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Comparison · By Chris Sowa · Published
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What Is the Difference Between RedactSure and BigID for Sensitive Data Used by Enterprise AI?

BigID's AI security page describes finding the sensitive data AI uses, governing AI identities and permissions, and running remediation workflows across the data estate. RedactSure tokenizes identifiers on the screen before the model reads it and holds every consequential action for a named person. Treating one as the other leaves either the estate or the running agent ungoverned.

Enterprise records become protected fields before entering model and access control stages.

BigID's AI security page describes finding the sensitive data AI uses, governing AI identities and permissions, and running remediation workflows across the data estate. RedactSure tokenizes identifiers on the screen before the model reads it and holds every consequential action for a named person. Treating one as the other leaves either the estate or the running agent ungoverned. The two answer different questions. One asks where sensitive data lives and which AI systems can reach it. The other asks what a specific agent sees on a specific screen during a specific task, and who approves what it does next. Security teams buying for enterprise AI often need both answers, and a page that blurs them invites a gap at the moment an agent opens a record. The second question is the one render-layer tokenization was designed for. The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.

Key findings

  • BigID's AI security page says the product covers AI applications, copilots, agents, models, prompts and workflows that touch enterprise data, along with AI identities, permissions and what AI systems can access (BigID, 2026).
  • The same page says BigID identifies sensitive, regulated and confidential data used by AI, protects data in AI prompts and pipelines, and uses remediation workflows to reduce access and exposure; the page reviewed does not state that it blocks, redacts, masks or tokenizes values (BigID, 2026).
  • The page reviewed does not state where BigID sits relative to the model or to the screen an agent reads (BigID, 2026).
  • OWASP lists sensitive information disclosure as a top risk for LLM applications, covering personal data and confidential business data that reach or leave a model (OWASP, 2025).
  • Researchers showed that instructions planted in content an LLM application reads can redirect it, which makes anything the model can see a possible target (Greshake et al., 2023).
  • A 2026 study found that a model-agnostic firewall which strips unneeded data before tool calls, paired with one that cleans tool outputs, brought attack success near zero on four agent benchmarks (Bhagwatkar et al., 2026).

What does BigID's AI security page say the product covers?

BigID describes its AI security offering with the line "Secure AI Before Risk Becomes Reality" (BigID, 2026; vendor documentation, accessed October 14, 2026). The page frames the product around the AI systems that touch enterprise data: applications, copilots, agents, models, prompts and workflows. It also names AI identities, their permissions and what AI systems can access, and it refers to "sensitive data environments."

On sensitive data, the page says BigID identifies sensitive, regulated and confidential data used by AI, protects data in AI prompts and pipelines, and runs remediation workflows that reduce access and exposure. On agents, it says the product inventories AI agents, monitors their activity, governs their permissions and enforces policies through workflows. On reporting, it says BigID tracks AI interactions and sensitive data activity and lists reporting among its capabilities.

The page reviewed does not state several things a buyer evaluating agent controls will ask about. It does not state whether the product inspects network traffic, API calls, the browser, the endpoint or agent configurations. It does not state where the product sits relative to the model or to the screen. It does not state whether it blocks, redacts, masks or tokenizes a value, or whether it raises alerts. It does not state whether agent actions go to a person for approval or whether individual tool calls can be blocked. It does not state log formats or retention. Each of these may be covered elsewhere in BigID's documentation; the page reviewed is a solutions page, and other pages may carry the detail.

What the page does state places BigID's AI security work in the data estate and the permission model around it: find the sensitive data, see which AI systems can reach it, and reduce access where the exposure is too wide. That is the question an organization has to answer before it lets any AI near its records. Mapping that reach is the first step described in how to map all the data an AI agent can reach, and the inventory of AI identities BigID describes serves the same purpose from the data side.

The gap between the two designs is the moment of work. An inventory tells a team that an agent has access to a claims system. It does not, on the evidence of the page reviewed, decide which fields of a given claim the model reads while it works.

What happens to a sensitive value in each design?

Take a single record: a customer account screen with a name, a date of birth, an account number and an open balance. An agent has been asked to reconcile the balance against an invoice.

In the design BigID's page describes, the account number is a known piece of sensitive data in a known system. BigID identifies it, associates it with the AI identities that can reach that system, and, through remediation workflows, can reduce the access those identities hold (BigID, 2026). The page also says BigID protects data in AI prompts and pipelines. The page reviewed does not state the mechanism, so a buyer should ask whether protection in a prompt means detection, blocking, masking or something else, and whether it applies to what an agent reads from an application screen as well as what a person types into a prompt box.

In RedactSure's design, the agent works inside a governed environment. Before any model reads the screen, the environment replaces the name, date of birth and account number with consistent tokens such as PATIENT_001 and ACCT_001. The model receives the task's fields with identifiers as tokens; the balance and the invoice amount stay in clear because the reconciliation runs on them. If the task requires writing the account number into a payment field, the real value resolves only at that approved destination, at the moment a named person approves the action. This is screen-level tokenization applied per task, and the principle behind it is AI agent data minimization: the model gets what the task needs and nothing more.

The difference matters because of what a model does with what it reads. OWASP names sensitive information disclosure as a top LLM risk (OWASP, 2025), and indirect prompt injection research shows that content a model reads can carry instructions that redirect it (Greshake et al., 2023). An agent with legitimate access to a system still reads every value on the screen it opens. Reducing access narrows which screens it opens. Tokenizing the render narrows what it learns from each one.

The two controls stack. A team can use an estate view to cut an agent's reach to the systems the work requires, then govern what the model reads inside those systems. Neither replaces the other.

How do the two compare side by side?

The table records what each side's documentation states. For BigID, that is the AI security solutions page as accessed on October 14, 2026 (BigID, 2026). For RedactSure, it is the current published design.

Dimension BigID, per its documentation RedactSure, per its published design
What it inspects or controls AI applications, copilots, agents, models, prompts and workflows that touch enterprise data; AI identities and permissions; "sensitive data environments" The screen an agent reads inside a governed environment, and the actions the agent takes there
Network, API, browser, endpoint The page reviewed does not state it Works the application as rendered, under existing user permissions, with no per-application integration
Position relative to the model and the screen The page reviewed does not state it Between the rendered screen and the model; tokenization happens before any model reads the screen
What it does with sensitive data Identifies sensitive, regulated and confidential data used by AI; protects data in prompts and pipelines; remediation workflows reduce access and exposure Replaces identifiers chosen by policy with consistent tokens; real values resolve only at approved destinations at the moment of an approved action
Blocking, masking, tokenization The page reviewed does not state it Tokenization at the render layer; the Planner sets which values are tokenized
Agent actions and approval Inventories agents, monitors activity, governs permissions, enforces policies through workflows; approvals and tool-call blocking not stated on the page reviewed Supervised Delegation: a named person confirms policy before the run and approves every payment, submission and record change
Record Tracks AI interactions and sensitive data activity; reporting listed; formats and retention not stated on the page reviewed Every action logged, human and computer; all PII as tokens. The AI Control Record exports to the customer's SIEM
Permissions Governs AI identities' permissions and reduces access through remediation Does not change permissions; changes what the AI can see
Model relationship The page reviewed does not state it Model agnostic; models swap without moving the controls

Several BigID cells read "not stated" because the page reviewed is a solutions page. Those cells describe the page, not the product's full capability, and a buyer should confirm each one against BigID's technical documentation or a demonstration. The RedactSure column describes a design in pilot.

Who or what is being governed, a data estate or an acting agent?

BigID's page describes governance over a population: all the AI systems in an organization, the identities they run under, the permissions those identities hold and the sensitive data they can reach (BigID, 2026). The unit is the estate. The question is coverage: has every agent been found, and is every sensitive store accounted for?

RedactSure governs a run. The unit is one agent doing one piece of work for one named person. The tethered agent works only inside the environment that person connected it to, on the applications that person chose. The question is exposure: on this screen, for this task, what should the model read?

Both questions are real, and they fail in different ways. Gartner reported in May 2026 that applying uniform governance across AI agents will lead to enterprise AI agent failure, arguing that agents differ enough that one policy set does not fit all (Gartner, 2026). An estate view supplies the inventory that differentiated governance needs. It also leaves open what happens inside each run once access is granted.

The NIST AI Risk Management Framework separates mapping a system's context from managing its risks in operation (NIST, 2023). Estate discovery fits the mapping work: what exists, what it touches, who owns it. Runtime controls fit the managing work: what happens while the system acts. Microsoft's AI Red Team taxonomy of agent failure modes includes failures that occur during execution even when configuration was sound (Microsoft, 2025), which is the territory runtime controls address.

The practical test for a buyer is to name one agent, one system and one task, then ask each vendor to show what the model receives on the first screen of that task. A product focused on the estate will show where the data is and who can reach it. A product focused on the run will show the screen as the model sees it. The answers are different because the products are pointed at different moments.

How do approval and the record differ?

BigID's page says the product enforces policies through workflows and tracks AI interactions and sensitive data activity (BigID, 2026). The page reviewed does not state whether an agent's action can be held for a person's approval, whether a tool call can be blocked in flight, or what format the record takes. Remediation workflows, as the page describes them, act on access and exposure, which places them before or between runs rather than inside a single action.

In RedactSure's design, approval sits 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 Planner sets which values are tokenized and what the agent may do on each screen; the named person confirms that policy before the run starts. The agent never pays, submits or changes a record on its own authority.

The reason to hold actions outside the model is that model-internal defenses have gaps. MIT Technology Review reported in October 2026 that model refusal is treated as a primary safety mechanism and is far from foolproof, with vendors stacking layered defenses whose individual holes they hope will not line up (MIT Technology Review, 2026). OWASP describes prompt injection as a risk that can cause a model to take unintended actions (OWASP, 2025). If an injection succeeds against a RedactSure agent, it collects tokens, and the consequential action still stops for a named person.

The record follows the same split. In RedactSure's design, the AI Control Record captures every screen the agent saw as tokens, every action and every approval with a name, and exports to the customer's own SIEM. Because the screens are stored as tokens, the record does not become a second copy of the identifiers it describes. BigID's tracking of AI interactions and sensitive data activity, as the page describes it, serves the estate view: which systems and identities touched which data. The two records answer different audit questions, and an organization may want both.

Where does RedactSure sit?

For sensitive data used by enterprise AI, RedactSure sits at the screen, between the rendered application and the model. Inside a governed environment deployed into the customer's cloud, on hardware-encrypted enclaves with keys the customer holds, identifiers chosen by policy are replaced with consistent tokens before any model reads the screen. 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 environment is model agnostic, needs no per-application integration and leaves user permissions as they are. Deployments are in pilot. An estate view such as the one BigID's page describes and a runtime control such as this one address different moments, and the first can narrow what the second has to govern.

Everyone else controls where data can go. RedactSure controls what the AI can see.

Methodology and limitations

This page rests on BigID's AI security solutions page, accessed October 14, 2026 (vendor documentation); OWASP's Top 10 for LLM Applications 2025 and the NIST AI Risk Management Framework (standards and guidance); peer-reviewed and preprint research on indirect prompt injection and agent firewalls; industry analysis from Gartner, Microsoft's AI Red Team and MIT Technology Review; and RedactSure's published design. Sources were read as of October 13, 2026, with the BigID page read on October 14.

No hands-on testing of BigID or any other named product was performed. BigID's description comes from one solutions page, and where that page does not state a capability the table says so; other BigID documentation may describe it. The comparison covers the AI security offering only; BigID's broader data discovery, privacy and governance products were outside scope, and nothing here assesses their performance, accuracy or coverage. Vendor features change, and the page carries its date. RedactSure statements describe the current design, with deployments in pilot. No specific organization is described.

Whether either product, or both together, meets an organization's obligations is a determination for its counsel, its assessors, and its own security and compliance officers. A buyer should confirm each "not stated" cell directly with BigID and request a demonstration of RedactSure's tokenized screen on the buyer's own task before relying on any row of the comparison.

What the record shows

BigID's AI security page describes an estate view: finding sensitive data used by AI, inventorying agents and governing their permissions. A runtime control governs the run: what the model reads on each screen and who approves each consequential action. Relying on an estate view alone leaves the values on an open screen in the model's context once access is granted. Relying on runtime controls alone leaves the inventory of agents and data stores to some other process. The test is to name one agent and one task and ask each vendor to show what the model receives on the first screen.

Frequently asked questions

Does BigID tokenize data before an AI agent reads it?

The AI security page reviewed on October 14, 2026 does not state that BigID tokenizes, masks or redacts values. It says BigID identifies sensitive data used by AI, protects data in prompts and pipelines, and reduces access and exposure through remediation workflows. Other BigID documentation may describe additional mechanisms, so a buyer should ask BigID directly.

Can BigID and RedactSure be used together?

Yes, the designs address different moments. An estate view can find which AI systems reach which sensitive stores and cut access the work does not need. RedactSure then governs what the model reads inside the systems that remain, tokenizing identifiers on the screen and holding consequential actions for a named person.

Does RedactSure discover AI agents across the enterprise?

No. RedactSure governs agents that a named person connects to its governed environment, on applications that person chooses. Finding agents running elsewhere in the organization is discovery work, which BigID's page describes as part of its AI security offering through inventorying AI agents and monitoring their activity.

Does RedactSure change what an agent's user account can access?

No. Permissions stay exactly as they are. RedactSure changes what the AI can see on the screens those permissions open, replacing identifiers with tokens before any model reads them. BigID's page, by contrast, describes governing AI identities' permissions and reducing access through remediation workflows.

Where is each product's record kept?

RedactSure's AI Control Record captures every screen as tokens, every action and every approval with a name, and exports to the customer's own SIEM. BigID's page says it tracks AI interactions and sensitive data activity and lists reporting; the page reviewed does not state log formats, retention or export targets.

What should a buyer ask in a joint evaluation?

Pick one agent, one application and one task. Ask BigID to show which sensitive data that agent can reach and how remediation would narrow it. Ask RedactSure to show the first screen of the task as the model receives it and the approval step for the consequential action. Compare the answers against the risk the organization is trying to close.

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About the author

Christopher Sowa
Chris SowaCo-Founder, President and Chief Commercial Officer, RedactSure

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.

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