Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs Fortra DLP (Digital Guardian): 2026 Comparison & Alternative
Fortra DLP controls data moving across endpoints, networks, email, cloud and AI sites, while RedactSure, a Fortra DLP 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 Fortra DLP alternative for AI agents already run DLP for people; the open question is what an agent receives when it opens a patient record. Fortra's acquisition page introduces the product as "Digital Guardian (Now Fortra DLP)" (Fortra, Acquires, 2026). Its control follows data as it moves: an endpoint agent, network inspection, and policies on what a user submits to an AI site. RedactSure's control sits on the rendered screen, replacing each identifier with a token before any model reads it.
This page quotes Fortra's own pages and uses one record throughout: a patient record in a hospital billing application the organization bought rather than built, with a name, a medical record number, a date of birth and an insurance member ID, opened by an agent working the billing queue as a specialist would. What Is Render-Layer Tokenization? defines the approach.
Key findings
- As a Fortra DLP alternative for agent work, RedactSure replaces values on the rendered billing screen before any model reads it, so the agent sees PATIENT_001 and real values resolve only at an approved destination (What Is Render-Layer Tokenization?).
- A model that received only tokens cannot be steered into disclosing a record it never held; OWASP lists "Personal identifiable information (PII) may be disclosed during interactions with the LLM" as the risk a token settles first (OWASP, LLM02, 2025).
- None of the Fortra pages reviewed describes an AI agent reading a rendered application screen or any substitution of values on it; the AI page puts its control on "questions submitted to AI sites" (Fortra, AI Data Security, 2026).
- Fortra DLP acts on a transfer or a use its policy has identified: its agent "captures events at the system, user, and data level" (Fortra, DLP Solutions, 2026).
- For personal data, Fortra DLP will "discover, monitor and control personal data transmitted on the network, in use on workstations, or at rest," a control over where data sits rather than what a model is shown (Fortra, GDPR, 2026).
- For AI, Fortra adds a content pack to "Monitor and block questions submitted to AI sites based on sensitive data patterns," a control on what a person sends rather than on what a model reads (Fortra, AI Data Security, 2026).
How do the two compare side by side?
Each row sets a Fortra DLP statement from its own pages against the render-layer design, with the check a buyer runs on one workflow.
| Dimension | Fortra DLP, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | On data "in use, in motion, and at rest" (Fortra, DLP Solutions, 2026) | On the screen the agent works in, before any model reads it | Where in the billing application a value is replaced |
| How coverage starts | An endpoint agent, network inspection and gateways placed in the data path | The agent works inside the governed environment; the bought application is unchanged | Whether the agent's browser session is a path the endpoint agent sees |
| What the model receives | Policies that "Monitor and block questions submitted to AI sites based on sensitive data patterns" (Fortra, AI Data Security, 2026) | The screen with PATIENT_001, MRN_001, DOB_001 and MEMBER_001, consistent for the whole run | One billing screen, exactly what the model received |
| How real values are restored | Data "encrypted, masked, or blocked from unauthorized access or transfer"; no release step for a model is described (Fortra, PCI, 2026) | Released at the approved destination when a named person approves the action | What releases the member ID to the claim, and whose name is recorded |
| Who approves an action | A policy: the platform "identifies unauthorized exfiltration, and executes automated remedial actions" (Fortra, DLP Solutions, 2026) | A named person approves every claim submission and record change | A person's name on the submission, not a rule |
| The audit record | Endpoint events, under the user whose session produced them | Every screen as tokens, every action and approval, exported to the customer's SIEM | Whether the record shows what the model saw, under the agent's identity |
Read down the two middle columns and the object each product governs is different: Fortra DLP governs movement its policy has recognized, so its answer to what the model saw is a log of transfers, while RedactSure governs what an agent reads and answers with the screen the model received. They also differ on who turns a token back into a real value: a policy action on Fortra's pages, a named person in RedactSure's design.
What does Fortra DLP protect, and where does it act?
Fortra's DLP page describes an endpoint agent that "captures events at the system, user, and data level," network inspection in which Fortra "inspects all network traffic and enforces strict protection policies," and gateways that add "deep content inspection and automated redaction" to managed file transfers and proxy servers (Fortra, DLP Solutions, 2026). For health data, "Adaptive redaction allows for content that would constitute a HIPAA breach to be dynamically modified (redacted or sanitized), allowing the rest of the communication to be delivered unhindered" (Fortra, HIPAA, 2026).
Each benefit depends on one condition: the data must cross a path the control inspects, as a transfer, a paste, an upload or a prompt a person submits, and a policy must first recognize it as sensitive. The pages reviewed describe controls on data movement and none describes an agent reading a rendered application screen. That boundary matters because an agent reaches the patient record through the billing screen, where nothing moves until the model has read it.
The 86-product review counts Fortra with the 58 that control where data can go; what its public documentation does not describe is a record of what the model was shown or an export to a SIEM, and no product in the 86 describes a token the agent can use for the task without holding the real value (How Many AI Security Products Control What an AI Agent Sees?).
What does an agent read when it opens a patient record in a billing application?
The billing application was bought, so the organization never wrote the code that renders it; the agent opens the queue as a billing specialist would, and the screen shows the name, medical record number, date of birth and insurance member ID. A page rendered in an agent's browser session is neither a transfer nor a paste, so the endpoint agent and the network inspection have nothing to recognize until the model has read every field; a rule must first identify content as sensitive before it can allow, warn, block or redact (Runtime Tokenization vs DLP for AI Agents). The AI-site control acts later still, on "questions submitted to AI sites" (Fortra, AI Data Security, 2026), by which time the record is in the model's context.
The exposure happens in the model, where OWASP warns that disclosure "can result in unauthorized data access, privacy violations, and intellectual property breaches" (OWASP, LLM02, 2025). HIPAA's minimum necessary standard asks a covered entity to "make reasonable efforts to limit protected health information to the minimum necessary to accomplish the intended purpose of the use, disclosure, or request" (Cornell Law School, 2026); whether a full screen read by a model meets it is a question for counsel. The agent also used the specialist's session, so the application's log records the specialist.
Which control fits which work?
Keep Fortra DLP for
- Data movement across endpoints and the network, where its agent "captures events at the system, user, and data level" (Fortra, DLP Solutions, 2026).
- Regulated data on managed devices, where "Endpoint protection ensures that data flagged for HIPAA protection is secured throughout its entire lifecycle" (Fortra, HIPAA, 2026).
- People using generative AI sites, where the content pack can "block data egress via AI sites right out of the box" (Fortra, AI Data Security, 2026).
Add RedactSure for
- Agents reading screens in applications the organization bought, where the model receives PATIENT_001 instead of the real record (What Is Render-Layer Tokenization?).
- A named person who approves every claim submission or record change before the agent carries it out (What Is Supervised Delegation?).
- The record of what the model received on each screen, exported to the customer's SIEM (What Is an AI Control Record?).
Most organizations running agents will keep both; this block sets out which product protects the screen.
How do you test it on one workflow?
Run the comparison on one real billing workflow in an afternoon, with both vendors present and every answer written down.
Step 1. Choose a workflow the agent will run, such as the billing queue, and pick one screen from it, the patient record, rather than a diagram of the data flow.
Step 2. Ask each vendor to show that screen exactly as it reached the model; the output settles the question, real values or tokens. Fortra's control acts on data "in use, in motion, and at rest" (Fortra, DLP Solutions, 2026); ask where a page rendered for a model falls in that list.
Step 3. List every application in the workflow and mark each as covered by the endpoint agent, routed through network inspection, or neither; then mark whether the agent's browser session is in any of those paths.
Step 4. Trace one value, the insurance member ID, from the screen to the claim: what released it, when, and whose name is in the record. An automated remedial action is a rule written in advance; ask which product records a named approver.
Step 5. Calculate first-day coverage. If the queue touches five applications and the endpoint agent sees two as transfers, coverage for the agent's reads is two of five at best. Compare the costs that differ: policy tuning for an agent's traffic, and the agent work delayed meanwhile.
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, recorded under its own identity, and sees a different screen from the billing specialist: each identifier is replaced with a consistent token before the model reads anything, so the patient is PATIENT_001, with MRN_001, DOB_001 and MEMBER_001, on every screen of the run. Real values resolve only at approved destinations at the moment of an approved action, a named person approves every claim submission and record change, and every screen, action and approval goes to a control record exported to the organization's SIEM.
OWASP notes that "Indirect prompt injections occur when an LLM accepts input from external sources, such as websites or files," and a billing note is such a source; its listed mitigation, to "Restrict the model's access privileges to the minimum necessary for its intended operations," is set on the screen for an agent that works from pages (OWASP, LLM01, 2025). The deciding layer must act between the application producing the screen and the model reading it (AI Gateway, DLP or Tokens: What Controls AI Exposure?), and a model that only received MRN_001 has no number to send (What Is Least Exposure?).
What the record shows
Ask the buyer's one question: who decides what the model receives, or whose name is on the release? On the billing screen, the patient's name, medical record number, date of birth and insurance member ID are tokens before the model reads it, RedactSure's job for this record as the Fortra DLP alternative, with real values released only for an approved claim and the record under the agent's identity. Fortra DLP's real coverage is data movement, endpoint events, network traffic, email and cloud channels, and what a person submits to an AI site; its pages describe no control over what a model reads from a rendered page in a bought application. Run one billing task with the agent on a test record, ask each vendor for the model's input from that screen, then follow the member ID to the claim and see which record names the approver.
Methodology and limitations
This page rests on six Fortra pages (DLP, AI data security, three compliance pages and the acquisition page), two OWASP Top 10 for LLM Applications entries, the HIPAA minimum necessary standard as republished by Cornell's Legal Information Institute, 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, no customer of either is described, both change, and the page carries its date. RedactSure is deploying now with enterprise and government customers. The patient record is a record type, not an organization. Compliance determinations belong to counsel, assessors and the organization's security team; nothing here replaces a pilot of one workflow on its own screens.
Frequently asked questions
Does Fortra DLP protect data sent to an LLM?
Only where a person sends it through a path Fortra inspects. The AI data security page describes a content pack that will "Monitor and block questions submitted to AI sites based on sensitive data patterns," a control on a submission. An agent that opened the patient record holds the fields before any prompt leaves, so the inspection judges a prompt built from the real record.
Does Fortra DLP cover what an agent reads on a billing screen?
Not from the pages reviewed. Fortra's pages describe an endpoint agent capturing system, user and data events, network inspection, email and cloud controls, and policies on what users submit to AI sites. A page rendered in an agent's browser session is none of those events until something leaves it. Ask Fortra, in writing, whether a model's read of a page appears in its event record.
Who approves the agent's action, a policy or a person?
A policy, on Fortra's pages, which say the platform "identifies unauthorized exfiltration, and executes automated remedial actions," a rule written in advance about data. In RedactSure's design a named person approves each claim submission or record change when the agent proposes it, and the approval carries the name. The buyer should know which control decides whether a corrected member ID reaches the claim.
What record shows that the agent, and not the billing specialist, read the record?
None on Fortra's pages for a page read. The DLP agent "captures events at the system, user, and data level," and those events carry the user whose session produced them, so an agent under the specialist's login leaves the specialist's name. RedactSure's AI Control Record keeps every screen as tokens and every action and approval under the agent's identity, exported to the SIEM.
Does adaptive redaction keep protected health information away from the model?
Only in communications. Fortra's HIPAA page says adaptive redaction lets content "that would constitute a HIPAA breach" be "dynamically modified (redacted or sanitized)" while the rest of the communication is delivered. A billing screen the agent reads is no communication in transit, so the model receives the record as displayed. For an agent, redaction has to happen on the screen, before the read.
Can a hidden instruction in a billing note make the agent leak the record?
Only if the model holds something real to leak. OWASP lists input from external sources such as websites or files as the path of indirect prompt injection, and a billing note is such a source. Content inspection judges what leaves after the model has read the note. A model that received MRN_001 can be steered and still has no number to send, the argument in Does Prompt Injection Expose Customer Records?.
How RedactSure closes the screen-level gap in movement-based data protection
Movement-based controls act on a transfer or prompt a policy has recognized, so an agent reading a rendered billing screen receives the whole patient record first. Fortra's AI control blocks questions a user submits to AI sites, after the model has the page.
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the billing queue under the specialist's session, the screen shows the name, medical record number, date of birth and member ID, and the model receives all four. Inside the governed environment: the screen shows PATIENT_001, MRN_001, DOB_001 and MEMBER_001, the real member ID reaches the claim only when the named specialist approves it, and screen, action and approval land in the 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 Fortra DLP. Bring one billing task and see, with test values, what the model receives and who approves the claim. Book a demo