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Okta CEO and co-founder Todd McKinnon’s claim that “AI security and identity security are one and the same” is directionally right—but incomplete. At Oktane 2025 in Las Vegas, McKinnon argued that AI agents should be treated as a new class of identity because they can access applications, use tools, retrieve data and take actions on behalf of people or organizations.
Identity controls determine which agent is acting, whose authority it is using, what it may access and how quickly its permissions can be revoked. But identity is only the authorization and accountability layer of AI security. Prompt injection, model failures, data leakage, insecure code and dangerous tool behavior require additional defenses.
What McKinnon meant
McKinnon’s remarks, reported by Computer Weekly, came during Okta’s broader announcements about securing AI agents and non-human identities at Oktane 2025 on September 25–26.
The headline is a strategic simplification, not an industry definition. McKinnon’s narrower argument is that once an AI system can take authenticated actions, AI security depends heavily on identity security. Without reliable identity and access controls, an organization may not know which agent acted, what permissions it used, who approved those permissions or how to stop it.
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That argument also serves Okta’s commercial strategy: extending identity management beyond employees and customers to service accounts, workloads, API keys, automation and AI agents.
Why AI agents create a new identity problem
An AI agent is more than a chatbot that generates text. Generally, it receives a goal, uses software tools or APIs, maintains context, takes multiple steps without approval for every action and may create, modify, transmit or delete information.
A chatbot can create confidentiality and accuracy risks even without a conventional enterprise identity. An agent that can send email, alter customer records, deploy code, retrieve confidential documents or initiate a transaction necessarily needs authorization controls.
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Compared with a human user, an agent may:
- Operate continuously and at machine speed.
- Make many downstream API calls in seconds.
- Act with delegated authority from a user.
- Be replicated across environments.
- Change behavior when its model, prompt, tools or workflow changes.
- Invoke other agents and potentially accumulate privileges.
- Use credentials embedded in code, orchestration systems or shared service accounts.
Creating a username for an agent is not enough. A useful identity model must connect the agent to an owner, purpose, authorization policy, runtime context, tools, secrets and audit records.
What an agent identity should answer
Organizations should be able to answer:
- Which agent made the request?
- Who created and owns it?
- Is it acting for a person, team, application or business process?
- Which tools, records and data may it access?
- Was the action delegated by a user or initiated autonomously?
- How long should its access remain valid?
- What happens if the agent is modified, compromised, transferred or retired?
These questions are difficult when an agent operates through a shared service account. A shared account may provide access, but it weakens attribution, least privilege, ownership, revocation and anomaly detection.
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The identity-security-fabric idea
“Identity security fabric” is Okta’s architectural term, not a universally established industry standard. Okta describes it as a unified approach for managing human, customer, partner, service-account, workload and AI-agent identities across SaaS applications, databases and on-premises systems. Its description of the concept emphasizes visibility, centralized control, governance and threat monitoring.
Translated into practical controls, the model includes:
- Discovering agents and other non-human identities.
- Registering each agent and assigning an owner.
- Authenticating agents and managing their credentials.
- Recording whether an action is user-delegated or autonomous.
- Applying least-privilege authorization to tools, APIs and data.
- Using short-lived, narrowly scoped tokens where supported.
- Reassessing risk during active sessions.
- Maintaining detailed audit trails.
- Terminating sessions and revoking access quickly.
- Finding shadow, orphaned and inactive agents.
The practical thesis is that identity becomes a control plane for authority—not a complete replacement for application or AI security.
Following one agent action
The argument becomes clearer when an agent performs a real task:
- Registration: The organization knows the agent exists.
- Ownership: A person, team or business unit is accountable for it.
- Authentication: The agent proves which workload or application it is.
- Delegation: The system records whose authority, if any, it is using.
- Authorization: Policy determines which resources and operations are allowed.
- Context: Risk, session, workload, data sensitivity and transaction details influence the decision.
- Execution: The agent acts using a scoped credential or token.
- Monitoring: Activity is logged and unusual behavior is detected.
- Revocation: Access can be terminated if risk changes.
- Retirement: Tokens, secrets, schedules, webhooks and delegated permissions are removed.
What Okta announced at Oktane 2025
Okta for AI Agents
Okta announced capabilities aimed at discovering, registering, governing and controlling AI agents within its broader identity-security model. The company’s announcement describes a lifecycle covering discovery, access control, governance and monitoring.
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Do not assume every conference capability was generally available to every customer. Availability can depend on product edition, geography, preview status, integration support and contract. Current product scope should be checked through the Okta Solutions pages and the Okta Product Hub.
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Cross App Access, or XAA, is positioned by Okta as an extension of OAuth for agent-driven and application-to-application interactions. The goal is to give organizations more visibility and policy control when an agent moves between applications instead of leaving each application to manage access in isolation. Okta has cited participation or support from AWS, Box, Google Cloud, Salesforce and Glean.
XAA should be treated as an Okta-promoted, standards-oriented protocol initiative—not as a universally adopted solution. Its value depends on consistent implementation, clear delegation and consent semantics, token scoping, logging, revocation and broad adoption.
Auth0 for AI applications
Auth0 is the developer-facing part of Okta’s strategy. Its AI-related capabilities are intended for customer-facing applications and can support agent login, API access on behalf of users, approval for sensitive actions and granular authorization for retrieval-augmented-generation content. See the Okta announcement coverage and Auth0 pricing page for current product and plan information.
- Auth0: Customer identity, developer authentication and authorization for embedded AI experiences.
- Okta Workforce Identity: Employee access, enterprise applications, lifecycle management and workforce governance.
- Broader Okta strategy: A common identity-security approach spanning humans, customers, partners, workloads and agents.
Non-human identity and threat protection
Okta’s April 2025 strategy already addressed service accounts, shared accounts, break-glass identities, API keys, access tokens and automation. Its non-human identity announcement connected Identity Security Posture Management and Okta Privileged Access with AI-agent security.
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Okta also describes Identity Threat Protection with Okta AI as continuously evaluating risk and authentication policies during active sessions, including for threats such as session hijacking and compromised accounts. That can strengthen identity response, but it does not replace endpoint, cloud, application or AI-runtime security.
What identity security can solve
Identity controls are especially valuable at the point where an agent receives authority to act. They can provide:
- Authentication for users, workloads and agents.
- Delegated authorization and separation of user identity from agent identity.
- Least-privilege access to specific tools, records and operations.
- Short-lived credentials and token rotation.
- Ownership and lifecycle governance.
- Activity attribution for audits and investigations.
- Session termination and rapid revocation.
- Approval workflows for high-risk actions.
- Detection of unusual identity behavior.
What identity security cannot solve
A correctly authenticated agent can still do the wrong thing. Identity security does not by itself eliminate:
- Prompt injection: Malicious instructions can manipulate an agent into taking actions that appear authorized.
- Excessive permissions: A legitimate agent can misuse broad access.
- Model and data risks: Hallucinations, poisoned data and supply-chain problems sit outside ordinary IAM.
- Retrieval leakage: Correct authentication does not guarantee that a retrieval system will expose only appropriate information.
- Tool misuse: Email dispatch, shell execution, database writes and financial APIs need transaction-level safeguards.
- Insecure code: Vulnerabilities in the agent, plugins or surrounding application remain application-security problems.
- Weak isolation: A compromised agent may move laterally if workloads and environments are poorly separated.
Organizations still need output validation, sandboxing, secure software development, data governance, runtime monitoring, human approval for consequential actions and model-risk controls.
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Okta cited its own 2025 research saying that 91% of organizations were deploying agentic AI while only 10% had a strategy for governing non-human identities or AI agents. Those figures should be understood as Okta survey findings, not neutral measurements of the entire market.
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Okta and Computer Weekly also referenced incidents involving AI-enabled or AI-adjacent systems, including the McDonald’s hiring chatbot breach. Such incidents illustrate weak application security and credential hygiene; they do not prove that autonomous agents caused every related failure.
Questions buyers should ask
Visibility
- Can the platform discover agents created outside central IT?
- Can it find shared accounts, static API keys and embedded credentials?
- Can it map each agent to its owner, tools, data and downstream applications?
Authorization and credentials
- Can policies distinguish user-delegated actions from autonomous actions?
- Can access be limited by tool, data field, transaction type and operation?
- Are credentials short-lived, scoped and revocable?
- Can agent-to-agent calls preserve provenance without privilege accumulation?
Runtime and lifecycle controls
- Can risk be reassessed during an active session?
- Can suspicious access be terminated automatically or quickly?
- Are logs detailed enough to identify the human, agent, application and data owner involved?
- Can agents be paused, modified, reviewed and retired?
- Does offboarding revoke tokens, rotate secrets, disable jobs and remove webhooks?
Interoperability and availability
- Does the product work with your clouds, identity providers, model providers, orchestration frameworks, APIs and data stores?
- Which capabilities are generally available, preview or sales-led?
- Are there regional, public-sector, data-residency or regulated-industry restrictions?
- Does the control plane depend on a proprietary ecosystem?
Okta’s public-sector limitations illustrate why availability should be verified by edition and jurisdiction rather than assumed from commercial product pages.
Okta versus other approaches
Microsoft Entra may be the natural comparison for organizations already standardized on Microsoft 365, Azure, Defender and Purview. CyberArk is particularly relevant when the central problem is privileged access, secrets, service accounts and machine identities. Palo Alto Networks is relevant when identity controls need to connect closely with network, endpoint, cloud and security-operations systems; Okta and Palo Alto have announced joint work on identity-driven security and response.
Cloud-native workload identity can be effective when agents operate almost entirely inside AWS, Azure or Google Cloud, but may be less convenient across multiple clouds, SaaS applications, workforce identity and customer identity. A build-your-own stack using OAuth/OIDC, policy engines, secrets managers, API gateways and observability tools offers flexibility, but leaves the organization responsible for integration, policy consistency, upgrades and incident response.
The practical conclusion
McKinnon’s statement is best understood as a warning about authority. If an AI agent can act, it needs an identity model that makes its authority explicit, limited, attributable, observable and revocable.
That makes identity security a foundation of agent security—but not the whole foundation of AI security. The strongest architecture combines identity and access controls with secure application design, data protection, model governance, runtime isolation, tool restrictions and human oversight for high-impact decisions.
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