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Comparison · By Chris Sowa · Published
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What Is the Difference Between RedactSure and Okta for AI Agent Identity and Access?

Okta answers who an AI agent is and what it may access; RedactSure answers what the agent's model receives once access is granted and who approves each consequential action. An agent with a correct identity and correct scopes still reads every value on the screens it opens, so identity alone leaves exposure ungoverned.

Identity controls admit a browser session whose sensitive fields pass a separate protection gate.

Okta answers who an AI agent is and what it may access; RedactSure answers what the agent's model receives once access is granted and who approves each consequential action. An agent with a correct identity and correct scopes still reads every value on the screens it opens, so identity alone leaves exposure ungoverned. Identity is the first control most security teams reach for when agents arrive, because it is the control they already run for people: sign the agent in, give it an owner, scope its access, revoke it when the work ends. It stops at the door of the application, though, and the agent's model then reads whatever the application renders for that identity. RedactSure works on the other side of the door, with render-layer tokenization that replaces sensitive values with consistent tokens before any model reads the screen, and a named person approving each consequential action. The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.

Key findings

  • Okta's developer documentation says Okta for AI Agents "manages the registration, configuration, and lifecycle of AI agents," including their public key credentials and linked apps, with agent status deciding whether an agent can authenticate or connect (Okta, 2026).
  • Identity best practice for workloads, as AWS documents it, centers on least privilege, temporary credentials and no long-lived access keys, which settles what a workload may reach, not what it reads once it gets there (AWS, 2026).
  • Agent platforms now ship identity as a component: Google describes Agent Identity as assigning every agent "a unique cryptographic ID" for traceability and auditing (Google Cloud, 2026), and AWS lists Identity as an AgentCore component compatible with existing identity providers (AWS, 2026).
  • OWASP lists sensitive information disclosure as LLM02 in its 2025 Top 10 for LLM Applications, a risk that turns on what the model is given in the first place (OWASP, 2025).
  • Indirect prompt injection arrives through content the agent is authorized to read, such as a web page, an email or a database field (Greshake et al., 2023), and a 2026 study found that stripping unnecessary data at the agent and tool boundary cut attack success to near zero on four benchmarks (Bhagwatkar et al., 2026).

What does Okta say it does for AI agents?

Okta's developer documentation states: "Okta for AI Agents manages the registration, configuration, and lifecycle of AI agents" (Okta, 2026). The page covers registering and configuring agents, managing their lifecycle, their public key credentials and the apps linked to them. Agent status governs connection: an ACTIVE agent can authenticate or connect, and an INACTIVE agent cannot. Access is authorized through token exchange flows (Okta, 2026).

The page reviewed does not state where the product sits relative to the model or the screen, what it does with sensitive data, whether it applies policies, approvals or blocking to tool calls, or what it logs or reports (Okta, 2026).

Within the identity category, the work is well defined. An identity platform establishes who or what is acting, authenticates it, decides which applications and scopes it may use, issues the credentials or tokens that carry that decision, and records the sign-ins and grants that follow.

Identity decides whether an actor may open a door and what it may do once inside, at the level of applications, scopes and permissions. It does not, as a category, decide what values appear on the screen or in the API response once the door opens, because those values belong to the application and to the permissions the identity carries. The Okta page reviewed does not state any inspection of network traffic, API calls, prompts and responses, browser activity, endpoints or data stores (Okta, 2026).

What does identity settle, and what does it leave open?

Identity settles reach. AWS's identity best practices for workloads call for least privilege, temporary credentials and the removal of long-lived access keys (AWS, 2026). Google's Agent Identity assigns every agent a unique cryptographic ID so that autonomous actions are traceable to a specific agent and owner (Google Cloud, 2026). AWS lists Identity among the AgentCore components, built to work with existing identity providers (AWS, 2026). The direction across vendors is consistent: every agent gets a name, an owner and a scoped grant.

What identity leaves open is exposure inside the grant. Take an accounts payable agent with a correctly scoped identity in the ERP. Its permissions let it open vendor records, because the work requires it. Each vendor record shows a bank account number, a tax identifier and a remittance address. The identity was right, the scope was right, and the model still reads every one of those values, because the application renders them for any actor with that permission. Least privilege at the identity layer answers which records the agent may open. It does not answer which values on those records the model needs, a gap the page on whether least privilege covers what the AI sees works through in detail.

There is a second open question. Identity decides whether an action is permitted. A permitted action is not the same as an approved one. A payment the agent's role may release is still a payment someone should look at before it leaves. Permission is set in advance for a class of actions; approval is given in the moment for one action with its amount, payee and context in view. Identity platforms carry the first. The second needs a control that runs with the work.

How do the two products compare side by side?

The table places the two designs on the dimensions that decide agent exposure. Okta entries follow the vendor's AI agents documentation page, and where that page does not state a capability, the table says so.

Dimension Okta, per its documentation RedactSure, per its published design
What it is Okta for AI Agents "manages the registration, configuration, and lifecycle of AI agents" (Okta, 2026) AI agent controls, governance and data protection for AI co-workers working inside a governed environment
What it controls Identities and agent configurations: registration, lifecycle, public key credentials and linked apps (Okta, 2026) What the model receives from each screen, and who approves each consequential action
Where it sits relative to the model The page reviewed does not state it (Okta, 2026) Between the rendered screen and the model, inside a secure virtual machine with customer-held keys
Sensitive values The page reviewed does not state it Values chosen by policy replaced by consistent tokens such as ACCT_001 at the render layer before any model reads the screen
Credentials Manages agents' public key credentials; access is authorized through token exchange flows (Okta, 2026) Works under the user's existing permissions; permissions do not change
Agent actions Agent status controls whether an agent can authenticate or connect (ACTIVE can, INACTIVE cannot); the page reviewed does not state policies, approvals or blocking of tool calls A named person approves every payment, submission and record change at the moment it runs
Record The page reviewed does not state what it logs or reports 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 it Model agnostic; Claude, GPT, Gemini or open-weights models swap without moving the controls

The table is not a ranking. Okta manages the registration, lifecycle and credentials of AI agents, a scope beyond any single agent run, and RedactSure does not claim to be an identity provider. RedactSure intervenes at a moment the Okta page reviewed does not address: the instant a model is about to read a screen the agent was authorized to open.

What happens to a sensitive value after access is granted?

Follow one value through each design. A claims agent signs in with a scoped identity and opens a claim file. The file shows the claimant's name, Social Security number, date of birth and bank account.

In an identity-governed design, the sign-in succeeded, so the application renders the full record for that identity. The values go to the model as the application shows them. If the agent passes a field to another tool, the value travels with it. If a document in the file carries hidden instructions, the model reads those too. Greshake and colleagues showed that indirect prompt injection works through exactly this route: content the agent is entitled to read carries instructions the agent then follows (Greshake et al., 2023). OWASP ranks prompt injection first in its 2025 Top 10 for LLM Applications (OWASP, 2025). None of that is an identity failure. The identity did its job; the exposure happened after it. Credential protection answers how the agent gets in, not what it reads inside.

In a RedactSure run, the Planner sets which values are tokenized for the claims task, and a named person confirms that policy before the run. When the agent opens the claim, the claimant's name, SSN and account number become consistent tokens such as PATIENT_001, SSN_001 and ACCT_001 at the render layer, before any model reads the screen. The model receives the task's fields with identifiers as tokens. The claim amount, the loss date and the coverage facts the work runs on stay in clear where the policy says so. Real values resolve only at approved destinations at the moment of an approved action. A successful injection collects tokens, and the consequential action still stops for a named person.

Recent research points the same way. Bhagwatkar and colleagues tested a Tool-Input Firewall that strips data a tool call does not need, alongside a Tool-Output Firewall that cleans returned content, and reported near-zero attack success with little loss of utility across AgentDojo, Agent Security Bench, InjecAgent and tau-Bench (Bhagwatkar et al., 2026). The principle it supports is minimizing what reaches the model, which is the principle of Least Exposure.

Who approves the action an authorized agent takes?

Identity decides in advance which actions an agent's role may take. Supervised Delegation decides, in the moment, whether this action goes ahead. The two answer different questions, and agents need both.

The case for a control outside the model is well documented. Microsoft's AI Red Team taxonomy of failure modes in agentic systems catalogs ways an agent can act against its operator's intent, including through injected content and misused tools (Microsoft, 2025). MIT Technology Review argued in October 2026 that model refusal, the model declining a disallowed request, is treated as a primary safety mechanism while remaining far from foolproof, with vendors stacking classifiers and probes in a layered model where each layer has holes (MIT Technology Review, 2026). A permission grant does not stop a manipulated agent from taking a permitted action, and refusal inside the model may not either. A consequential action needs a control the model cannot talk its way past.

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. A budget transfer, a claim payment or a change to a vendor's remittance details stops for that person with the amount, the destination and the context in view. The AI never pays, settles, submits or decides. The person can watch the same tokenized stream, pause it and take over.

Approval design matters as much as approval existence. Copilot Studio's documentation notes that if the reviewer for flagged instructions is someone other than the person running the agent, that reviewer likely does not see the activity and cannot properly verify the request (Microsoft, 2026). An approval is only as good as the approver's view of what is being approved. RedactSure puts the approval inside the run, attached to a named person who sees the action, which is the core of AI agent action governance.

How do the records differ?

Both designs keep records, and they record different things. An identity platform's record, as a category, holds sign-ins, grants, token issuance and revocations: who or what authenticated, to which application, with which scopes, and when. Google's description of Agent Identity frames the same purpose for agents, mapping autonomous actions to a lifecycle owner for traceability and auditing (Google Cloud, 2026).

The AI Control Record is scoped to the work itself. It holds every screen the agent saw, as tokens, every action, and every approval with a name, and it exports to the customer's own SIEM. An auditor can open one run and see what the model received on each screen, which values were tokens, which step changed a record and who approved it. Because the screens are stored as tokens, the record does not become a second copy of the sensitive data.

Where does RedactSure sit?

RedactSure sits after identity, between the screen and the model. The agent works an organization's existing applications, an ERP, a claims platform, an EHR or a student information system, inside a governed environment with no per-application integration and no change to user permissions. The environment is a secure virtual machine in the cloud the customer's posture requires, on hardware-encrypted enclaves, with keys the customer holds. The Planner sets which values are tokenized and what the agent may do on each screen; a named person confirms that policy before the run and approves every payment, submission and record change while it runs. 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 every screen, action and approval lands in the AI Control Record for the customer's SIEM. The environment is model agnostic. Deployments are in pilot.

Identity tells the organization which agent is acting and what it may open. RedactSure governs what that agent's model is shown once the door opens. Everyone else controls where data can go. RedactSure controls what the AI can see.

Methodology and limitations

This page rests on published material read as of October 13, 2026. For Okta, statements rest on the vendor's AI agents developer documentation page, accessed October 14, 2026; where that page does not state a capability, this page says so. Other vendor documentation from AWS, Google Cloud and Microsoft is cited for how agent platforms handle identity, credentials and approvals. Research sources are preprints on indirect prompt injection and agent-tool firewalls; the standards source is the OWASP Top 10 for LLM Applications 2025; industry sources are Microsoft's AI Red Team whitepaper and MIT Technology Review. RedactSure statements are limited to its published design.

No product was tested. No Okta, Auth0 or RedactSure session was run, and no benchmark is reported. Where the comparison says the page reviewed does not state a capability, that statement is limited to the material reviewed on the date of review and is not a claim about Okta's full product set, partner integrations or future releases. Vendor features change; the page carries its date and is revised when documentation changes. Pricing, licensing and availability were out of scope. The accounts payable and claims examples describe record types, not any organization, and no customer or prospect is described.

Whether either product, or both together, meets an organization's requirements for a 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 that decision depends on.

What the record shows

Identity and render-layer control answer different questions about the same agent. Identity decides who the agent is, what it may open and what its role may do; render-layer control decides what its model receives on each screen and who approves each consequential action. An agent governed by identity alone, however well scoped, still hands its model every value the application renders for that identity, and a permitted action still runs without a person looking at it. The test a reader can apply: for one screen of one agent run, show what the model received and who approved the step that changed the record. An access log answers whether the agent was allowed in; it does not answer that question.

Frequently asked questions

Does Okta replace RedactSure for AI agent security?

They govern different layers. Okta's documentation describes registering, configuring and managing the lifecycle of AI agents, their credentials and linked apps, with agent status deciding whether an agent can authenticate or connect (Okta, 2026). RedactSure governs what the agent's model receives from each screen and requires a named person to approve each consequential action. An organization can run both, with identity controlling reach and RedactSure controlling exposure and approval inside that reach.

If an agent has least-privilege access, does it still see sensitive data?

Yes, when the task requires opening records that contain it. Least privilege limits which records and actions the agent may reach. Once it opens a permitted record, the application renders every value that permission allows, and the model reads them. RedactSure replaces policy-chosen values with consistent tokens such as SSN_001 at the render layer before any model reads the screen.

Does RedactSure change user permissions or replace the identity provider?

No. RedactSure works under the user's existing permissions, and permissions stay exactly as they are. What changes is what the AI can see. The identity provider continues to authenticate people and grant access. The named person who approves an action in a RedactSure run is a person the organization already manages in its identity system.

How does approval differ from authorization?

Authorization is set in advance for a class of actions: this role may release payments. Approval is given in the moment for one action: this payment, this amount, this payee. Under Supervised Delegation, a named person approves every payment, submission and record change while the run is in progress, with the action in view, and that approval lands in the AI Control Record.

What does a prompt injection collect in each design?

In a design governed by identity alone, the agent reads real values on every screen it is authorized to open, so a successful injection can reach whatever the agent read. In a RedactSure run, the model holds tokens such as ACCT_001 in place of identifiers, so a successful injection collects tokens, and the consequential action still stops for a named person.

Which control should an organization put in place first?

Start with identity for every agent: an owner, scoped access and a revocation path, because an unowned agent is a standing liability. Then ask, for each workflow, what the model needs to see and who should approve the step that changes a record. Those two questions are where RedactSure's render-layer tokenization and Supervised Delegation apply.

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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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