Human vs AI Agent ID Management: Okta’s Next-Gen Security Strategy

Okta is a cloud-based identity management service for enterprises.

In the coming years, the biggest challenge facing corporate security teams will not be managing the IDs of “human employees,” but rather those of “AI agents.” Todd McKinnon, Co-founder and CEO of Okta, explicitly identified “Identity Protection in the AI Era” as the next primary battlefield on the company’s official blog Okta Blog Official, noting that the scope of issues that cannot be covered by traditional ID management methods is expanding rapidly.

This article compares “human ID management,” which has long been used in corporate environments, with the rapidly growing “AI agent ID management,” using Okta’s strategy as a guide to clarify what is changing and what IT security professionals need to prepare for now. The content is organized to serve as a practical basis for decision-making.

Human ID Management vs. AI Agent ID Management: Comparison Table

Let’s start with the big picture. The nature of ID management required differs considerably between human employees and AI agents moving within the company. The main differences are summarized in the table below.

Particular attention should be paid to the differences in “permission granularity” and “operation history.” For human employees, broad categories like “Sales Department Role” have functioned adequately. In contrast, AI agents exhibit cross-functional behaviors, such as calling multiple external APIs within a single workflow, writing to databases, and sending emails. Role-based permission design cannot strictly control “what an agent can access.”

The OAuth 2.0 authorization framework standardizes the mechanism for assigning fine-grained scopes to tokens IETF RFC 6749 The OAuth 2.0 Authorization Framework, and is gaining renewed attention as foundational technology for agent-level access control. The existence of standards that define what is permitted at the token level is a tailwind for AI agent management.

Furthermore, because AI agents act autonomously, failure to record the path of decision-making (“why this operation was performed”) makes it difficult to determine the cause of incidents. It is not enough to simply have a record of API calls.

Characteristics and Limitations of Human ID Management

Human ID management, which corporate IT security departments have built over many years, has evolved around three pillars: SSO (Single Sign-On), Multi-Factor Authentication (MFA), and Lifecycle Management (granting permissions upon hiring and revoking them upon termination). This is also the domain provided by major ID management vendors such as Okta.

The following three points are particularly refined in this area:

First, the SSO mechanism that bundles the increasing number of SaaS applications that humans cannot memorize into a single login. Second, the standardization of MFA to counter password leak risks. Third, the automation of permission changes associated with hiring, termination, and department transfers. All of these are designed on the premise that “humans log in only a few times a day,” “access during business hours,” and “retain permissions until they leave.”

However, these premises do not apply to AI agents. Agents operate 24/7 and may repeat authentication on a second-by-second basis; moreover, the concept of “leaving” does not exist for them. Simply applying traditional mechanisms results in authentication logs becoming so massive that audits cannot keep up, and designing triggers for permission revocation becomes difficult.

The NIST Electronic Authentication Guideline SP 800-63B organizes Assurance Levels (AAL) and combinations of authentication factors NIST SP 800-63B Digital Identity Guidelines. However, this is a guideline premised on “humans authenticating,” and direct application to agent authentication is not assumed. Even by U.S. government security standards, the agent side remains a blank space in regulations.

CEO McKinnon also emphasizes this structural gap on the official blog. He indicates that extending traditional ID management to AI agents requires a fundamental redesign, not merely an extension of account issuance.

With the spread of AI agents, the role of ID management is expanding from simple human login control to defining trust boundaries between autonomous systems—the gist expressed by Okta CEO Todd McKinnon in his official blog post. Okta Blog Official

New Domains Covered by AI Agent ID Management

The elements covered by AI agent ID management can be broadly decomposed into three: access permission scope design, granularity of operation history and audit logs, and integration with state management across sessions. Let’s examine them in order.

Access Permission Scope Design

It is assumed that AI agents operate by combining multiple tools. Industry trends in agent development show a shift from DIY (custom implementation) to using dedicated frameworks by 2026. As frameworks provide state management and guardrails, it has become possible to define “what goal the agent is pursuing and which tools it uses to what extent” outside of code.

From an ID management perspective, this necessitates mechanisms to restrict API access permissions “per agent” and “per task.” Human role designs are too coarse, requiring infrastructure capable of issuing finer scopes with time limits. Okta’s traditional concept of lifecycle management offers room for application to automate this scope issuance.

Anthropic has emphasized the importance of permission control for each tool call in its official documentation regarding Claude’s tool usage Anthropic Official Documentation Tool use overview, explaining that access control must be integrated into each layer of agent implementation. Both framework-side and ID platform-side responses are required.

Granularity of Operation History and Audit Logs

In another technical explanation covering multi-agent collaboration, a phenomenon of accuracy degradation in chain-type multi-agent collaboration has been reported, with the degree depending on architecture (approx. 24% drop in linear chains, approx. 5% in hierarchical types). The cause is that “the ambiguity of summaries compiled by the first agent accumulates each time it is passed to the next agent,” a structure similar to the human game of “telephone.”

Viewing this problem from the ID management side, audit logs that can track “who received which instruction and made which decision” become extremely important. Records stating merely “Agent A called an API” are insufficient to identify causes when problems arise later. Designing to save inputs, outputs, and decision rationale linked to IDs will be a differentiating factor for future agent ID management.

As a standard for audit logs, the “gen-ai” namespace has been added to OpenTelemetry’s semantic conventions, and standardization is progressing for recording LLM call inputs and outputs as telemetry OpenTelemetry Semantic Conventions for Generative AI. If ID management infrastructure incorporates this specification, agent operation history can be accumulated in an industry-standard format.

State Management Across Sessions

In a technical article handling AI agent memory, “true agent memory is not a large context window, but a persistent state that evolves across sessions” is defined. This is characterized as a “database problem” combining vector search, graphs, relational data, and ACID transactions.

From an ID management standpoint, this creates a responsibility to link and manage this memory with IDs. The memory held by agents (what customer support was handled previously, what data was processed) may contain personal or confidential information, giving rise to new issues such as how to process the memory of an agent that has lost permissions and how much to disclose when passing it to another agent. Managing logins is no longer the end of the story.

Additionally, the business application of AI agents and corporate strategic concerns are organized in another article, “Behind ‘60% of AI Agent Development in Production’: Why 80% of Japanese Companies Fear Vendor Lock-in,” so those in the introduction consideration phase are advised to refer to it as well.

Comparison of Major ID Management Vendors’ AI Agent Support

Okta is not the only option. There are several powerful vendors in the cloud ID management market, with differences in how they promote AI agent support. Here, we compare four representative products.

Okta acquired Auth0 in 2021 for approximately $6.5 billion Okta Official Auth0 Acquisition Completion Press Release 2021-05-03, holding a portfolio covering both developer-focused API areas and employee-focused SSO areas. Since AI agents exhibit both the aspect of “behaving as API clients” and “crossing multiple SaaS platforms like employees,” vendors possessing know-how in both areas are well-positioned to respond.

Microsoft Entra ID supports three types as Workload identities: “Applications,” “Managed IDs,” and “Service Principals” Microsoft Entra ID Official Documentation, making it easy to leverage existing frameworks for agents running on Azure. For companies advancing business automation combined with Microsoft 365, Entra ID often becomes the practical solution.

Google Cloud IAM’s Workload Identity Federation is a mechanism that converts short-lived tokens issued by external ID providers into GCP-side tokens Google Cloud Workload Identity Federation Official, offering the benefit of not needing to store long-term credentials on GCP. In designs where AI agents call GCP services, there is high value in prioritizing this mechanism.

SaaSpocalypse and Okta’s Strategic Position

Okta CEO McKinnon expresses strong caution regarding the industry discussion called “SaaSpocalypse (The End of SaaS).” According to an interview with The Verge, he fears that as generative AI evolves, companies may begin to think they can “code the tools they need themselves,” leading them to terminate traditional SaaS contracts one after another, describing this concern as “borderline paranoid” The Verge – Decoder Podcast McKinnon Interview.

This discussion has two sides.

On one hand, if companies internalize individual business tools, there is a benefit of reducing license costs. On the other hand, few companies can handle the ID management, security, and audit response for internalized tools themselves. Ultimately, cross-platform infrastructure like ID management platforms is likely to become more important than during the peak of the SaaS era.

McKinnon’s logic targets this point. Even if individual SaaS decreases, the “infrastructure for centrally managing employees, the tools they create, and the AI agents running them” will remain. Rather, in an era where AI agents run behind business operations, the scope of ID management will expand beyond human SaaS management. Okta itself is centering its strategy on feature expansions that can handle AI agents in line with this reading.

This strategic position is a common movement among other major ID management vendors. The entire industry is moving toward what is described as an “Identity Fabric,” with the idea of handling humans, services, and AI agents on the same platform becoming widespread.

Practical Steps Companies Should Prepare Now (Recommended by Use Case)

Based on the discussions so far, we specifically indicate “what should be done now” by role. Avoiding judgment by vaguely saying “it depends on each person” provides no basis for decision-making.

Companies that should prioritize optimizing Human ID Management

• SSO and MFA have not yet been rolled out company-wide.
• It takes too long to delete accounts of resigned employees.
• The actual usage of SaaS has not been audited/inventoried.

If these conditions apply, companies should solidify the foundation for humans using a mature ID management platform like Okta before tackling AI agent support. Stepping into agent management without a foundation will ultimately result in scattered logs and operational failure.

Companies that should advance proactive preparation for AI Agent ID Management

• SSO and MFA are already established company-wide.
• The company is operating multiple AI agents in production or plans to do so.
• The industry has strict accountability requirements for “who did what” in audits.

If these conditions apply, there is value in undertaking the following practical steps.

First, solidify the design policy for agent accounts. Whether to mix them in the same namespace as humans, create a dedicated tenant, and how much to automate issuance and expiration. Second, proactively prepare the audit log infrastructure. Starting log design after agents begin to move will make it impossible to catch up on both log volume and meaning. Third, place a “switch to stop immediately in case of runaway.” Prepare a path to invalidate IDs instantly when an agent starts behaving unexpectedly.

While these steps overlap with Okta’s areas of expertise, they can also be supported by other major ID management vendors or the company’s own IAM (Identity and Access Management) infrastructure. The important thing is not choosing a specific product, but deciding on the policy of “managing agents with the same rigor as humans.”

Phased Rollout in 90 Days

Listing only “what needs to be done” can make it difficult to start, so we present a migration roadmap condensed into a 90-day timeline. It is tailored to the granularity executable by the IT security department of a mid-sized company.

This roadmap is not an idealistic concept on paper, but is based on the granularity of companies that have actually implemented Zero Trust. Microsoft’s published Zero Trust implementation reference also shows a design reaching Phase 1 to Phase 3 in about 100 days Microsoft Zero Trust Official Guidance, making the 90-day span fall within a realistic range.

Frequently Asked Questions

Q. Can individuals use Okta?

Okta is primarily provided as an ID management platform for enterprises and organizations, and is not a product designed for the daily login management of individual users. Note that its positioning differs from free password managers for personal use.

Q. Is AI agent ID management necessary immediately?

While the company is not yet operating AI agents in production, it is more reasonable to prioritize solidifying the foundation for human ID management. However, starting discussions on audit log infrastructure and account design policies when entering the introduction consideration phase will reduce rework during production deployment.

Q. How does it differ from existing IAM infrastructure?

Traditional IAM (Identity and Access Management) is designed with human employees in mind. AI agent support introduces additional requirements such as a surge in authentication frequency, task-based permission issuance, and audit logs including decision paths. The mainstream approach is not to discard existing IAM, but to add extensions for agents.

Q. What is the pricing structure?

Okta uses a corporate contract model where fees vary by company size and features used. Specific prices require a quote based on requirements, so it is most reliable to contact the official website and provide your number of users, number of integrated SaaS, and feature requirements.

Q. Can IDs for AI agents be substituted with bot users in Slack or Microsoft Teams?

Substitution is possible for limited business scopes, but it is unsuitable for production operations. Bot users are confined to the API scopes of specific SaaS, lacking the credential design necessary for cross-tool usage and session-spanning permission management. Management via a dedicated ID platform is recommended for agents handling multiple SaaS.

Q. Can open-source IAM infrastructure (such as Keycloak) also be used?

Keycloak supports OAuth 2.0 / OIDC, making the issuance and verification of Client Credentials for agents technically possible. However, the combination of audit log infrastructure, operational load at scale, and compliance response depends on the company’s resources. For organizations with thin IT security staffing, commercial cloud ID platforms generally have lower operational loads.

Summary: What to Choose if Confused

Human ID management and AI agent ID management use the same word “identity,” but the underlying design premises are quite different. Human management suffices with authentication a few times a day and broad permissions, whereas agent management requires second-by-second authentication, task-based permissions, and audit logs including decision paths.

We provide final recommendations by role.

For companies where the foundation of human ID management is still insufficient, solidify that first. It is reasonable to roll out SSO, MFA, and lifecycle management company-wide on a mature platform like Okta before proceeding to agent support discussions. Attempting agent management without a foundation will only result in being overwhelmed by a flood of logs.

On the other hand, for companies where the human foundation is in place, proactive preparation of agent account design policies and audit log infrastructure should be done now. The changes in this area are as fast as CEO Okta described “paranoid,” and situations where ID management fails to catch up during production deployment should be avoided.

Whether McKinnon’s remarks are viewed as mere sales talk or as a precursor to structural change is a judgment for each. However, the direction of ID management’s scope expanding from humans to AI agents is a trend consistent across the industry. There is no harm in starting preparations now.

References

• Okta Blog Official
• Okta Official Auth0 Acquisition Completion Press Release (2021-05-03)
• IETF RFC 6749 The OAuth 2.0 Authorization Framework
• NIST SP 800-63B Digital Identity Guidelines
• Microsoft Entra ID Official Documentation
• Microsoft Zero Trust Official Guidance
• Google Cloud Workload Identity Federation Official Documentation
• Anthropic Official Documentation – Tool use overview
• OpenTelemetry Semantic Conventions for Generative AI
• The Verge – Decoder Podcast Okta CEO Todd McKinnon Interview

This article was written by the AI Tool Zukan Editorial Department based on information available at the time of writing. Evaluations may change due to product updates, third-party benchmarks, price changes, or supported runtime variations. Re-verification is recommended for content that has aged.