Extended access management for AI agents and trusted devices

At a glance

What this is: This is 1Password’s case that Extended Access Management is needed because legacy IAM and IGA do not fully govern SaaS discovery, trusted devices, and AI-driven access paths.

Why it matters: It matters because IAM teams now have to govern access across people, apps, devices, and AI workflows without creating friction that slows business operations.

Context

Modern access governance has become broader than login control. As organisations distribute work across SaaS, personal devices, and AI-driven workflows, the control point shifts from authentication alone to whether access is issued to the right subject, on the right device, and for the right application.

1Password’s article frames this as a gap in traditional IAM and IGA rather than a narrow product problem. Its central claim is that discovery, device trust, and governance need to operate together when work happens across human users, trusted endpoints, and AI agents.

The article’s sports partnership framing is promotional context, but the governance signal is real: access control is being repositioned as a performance and trust layer. That is typical of the broader market shift, where identity programmes are expected to absorb both user experience and operational speed.

Key questions

Q: How should security teams govern access across SaaS sprawl?

A: Security teams should govern SaaS sprawl with one inventory, one policy model, and one review process that covers both human and non-human access. The practical goal is to connect application approval, entitlement review, and revocation to business ownership. Without that linkage, access governance becomes a manual cleanup exercise instead of a control system.

Q: Why does device trust matter when credentials are already authenticated?

A: Because a valid credential does not prove the endpoint is safe. Device trust adds posture, compliance, and ownership signals to the access decision, which reduces the chance that unmanaged laptops, tablets, or phones become the path to sensitive applications.

Q: What breaks when AI agents are discovered but not lifecycle-managed?

A: Discovery alone breaks down because the organisation still cannot tell who owns the agent, whether it is still needed, or when its access should end. That gap leaves credentials, permissions, and API keys active long after the business purpose has expired, which is how shadow agents become persistent risk.

Q: Should IAM teams treat people, devices, SaaS apps, and AI agents as the same access problem?

A: No. They are linked, but they are not governed the same way. People authenticate, devices establish trust, SaaS applications need discovery and certification, and AI agents require explicit ownership and lifecycle controls. A single process will miss one of those dimensions.

Technical breakdown

Why traditional IAM misses SaaS discovery

Traditional IAM assumes the organisation already knows which applications matter and which identities should reach them. SaaS sprawl breaks that assumption because applications are adopted outside central IT, used by teams directly, and often connected to business workflows before governance teams see them. Discovery becomes part of access governance, not just inventory hygiene. When unmanaged SaaS is invisible, entitlements cannot be reviewed, deprovisioned, or risk-ranked accurately. That turns identity governance into an incomplete control plane, especially where access is spread across departments and seasons of work change quickly.

Practical implication: build SaaS inventory into access governance so hidden applications are not outside review and deprovisioning controls.

How device trust changes access control

Device trust adds a condition to authentication that many legacy identity programmes treat as separate from user identity. In practice, a valid credential is not enough if the device is unmanaged, non-compliant, or outside policy. Conditional access uses device posture as an access signal, meaning the security decision includes the endpoint state at sign-in. This matters when laptops, tablets, and phones move between work and personal use, because the access decision now depends on the integrity of the device as much as the identity of the person or system.

Practical implication: tie high-risk application access to trusted-device checks so authentication cannot succeed from unmanaged endpoints.

What AI agent access means for governance

AI agent access introduces a non-human subject that needs scoped permissions, audited usage, and clear ownership. The governance problem is not only whether the agent can authenticate, but whether it can reach the right application context without inheriting excess privilege from the human or workflow that created it. That makes agentic access a lifecycle issue as well as an authentication issue. If an agent can act in production workflows, access reviews and entitlement controls must account for the agent as an identity subject, not just the operator who configured it.

Practical implication: treat AI agents as governed access subjects and assign explicit ownership, scope, and offboarding controls.

Breaches seen in the wild

• Okta support system breach 2023: A support service account credential saved in a personal Google profile let attackers take HAR files and hijack five Okta customers' sessions.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Extended access management reflects the collapse of the login-only model. The article describes a market reality in which access governance now spans people, SaaS, devices, and AI workflows, which means the control boundary has moved far beyond traditional SSO. Identity programmes that stop at authentication miss discovery and endpoint trust, leaving material exposure in the access layer. Practitioners should treat access as a full lifecycle governance problem, not a sign-in event.

SaaS discovery has become an access-control prerequisite, not an inventory side task. If teams do not know which applications are in use, they cannot enforce meaningful governance, recertification, or deprovisioning. That makes shadow applications a control failure, not just an operations issue. The practical consequence is that access governance now depends on application visibility before it depends on policy enforcement.

Trusted-device enforcement is now part of identity assurance. The article’s emphasis on Device Trust shows that access decisions increasingly depend on endpoint compliance, not just credentials. That aligns with zero trust thinking, but the operational challenge is that device state must be evaluated at the point of access. Practitioners should interpret device posture as an identity signal that belongs inside access policy.

AI agents force identity governance to recognise non-human access subjects. When the article says the right person or AI agent should have access to the right app from a trusted device, it is acknowledging that agentic access has to be owned, scoped, and offboarded like other non-human identities. Agentic access ownership: governance breaks when an AI workflow inherits permissions without a defined lifecycle owner or review boundary. The implication is that IAM and NHI teams must align on who is accountable for agent-issued access and when it ends.

Performance language does not remove governance obligations. The article frames access as a way to preserve speed, but speed without visibility simply compresses the time available to detect poor entitlement design. That is especially true in distributed work and AI-assisted operations, where access can proliferate faster than review cycles can react. Practitioners should read performance claims as a reminder that governance must move earlier in the access path, not disappear from it.

What this signals

Access governance is moving from a login-centric model to a subject-centric model, where the control question is whether a person or AI agent can reach a specific app from a trusted device. That shift forces IAM teams to connect discovery, posture, and lifecycle controls instead of treating them as separate programmes.

Agentic access ownership: AI agents introduce a governance boundary that legacy IAM often misses because the human operator is not the same thing as the access subject. If ownership, scope, and offboarding are not explicit, the agent becomes an unmanaged identity even when its actions are technically permitted.

For practitioners

• Map SaaS discovery into access governance Use application discovery to identify shadow SaaS and AI usage before recertification and deprovisioning cycles begin. Access review scope should include unmanaged apps, not just the sanctioned catalogue.
• Bind high-risk access to trusted devices Require device compliance checks for sensitive applications so sign-in from unmanaged laptops, tablets, or phones is denied before access is granted.
• Define ownership for AI agent access Assign a named owner, scope, and offboarding path for every AI agent that can reach production systems or business data.
• Rebuild recertification around access subjects Review whether your recertification process can handle humans, applications, devices, and AI agents as distinct access subjects instead of assuming one model fits all.

Key takeaways

• Traditional IAM and IGA leave practical gaps when access extends across SaaS sprawl, endpoint trust, and AI-driven workflows.
• The governance problem is no longer just authentication strength. It is whether access can be discovered, constrained, and revoked across all the subjects that use it.
• IAM teams should connect application discovery, device trust, and non-human identity ownership so access governance keeps pace with distributed work.

Key terms

• Extended access management: Extended access management is a control model that covers access beyond traditional employee login, including SaaS, service identities, and AI agents. It tries to unify trust, entitlement, and device context so organisations can govern access wherever work actually happens.
• Device Trust: Device trust is the confidence that a requesting endpoint is known, managed, and in a compliant state. It matters because identity alone does not prove safety. In zero trust programmes, device trust becomes one of the inputs used to decide whether access should be granted or sustained.
• SaaS Discovery: SaaS discovery is the process of identifying all sanctioned and unsanctioned software-as-a-service applications in use across the organisation. It matters because cloud assurance increasingly depends on seeing where apps share data, what permissions they hold, and which identities can reach them.
• Agentic Access: Agentic access is delegated system access granted to an AI agent or autonomous workflow so it can perform defined tasks across tools and data sources. It differs from human access because the actor can execute continuously, combine actions quickly, and amplify mistakes at scale.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.