TL;DR: The underlying issue is no longer just protecting users, but managing non-human access at the same trust boundary as people, as 1Password’s inclusion on CRN’s 2026 Security 100 list comes as the company positions identity security around human, machine, and AI agent access, reflecting a wider shift in how access is governed across SaaS sprawl and automation, according to 1Password and CRN.
At a glance
What this is: This is a vendor announcement about CRN’s Security 100 recognition that uses the moment to argue that identity security now has to cover human, machine and AI agent access together.
Why it matters: IAM, PAM and NHI teams need to treat access governance as a shared trust layer across people, workloads and agents, because older employee-centric controls do not map cleanly to AI-driven operating models.
Context
Access governance is no longer a people-only problem. In environments shaped by SaaS sprawl, automation and AI-driven tools, the trust boundary now includes machine identities and autonomous agents alongside human users.
That shifts the IAM conversation from login experience to governed access across all identity types. For programmes built around employee identity alone, the gap is not authentication strength but whether the operating model can issue, scope and retire access for non-human actors with the same discipline.
Key questions
Q: How should security teams build identity governance across humans, machines, and AI agents?
A: Start with a single inventory that records identity type, ownership, access scope, and system relationships across human users, service accounts, tokens, and AI agents. Then align IAM, PAM, and security monitoring around the same data so entitlement review, anomaly detection, and offboarding use one governance picture instead of three disconnected ones.
Q: Why do traditional access reviews miss non-human identity risk?
A: Traditional access reviews are built around people, managers, and stable directories. Non-human identities are created in cloud platforms, CI/CD systems, and SaaS tools, often without a manager or a clean review path. If the review population is incomplete, the assurance result is incomplete too.
Q: What breaks when AI agents inherit access from users and service accounts?
A: The main failure is that inherited access can be broader than the agent’s actual task, so privilege becomes easier to reuse than to govern. Once an agent can chain tool calls across systems, the original approval no longer describes the full blast radius. Security teams need to treat inherited access as a live identity surface, not a one-time provisioning artifact.
Q: Should organisations re-evaluate partner-led AI adoption through an identity security lens?
A: Yes. Partner-led rollout can hide access expansion inside deployment convenience, especially when SaaS, automation and AI tools are added quickly. Organisations should review who owns the access path, what actor type is being granted it, and how quickly it can be revoked.
Technical breakdown
Why human-centric access models break under non-human identities
Traditional IAM assumes the access subject is a person whose approvals, lifecycle and review cadence are reasonably stable. Non-human identities do not behave that way. Service accounts, tokens, AI agents and other machine identities can be created quickly, reused across systems and embedded into workflows where ownership is diffuse. Once those identities participate in SaaS estates and automation chains, the control problem shifts from login to lifecycle governance, entitlement scope and offboarding. The trust boundary is no longer the user session. It is the full set of identities that can act independently of a human operator.
Practical implication: map every non-human access path to an owner, scope and expiry rule rather than treating it as a side effect of application rollout.
What agentic AI changes in access governance
An AI agent changes the access model because it can initiate actions at runtime, not just consume pre-approved credentials in a fixed workflow. That matters for identity governance because the actor may select tools, trigger downstream requests and continue operating without a human checkpoint between decisions. In practice, this means access policies written for static service accounts are not enough when the identity can adapt its behaviour during a session. The key question becomes whether the programme governs the action path, not just the credential that enabled it.
Practical implication: classify agent access as a governed runtime identity problem, not merely a secrets-management problem.
How channel partners are being pulled into identity security decisions
The channel signal matters because partner-led adoption is often where new access models enter the enterprise. When solution providers help customers adopt AI agents and AI-powered tools, they also inherit responsibility for explaining who or what is receiving access, from which device or system, and under what governance rules. That makes identity security a deployment concern, not a back-office control. Partners that cannot distinguish human access from machine or agent access will struggle to help customers avoid over-scoping privileges or creating unmanaged trust relationships.
Practical implication: build partner-facing governance questions into every deployment discussion for SaaS and AI-driven services.
NHI Mgmt Group analysis
Access governance is now a multi-actor discipline, not an employee control problem: This announcement reflects a broader industry shift in which the trust boundary extends to machine identities and AI agents. Once access is no longer limited to employees, the core governance question becomes who or what is authorised to act, under what lifecycle, and with what oversight. Programmes that still centre only on human users will miss the identities that increasingly execute the work.
Human-paced review models do not fit runtime identity behaviour: Identity governance controls were built around stable subjects, predictable approval points and review cycles that assume access persists long enough to be certified. That assumption weakens when access is consumed by services and agents embedded into automated workflows. The implication is that entitlement governance must move closer to issuance and runtime scope rather than relying on after-the-fact review.
Channel-led adoption is now an identity design decision: When partners guide customers through SaaS, automation and AI adoption, they also shape how access is structured. If the access model is not explicit about human, machine and agent identities, the result is governance drift hidden inside deployment convenience. Practitioners should treat partner-assisted rollout as a control point, not just a procurement motion.
Identity security is becoming the control plane for AI adoption: 1Password’s message shows where the market is heading. The question is less whether organisations will adopt AI tools and more whether they can govern those tools through a shared access layer that handles people, workloads and agents consistently. That direction favours teams that can operationalise lifecycle governance across all actor types.
The named concept here is identity trust boundary expansion: Access decisions are no longer confined to human sign-in events. They now span machines, service accounts and autonomous agents, which means identity programmes must define the trust boundary around execution, not employment status. Practitioners should reframe access governance around actor type, action scope and lifecycle ownership.
What this signals
Identity trust boundary expansion: Access governance is shifting from user-centric controls to actor-centric controls. That means the programme must distinguish whether a request comes from a person, a workload or an AI agent before it decides how trust is issued and retired.
Programmes that still rely on employee lifecycle logic will struggle as automation and AI-driven tools become part of the operating model. The practical signal is to move governance closer to issuance, ownership and revocation for every non-human identity.
For identity teams, the next planning question is whether access policies can be enforced consistently across SaaS, automation and agentic workflows without making the human model do work it was never built to do.
For practitioners
- Define actor-specific access classes Separate human, workload and agent access into distinct governance classes so approvals, ownership and review logic do not blur across identity types.
- Inventory non-human access paths Catalogue every service account, token, automation identity and AI agent that can reach SaaS applications, then assign a clear business and technical owner.
- Set lifecycle rules for agent access Require explicit issuance, expiry and revocation rules for AI agents and other non-human identities before they are allowed into production workflows.
- Review partner-led deployments for scope creep Use channel deployment reviews to check whether the access model grants broader application reach than the task actually requires, especially where automation is involved.
Key takeaways
- The core issue is not simply securing more logins, but governing access for humans, machines and AI agents through the same identity programme.
- Once non-human identities enter mainstream workflows, employee-centric review and approval models no longer capture the real trust boundary.
- Teams need actor-specific ownership, lifecycle and revocation rules if they want identity security to keep pace with AI-driven adoption.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and MITRE ATT&CK address the attack and risk surface, while NIST CSF 2.0 and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | The article centres on access scope expanding beyond employees into non-human identities. |
| NHI-10 — Human Use of NHI | The channel context raises the risk of people using machine or agent access as if it were user access. | |
| Recommendation — Review non-human access grants for excess privilege and align scope to each actor's actual task. Separate human and non-human access paths so people do not inherit or reuse NHI credentials informally. | ||
| NIST CSF 2.0 | PR.AA-05 — Access Permissions, Entitlements and Authorizations | The article is fundamentally about governing permissions across multiple identity types. |
| Recommendation — Map all access permissions to actor type and verify that entitlements match current business need. | ||
| NIST SP 800-53 Rev 5 | IA-5 — Authenticator Management | Machine and agent access depends on credential lifecycle management and revocation discipline. |
| Recommendation — Manage authenticators so non-human credentials are issued, rotated and revoked on defined schedules. | ||
| MITRE ATT&CK | TA0006 — Credential Access | The article is about protecting the credentials that enable human, machine and agent access. |
| Recommendation — Hunt for exposed or misused credentials that expand access beyond intended identity boundaries. | ||
Key terms
- Non-Human Identity (NHI): A digital identity assigned to a non-human entity such as a software application, service account, API key, bot, machine, or AI agent that enables it to authenticate and interact with systems without direct human involvement. NHIs now outnumber human identities in most enterprises by 25 to 50 times.
- Agentic AI Identity: The complete set of credentials, permissions, and governance controls applied to an autonomous AI agent, covering authentication, authorisation, action logging, and access revocation. Distinct from traditional NHI because agent identities are often ephemeral, delegated, and multi-hop.
- Metadata Trust Boundary: A metadata trust boundary is the line between tool content that can be safely consumed and tool content that must be validated before use. For agentic systems, descriptions, examples, and schemas are security-relevant inputs because they can influence decisions and trigger actions with real-world impact.
- Lifecycle Governance: Lifecycle governance is the set of controls that cover creation, assignment, review, rotation, and retirement of identities and credentials. For NHIs, it is the difference between a temporary automation asset and a persistent access risk. Strong lifecycle governance keeps ownership and expiry tied to actual business use.
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.
Published by the NHIMG editorial team on June 10, 2026.
Updated on October 10, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org