Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
Published
Last updated
RedactSure vs Forcepoint: 2026 Comparison & Alternative
Forcepoint applies policy to data at rest, in motion, in use and in AI prompts; RedactSure, a Forcepoint alternative built for AI agents, tokenizes the screen before any model reads it. RedactSure is an alternative way to think about agent browsing security, because it protects sensitive data as it is rendered on a screen, before an agent uses it.
Security teams looking for a Forcepoint alternative for AI agents already run data loss prevention for people, and the open question is what an agent reads when it opens a customer record. Forcepoint applies its control to data in stores and to data crossing a channel or a user action its policy recognizes, such as an email, an upload or a prompt. RedactSure applies its control to the rendered screen, replacing identifiers with tokens before any model reads the page.
This page quotes Forcepoint's pages for DLP, Risk-Adaptive Protection, DSPM and DDR and uses one record throughout: a customer record in a CRM, with a name, an account number, a date of birth, a home address and a service history, opened by an agent working the screen as a representative would. What Is Render-Layer Tokenization? defines the approach RedactSure uses.
Key findings
- As a Forcepoint alternative for agent work, RedactSure replaces values on the CRM page before any model reads it; the agent handles the customer as CUST_001 and the real value resolves only at an approved destination (What Is Render-Layer Tokenization?).
- A model that only ever received a token has nothing real to disclose; OWASP lists "Personal identifiable information (PII) may be disclosed during interactions with the LLM" as the risk (OWASP, LLM02, 2025).
- The DLP page lists screen capture as a user activity the endpoint control "can see and control," and no Forcepoint page describes replacing values on a rendered screen before an AI model reads it (Forcepoint, Data Loss Prevention, 2026).
- Forcepoint's DLP page says the product will "Enforce one policy everywhere data goes," naming cloud, web, AI, email, endpoint and network, a policy on data and channels, not on the page an agent reads (Forcepoint, Data Loss Prevention, 2026).
- For AI, the same page lists "inspecting prompts and responses for generative AI tools," and Risk-Adaptive Protection adds "Real-time blocking or coaching when users attempt to upload sensitive data to unsanctioned AI tools," both acting on a user's transfer (Forcepoint, Data Loss Prevention, 2026; Forcepoint, Insider, 2026).
- DSPM works before access, finding "overpermissioned and ROT data before users share it with AI applications such as Claude, ChatGPT and Copilot, or unknowingly give AI agents access," and stops at permissions, not at what an agent reads (Forcepoint, DSPM, 2026).
How do the two compare side by side?
Forcepoint's own wording sits beside what a governed environment does on the same point, with the check to run on the CRM.
| Dimension | Forcepoint, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | "Forcepoint DLP protects data in motion, at rest and in use" (Forcepoint, Data Loss Prevention, 2026) | On the CRM page, before any model reads it | Where, for one workflow, a value is replaced |
| How coverage starts | Policies applied "across endpoints, on-prem networks and cloud apps" (Forcepoint, Data Loss Prevention, 2026) | Inside the governed environment; the CRM is unchanged | Which of the agent's applications each control sees on day one |
| What the model receives | "inspecting prompts and responses for generative AI tools" (Forcepoint, Data Loss Prevention, 2026); the rendered page is not addressed | The page with tokens, CUST_001 and ACCT_001, for the whole run | One screen, exactly as the model saw it |
| How real values are restored | Enforcement is "blocking, quarantining, encrypting or coaching users" (Forcepoint, Data Loss Prevention, 2026); restoring a value is not addressed | At an approved destination, when a named person approves the action | Who authorizes the release, and where it is recorded |
| Who approves an action | Risk-Adaptive Protection "scores user risk in real time and adjusts controls automatically" (Forcepoint, Insider, 2026) | A named person approves every payment, submission and record change | A name on the address change, not a score |
| The audit record | DDR's "detailed file lineage and explainable alerts" (Forcepoint, DDR, 2026) | Every screen as tokens, every action and approval, in the AI Control Record and the customer's SIEM | Whether the record shows what the model saw |
Forcepoint governs data and the channels and people it moves through, so its answer to what the model saw covers a transfer its policy caught, not a page the model read directly. RedactSure governs what an agent reads, and its answer is the screen as the model saw it. The two also differ on who turns a token back into a real value: a policy and a risk score on Forcepoint's pages, a named person in RedactSure's design (What Is Supervised Delegation?).
What does Forcepoint protect, and where does it act?
Forcepoint's DLP page states the scope of the product in one line: "DLP scans data at rest (file shares, SharePoint, OneDrive), in motion (email, web, SaaS uploads) and in use (copy/paste, printing, screen capture)" (Forcepoint, Data Loss Prevention, 2026). That coverage follows a spreadsheet of account numbers across endpoint, network and cloud as long as it moves in a way a policy can recognize, prompts to generative AI tools included.
DSPM works earlier, on data at rest. Its page says it will "Automatically discover and classify sensitive data with AI, assess and prioritize what matters most, and remediate with precision and control," with remedies of "moving file, deleting files and changing access permissions" (Forcepoint, DSPM, 2026).
Each benefit rests on one condition: the data sits in a store the product scans, crosses a channel it inspects, or moves through a user action its endpoint control recognizes, which suits people, who move files and type prompts. An agent working a CRM moves nothing across a channel; it reads the rendered page, and the four pages describe no step that changes what that page shows a model.
What does an agent read when it opens a customer record in a CRM?
An agent assigned to update a mailing address opens the record under the representative's session: name, account number, date of birth, home address and service history. A computer-use agent takes in the whole page (What Does an AI Agent See in a Screenshot?), so the task needed one field and the model received the whole record.
No control on Forcepoint's pages sits between that page and the model. Nothing crossed a channel, DSPM set the permissions earlier, and the role allows the record. Prompt inspection may apply if the agent's model call passes through it, but by then the identifiers are already in a prompt built from the screen, the timing that Runtime Tokenization vs DLP for AI Agents describes. Forcepoint's public documentation describes logging each AI interaction event with attribution to the user, the agent acting for them, the data touched and the action taken, and does not describe giving an agent tokens it can work with in place of the values it reads on a screen (How Many AI Security Products Control What an AI Agent Sees?).
The service history adds a second exposure: a note can carry anyone's text, and OWASP states that "Indirect prompt injections occur when an LLM accepts input from external sources, such as websites or files" (OWASP, LLM01, 2025). Identity widens the gap, because any risk score attaches to the representative whose session the agent borrowed.
Which control fits which work?
Keep Forcepoint for
- Files, email and uploads, where "Forcepoint DLP protects data in motion, at rest and in use" (Forcepoint, Data Loss Prevention, 2026).
- People whose behavior changes, where Risk-Adaptive Protection will "Block or restrict sensitive actions the moment risk spikes" (Forcepoint, Insider, 2026).
- Stored data and its permissions, where DDR tracks "how files and records are created, accessed, modified, renamed, shared or deleted" (Forcepoint, DDR, 2026).
Add RedactSure for
- Agents reading screens in bought applications, where the model receives CUST_001 instead of the customer's name (What Is Render-Layer Tokenization?).
- A named person approving each payment, submission or record change (What Is Supervised Delegation?).
- A record of what the model received on every screen, with each action and approval, in the customer's SIEM (What Is an AI Control Record?).
Most organizations need both, because each covers a different part of the work.
How do you test it on one workflow?
The test takes one real workflow, one afternoon and both vendors in the room, with every answer written down.
Step 1. Pick one screen from a workflow the agent will run, such as the customer record opened for an address change, rather than a diagram of where data flows.
Step 2. Have each vendor produce the model's input for that record, as received rather than as described. A policy summary leaves the question open; the input itself shows whether the model held the real account number or ACCT_001.
Step 3. List every application in the workflow and mark each as covered by a store or channel control, or uncovered. Count the second group internally, because a vendor counts what it can reach.
Step 4. Trace the account number from token back to its real value. Write down what released it, when, and whose name the record shows; a policy decision and a person's decision are different controls.
Step 5. Work out first-day coverage. If the agent's first ninety days touch five applications and every read happens on a rendered page, channel coverage leaves all five open. Compare the two costs that differ: policy work for each new agent path, and agent work held back until it is done.
What does a governed environment change?
A governed environment is a class of control, defined by where it acts. The agent runs under its own session, and its work is recorded under its own identity. Before any model reads the CRM page, the environment replaces each identifier with a consistent token, so the customer appears as CUST_001 with ACCT_001, DOB_001 and ADDR_001 on every screen. Real values resolve only at the approved destination at the moment of an approved action, a named person approves each record change, and every screen, action and approval goes to an exported record.
Research puts the control in front of the model. Indirect prompt injection lets adversaries "remotely (without a direct interface) exploit LLM-integrated applications," so a service note can steer the agent that reads it (Greshake et al., 2023). OWASP defines excessive agency as "the vulnerability that enables damaging actions to be performed in response to unexpected, ambiguous or manipulated outputs from an LLM," which a named approver on the address change answers (OWASP, LLM06, 2025). A model that only ever received CUST_001 has no date of birth to give away, the principle in What Is Least Exposure?.
What the record shows
Whose name is on the release when an agent opens a customer record? The answer is the person who approves the action, and on this record the values the model receives are already tokens: the customer's name, account number, date of birth and address are replaced on the CRM page before the model reads it. RedactSure, as the Forcepoint alternative for agent work, releases real values only for an approved action and keeps the run on a record under the agent's own identity. Forcepoint's real coverage is data in stores, data crossing a channel and the user actions its endpoint control recognizes; its four pages describe no control over what a CRM page shows a model. The test runs on your own system: open one customer record with the agent, ask each vendor for the model's input from that screen, then trace the account number to the approval that released it and the name on it.
Methodology and limitations
This page rests on four Forcepoint product pages (DLP, Risk-Adaptive Protection, DSPM and DDR), three OWASP Top 10 for LLM Applications entries, one preprint on indirect prompt injection, and RedactSure research pages. Vendor pages were read between October 21, 2026 and October 23, 2026, and each Sources line carries the date for its page. Public documentation read in full gives a sound view of where each product applies its control; "not described" refers to the pages reviewed.
The products on this page are described from documentation; none was tested for this page, and no customer of either product is described. Documentation changes, and the page carries its date. RedactSure is deploying now with enterprise and government customers. The customer record is a record type, not an organization. Compliance determinations belong to counsel, assessors and the organization's security team, and nothing here replaces a pilot of one workflow on its own screens.
Frequently asked questions
Does Forcepoint protect data sent to an LLM?
Only where the data crosses a path Forcepoint inspects. The DLP page lists inspecting prompts and responses for generative AI tools, and Risk-Adaptive Protection can block or coach a user who uploads sensitive data to an unsanctioned AI tool. Both act on a transfer, and an agent that reads a CRM page holds the identifiers before any prompt leaves, so the inspection judges a prompt built from the real record.
Does Forcepoint cover what an agent reads on a CRM screen?
Not from the pages reviewed. The four pages describe protection for data in stores, data crossing email, web and SaaS uploads, and endpoint activities such as copy and paste, printing and screen capture. An agent reading a rendered page through its own browser session takes none of those paths. Ask Forcepoint, in writing, whether an agent's page reads appear as screen capture events.
Who approves the agent's action, a policy or a person?
A policy and a risk score, on Forcepoint's pages, which say Risk-Adaptive Protection scores user risk in real time and adjusts controls automatically. That decision is written in advance for a user. In RedactSure's design a named person approves each payment, submission or record change when the agent proposes it, and the approval carries that name. The buyer should know which control governs the address change.
What record shows that an agent, and not the representative, read the record?
None on Forcepoint's pages for a page read. DDR records data events such as reads, views and permission changes, and DLP logs the transfers its policy catches, so a page read under the representative's session leaves the representative's name in the log. RedactSure's AI Control Record holds every screen as tokens and every action and approval under the agent's own identity, exported to the SIEM.
Does DSPM stop an AI agent from reading customer records?
No. DSPM narrows what an agent can reach and leaves what it reads within that reach unchanged. Forcepoint's page describes finding overpermissioned and stale data before users share it with AI applications or give agents access, then moving or deleting files or changing permissions. Once a role allows the customer record, the page renders in full and the agent reads every field.
Can a hidden instruction in a service note make the agent leak the record?
Only if the model holds something real to leak. A note is external input, and OWASP lists input from external sources as the path of indirect prompt injection. Prompt inspection judges what leaves, after the model has read the note. A model that received CUST_001 can be steered and still has nothing real to send, the argument in Does Prompt Injection Expose Customer Records?.
How RedactSure closes the screen-level gap in channel-based data protection
Store and channel controls act on data a policy recognizes, so an agent reading a rendered CRM page receives the whole customer record. Forcepoint's DLP page names screen capture only as a user activity its endpoint control "can see and control."
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the record under the representative's session, and the model receives the name, account number, date of birth and address. Inside the governed environment: the same page shows CUST_001, ACCT_001, DOB_001 and ADDR_001, the new address reaches the real account only when the named representative approves it, and screen, action and approval land in the AI Control Record.
RedactSure is deploying now with enterprise and government customers.
Everyone else controls where data can go. RedactSure controls what the AI can see.
See it on your own workflow
Protecting the sensitive data an agent could read on a screen is a needed part of any solution that includes Forcepoint. Bring one CRM workflow and see, with test values, the record the model receives and the approval behind the change. Book a demo