in that the work can carry from one step to the next. That limit matters little for a one-off prompt and a great deal for an agent. The difference is the subject of a companion page on runtime tokenization and redaction for AI agents. An agent reconciling a payment has to match the account on the invoice screen to the account in the ledger and then post to the right record. If both values are blanked, the agent cannot tell whether they match. If both become ACCT_001, it can.
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 invoice, the account number is replaced by ACCT_001 at the render layer before any model reads the screen. The same number on the ledger screen is also ACCT_001, so the agent can follow the relationship without receiving the number. Amounts, dates and codes the work runs on stay in clear where the policy says so. When the agent proposes the posting, a named person approves it, and the real value resolves only at the approved destination at that moment.
OWASP frames sensitive information disclosure as a risk that begins with what a model is given access to (OWASP, 2025). Both designs act on that principle; they act on it for different content. Prompt redaction reduces what a person sends. Render-layer tokenization reduces what an agent reads. The 2026 firewall study reached the same conclusion for tools: stripping data a call does not need, at a boundary outside the model, outperformed detection inside it on the benchmarks tested (Bhagwatkar et al., 2026). The study also cautions that benchmark results near 100 percent deserve scrutiny, which applies to any vendor's detection rate, RedactSure's included.
How do the two designs handle agents, approvals and the record?
An employee prompt has a human author who reads the answer and decides what to do with it. An agent closes that loop itself: it reads, decides on a next step and acts. The control question moves from "what was sent" to "what was shown, what was done, and who agreed."
Prompt Security's product page addresses part of the second question. It describes least-privilege access within a defined scope and an audit log of agent actions (SentinelOne, 2026). The page reviewed does not state whether a person approves agent actions one by one or whether the product blocks tool calls.
RedactSure's design starts from the agent. 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, submits or changes a record on its own. The operator can watch the same tokenized stream the model works from, pause it and take over. A prompt injection that succeeds against the agent collects tokens and the remaining task context, and the consequential action still stops for a named person. That design follows a point MIT Technology Review made about model refusal: refusal is a control inside the model with known gaps, so the defenses stacked around it each have holes (MIT Technology Review, 2026). An approval held by a named person outside the model is a control the model cannot argue past.
The record follows the same split. Prompt Security's page describes a searchable audit log of agent actions, decisions and enterprise system interactions, plus dashboards of prompt counts and violations prevented (SentinelOne, 2026). The AI Control Record records every screen the agent saw as tokens, every action and every approval with a name, and exports to the customer's own SIEM. The NIST AI Risk Management Framework organizes AI risk work into Govern, Map, Measure and Manage (NIST, 2023). Discovery and dashboards of that kind support Map and Measure across the organization. A run record with named approvals supports Manage for each workflow while it runs.
Where does RedactSure sit?
For enterprise AI usage that runs through agents, RedactSure governs the run itself. The Planner sets which values on each screen become tokens, a named person confirms that policy before the run, and the model receives the task's fields with identifiers as tokens. Real values resolve only at approved destinations at the moment of an approved action, and a named person approves every payment, submission and record change. The agent works the organization's existing applications as rendered, inside a secure virtual machine with customer-held keys, with no per-application integration and no change to user permissions. The model can be any vendor's, swappable without moving the controls. Deployments are in pilot.
RedactSure is not a shadow AI discovery tool and does not inspect what employees paste into chat assistants. Organizations with both needs evaluate the two controls on their own terms. Everyone else controls where data can go. RedactSure controls what the AI can see.
Methodology and limitations
This page rests on the Prompt Security product page on the SentinelOne site, accessed October 14, 2026 and cited as vendor documentation, and on standards and research read as of October 13, 2026: the OWASP Top 10 for LLM Applications 2025 entries for LLM01 and LLM02 and the NIST AI Risk Management Framework (standards and official guidance); Greshake et al. and Bhagwatkar et al. (preprints); and MIT Technology Review (press). RedactSure statements are limited to the design the company publishes about itself.
Statements about Prompt Security are limited to what that page states. Where it does not state a point, such as placement relative to the model, detection and tokenization methods, approval workflows and tool-call blocking, this page says so, which is a statement about the page reviewed, not about the product. No product was tested, no session was run and no benchmark is reported. Vendor features change; the page carries its date and is revised when the documentation is reviewed again. Pricing, licensing and deployment requirements were out of scope. The billing record example describes a record type, not any organization.
Whether either product, or both together, meets an organization's requirements is a determination for its counsel, its assessor, and its security and compliance officers, who hold the policies, data inventory and risk tolerance the decision depends on.
What the record shows
The two products govern different moments in enterprise AI usage. A control on the AI exchange governs what is sent to and returned from an AI tool; a render-layer control decides what an agent's model reads on a screen it opened and holds each consequential action for a named person. An agent program governed only at the prompt path still hands its model whatever the application rendered, unless that traffic passes through the control. The test a reader can apply: for one screen of one agent workflow, show what the model received and who approved the step that changed the record.
Frequently asked questions
Does Prompt Security cover AI agents as well as employees?
The product page lists agents with MCP servers among what it covers, with least-privilege access and an audit log of agent actions (SentinelOne, 2026). The page reviewed does not state approval workflows or tool-call blocking. Ask SentinelOne for the current agent documentation and test it against one real workflow.
Is redaction the same as tokenization?
No. Redaction removes or masks a value, which suits a one-off prompt where the task never needed it. Tokenization replaces each value with a consistent stand-in such as ACCT_001, so an agent can match the same account across screens and act on it, while the real value resolves only at an approved destination at the moment of an approved action.
Can an organization run both products?
Yes. They govern different tracks. A prompt layer inspects what employees send to AI tools and finds unsanctioned ones. RedactSure governs agent runs inside a governed environment, tokenizing screens before the model reads them and holding consequential actions for a named person. RedactSure does not change user permissions and does not inspect employee chat traffic.
Does RedactSure find shadow AI?
No. RedactSure is not a discovery tool. Its agents are tethered to the applications a named person connected, so the agent program it governs is known by design. Discovery of unsanctioned tools employees already use belongs to a different control, such as an extension, a gateway or network monitoring.
What does a prompt injection collect in each design?
If injected content reaches a model through a path the prompt layer does not inspect, the model holds whatever real values it read. 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 remaining task context, and the consequential action still stops for a named person.
Which control should an organization evaluate first?
Start from the risk already visible. Employees pasting records into chat assistants and unknown AI tools in use point to a prompt and discovery control. Agents reading records and proposing payments or record changes point to a run-level control: what the model received on each screen, which values became tokens, and who approved the step that changed the record.
Related reading
What Controls Are You Actually Putting in Place to Stop Employees From Feeding Sensitive Data to Public AI? · What Is the Difference Between Runtime Tokenization and Redaction for AI Agents? · How to Handle Shadow AI Usage · What Is the Difference Between RedactSure and an AI Gateway for Protecting Sensitive Data? · Does Banning AI Tools Work? · What Is Shadow AI and Why Is It So Hard for Security Teams to See? · What Is AI Agent Oversharing and How Can Data Move Between Apps Without Anyone Noticing? · On the RedactSure blog: The Two Gaps AI Agents Opened in Your Security Stack
Sources
Peer-reviewed research and preprints
- Kai Greshake 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
- Kasa Bhagwatkar 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
- SentinelOne, "Prompt Security | SentinelOne AI Security Platform," product page (vendor documentation, accessed October 14, 2026). https://www.sentinelone.com/fr/platform/securing-ai-prompt/
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
- Arthur Holland Michel, "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, "What Is the Difference Between Runtime Tokenization and Redaction for AI Agents?" (2026). https://redactsure.com/research/what-is-the-difference-between-runtime-tokenization-and-redaction-for-ai-agents
- 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. SentinelOne and Prompt Security are trademarks of their respective owners; 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.