For decades, enterprise identity systems have been built around people signing in.
AI agents introduce a more complicated access path. An agent may act for an employee, run inside a separate workload, call an MCP server and reach a downstream system with credentials issued somewhere along the way. By the time the request arrives, several identities may be involved, and it may be unclear which one should determine access.
Identity and security vendors are approaching that problem from different directions, including identity governance, workload authentication, privileged access, credential management and runtime authorization.
This list is not exhaustive. It represents a deliberate cross-section of approaches rather than a ranking of every vendor addressing AI agent identity. Buyers should use it as a framework for comparison, not a complete market map.
What is IAM for Agentic AI?
IAM for agentic AI applies identity and access management controls to AI agents and the software workloads in which they operate.
At its core, it answers three questions whenever an agent makes a request:
- Who is this?
- What can it do?
- Given the current context, is this access allowed right now?
The primary functions may include agent discovery, workload authentication, authorization, credential delivery, delegated user context, lifecycle governance and access logging.
A platform that inventories agents and assigns owners serves a different purpose from one that authenticates a running workload and enforces policy during a live transaction. A secrets manager also performs a different task from an authorization service or MCP gateway.
Buyers should therefore look beyond whether a vendor claims to secure AI agents. The more useful questions concern which identity the product verifies, which authority it governs and where it can stop an unauthorized request.
Why Does Agentic AI Strain Traditional IAM?
Human IAM assumes someone is sitting at a keyboard. It expects an interactive login, an MFA prompt, a session timeout and a person who can respond when the system detects something unusual.
None of that necessarily applies to an autonomous agent.
AI agents may operate continuously, chain actions across systems and delegate tasks to other agents. There may be no password to enter, no browser session to track and no user available to challenge.
Traditional IAM remains necessary because the person directing an agent still requires a trusted identity. The difficulty begins after that person signs in. The organization must preserve enough context to govern the agent’s later actions without treating the agent as a complete extension of the user.
Which Identity Security Vendors Should Buyers Consider for AI agents?
The following vendors represent several parts of the identity stack and should not be treated as direct substitutes. The list is also not exhaustive, as the market continues to expand and established identity providers add new agent-focused capabilities.
1) Aembit
Aembit provides IAM for agentic AI and other software workloads. It authenticates workloads, evaluates contextual access policies and brokers credentials across cloud, SaaS and on-premises environments. Its platform also supports blended human-agent identity, policy-based just-in-time access, token exchange and an MCP Identity Gateway that authenticates and authorizes agent-to-MCP-server connections at the point of the call, rather than relying on a static API key configured in advance.
Where it fits: Runtime authentication, authorization and credential delivery for agents and workloads accessing enterprise resources.
What buyers should examine: Aembit’s core focus is runtime access – the point where workload identity, user context, policy and credential delivery converge as an agent reaches a resource. Organizations that require extensive discovery, certification or broader identity governance may continue using an IGA or NHI governance product alongside it.
2) Astrix Security
Astrix, now owned by Cisco, focuses on AI agents and non-human identities. Its platform provides discovery, governance, risk analysis and audit, while its Agent Control Plane can provision agents with short-lived credentials and scoped access.
Where it fits: Discovering shadow agents and NHIs, analyzing their privileges and governing them from deployment through retirement.
What buyers should examine: Buyers should distinguish between controls applied during agent provisioning and policy enforcement performed during each subsequent resource request.
3) Entro Security
Entro provides security and detection capabilities for AI agents, secrets and other non-human identities. It maps agents to their credentials, permissions, creators and resources, then monitors their behavior for misuse or compromise.
Where it fits: NHI discovery, ownership, secrets exposure, posture management and detection and response.
What buyers should examine: Entro’s principal strength lies in visibility, context and threat detection. Buyers should determine whether they also need a separate control for workload authentication and preventive access enforcement.
4) Oasis Security
Oasis combines NHI discovery and lifecycle governance with Agentic Access Management. The product describes intent-aware policy, short-lived session identities, time-bound access and continuous audit for AI agents.
Where it fits: Enterprises seeking a common program for service accounts, machine identities and AI agents, with both governance and agent access controls.
What buyers should examine: Buyers should test the available integrations, supported credential types and location of enforcement for their applications, agent frameworks and infrastructure.
5) Veza from ServiceNow
Veza from ServiceNow applies an authorization graph to human and non-human identities. Its platform discovers identities, maps effective permissions, identifies excessive access and supports governance across SaaS, cloud and on-premises resources. Veza has also introduced controls for governing AI agents alongside other identities.
Where it fits: Access intelligence, entitlement analysis, ownership and least-privilege governance.
What buyers should examine: Veza can show who or what can access a resource and help govern those permissions. Buyers should determine which system will authenticate the workload and enforce the resulting policy during a live transaction.
6) Idira
Idira, formerly CyberArk, is rooted in privileged access management. Following its acquisition by Palo Alto Networks, completed in February, its broader identity security platform covers privileged accounts, secrets, machine identities, workload identity, governance and AI agents. Its Secure AI Agents offering includes agent discovery and an identity broker that can govern access to MCP servers.
Where it fits: Agents that require privileged access to infrastructure, administrative accounts, databases and sensitive systems.
What buyers should examine: Buyers should establish how broadly its controls extend beyond privileged workflows to routine agent access across APIs, SaaS applications and other business systems.
7) HashiCorp Vault
HashiCorp Vault is an identity-based secrets and encryption management system. It centralizes secrets, rotates credentials, generates credentials on demand and records client interactions.
Where it fits: Storing, issuing, rotating and auditing credentials that applications and agents still need to use.
What buyers should examine: Vault manages credentials rather than eliminating the need for them in every case. Buyers should decide whether agents can and should retrieve credentials from Vault or whether a workload IAM layer should use Vault on their behalf and keep the credentials outside the agent.
8) Microsoft Entra
Microsoft Entra provides human IAM, workload identity, governance and specialized identities for AI agents. Entra Agent ID supports agent identity blueprints, agent identities, ownership, sponsorship, authentication and lifecycle governance within the Microsoft identity environment.
Where it fits: Organizations building agents through Microsoft 365, Azure, Copilot and other services tied closely to Entra.
What buyers should examine: Enterprises with substantial non-Microsoft infrastructure should verify how agent identity, workload authentication and policy extend to third-party SaaS, other clouds and private applications.
9) Okta
Okta extends its established human identity platform to AI agents. Its approach encompasses agent discovery, registration, ownership, risk analysis, access policy and lifecycle governance.
Where it fits: Organizations that want to manage agents through the same identity provider and governance framework used for employees and applications.
What buyers should examine: Buyers should determine how Okta verifies the active workload behind an agent and where its policies are enforced during individual tool calls and downstream access requests.
10) SailPoint
SailPoint Agent Identity Security brings AI agents into its identity governance platform. It emphasizes discovery, ownership, access pathways, certification and lifecycle governance.
Where it fits: Enterprises that need accountable ownership, entitlement reviews and governance for agents alongside human and non-human identities.
What buyers should examine: Governance establishes which access should be approved. Buyers should identify which product or destination control applies those decisions when an agent attempts to use that access.
Where Does SPIFFE Fit?
SPIFFE is an open standard for assigning strongly attested, cryptographic identities to software workloads. SPIRE is its open-source reference implementation. Together, they allow a workload to obtain a verifiable identity document and use it to prove its identity to another system.
SPIFFE addresses an important part of agent identity: establishing which workload is making a request. It does not, by itself, determine what the workload may do, preserve delegated human authority, broker credentials for every downstream system or govern access across resources that do not accept SPIFFE identities.
There is also a practical distinction between adopting the standard and operating the implementation. SPIRE requires its own supporting infrastructure and engineering effort, and enterprises may still need separate controls for SaaS applications, legacy systems, non-SPIFFE credential types and agent workflows that cross several trust boundaries.
For that reason, SPIFFE is better understood as an identity foundation than as an 11th vendor in this guide. A workload IAM platform can use SPIFFE identity as trusted evidence while adding authorization policy, credential delivery and audit controls around it.
What Capabilities Should Buyers Evaluate?
The most useful comparison begins with the access path rather than the vendor’s category.
How does the product authenticate the workload?
An agent registration record does not prove which running process is making a request. The platform should verify the active workload using trusted evidence from its cloud, host, cluster or runtime environment.
How does it handle credentials?
Agents should not store persistent API keys or passwords when credentials can be issued, exchanged or used at request time. Buyers should determine whether the product delivers credentials to the agent, holds them in a broker or replaces them with federated identity.
Can it preserve human and agent identity together?
Sometimes an agent acts on behalf of a user. Both identities matter. The access decision should confirm that the user may reach the resource, the agent may perform the task and the workload is operating in an approved environment.
Where does policy enforcement occur?
Discovery and governance show that an identity exists and who owns it. Runtime enforcement determines whether the present request may proceed.
A denial may occur during token issuance, at an MCP gateway, through a privileged access broker or at the destination. The location determines which actions the product can prevent.
What remains in the audit record?
The record should connect the initiating user or system, agent, hosting workload, policy decision, credential and destination. Where another agent or MCP server participates, the record should preserve that delegation path where technically possible.
How Should Enterprises Choose Among AI Agent Identity Security Vendors?
Most enterprises will use several parts of this stack.
A human identity provider may authenticate the user. An IGA or NHI governance platform may discover agents, assign owners and review permissions. A workload IAM platform may authenticate the active workload and enforce access. A PAM product may protect privileged destinations, while a secrets manager retains credentials that cannot yet be removed.
Aembit’s principal distinction is its focus on runtime access and policy-based enforcement. Astrix, Entro, Oasis, Veza and SailPoint place greater emphasis on discovery, ownership, permissions or governance. CyberArk concentrates heavily on privileged access. Vault manages secrets. Microsoft and Okta extend established human identity systems to agents.
The proper choice depends on the control an organization needs, not on how closely a vendor’s product description uses the phrase “AI agent.” Two products in the same category can differ sharply in where they authenticate the workload and where they enforce policy, and a governance platform is not a substitute for a runtime control even when both mention agents. Products from several of these categories may remain in the same architecture because they perform different functions.
For each vendor, buyers should ask:
- Which identity is verified?
- How is the active workload authenticated?
- Whose authority does the agent carry?
- Which credential reaches the destination?
- Where is policy enforced?
- What evidence remains after the action?
An agent identity has limited value when it exists only in an inventory. It becomes operationally useful when the enterprise can use it to govern access.
