Privileged access used to be a human problem. You decided who on your team got admin rights to the production database, you vaulted the credentials they needed, you recorded their sessions and you pulled their access the day they left. The discipline of privileged access management (PAM) grew up around that model and did it well.
Then the workloads showed up. Applications, services, CI/CD pipelines, automated jobs and now AI agents all need to reach sensitive systems at machine speed, without a human in the loop. Gartner’s 2025 Magic Quadrant for Privileged Access Management, published in October, makes the shift explicit: the market is now evaluated on its ability to manage privileged access for machines and cloud environments alongside traditional human privilege.
In the 2024 report, Gartner described PAM vendors as beginning to address nonhuman identities. The 2025 report treats “PAM for machines scenarios” as an expanded capability area that every serious vendor is expected to cover, and the evaluation criteria now explicitly include workload identity and secrets management. The market has caught up to the problem.
Catching up is not the same as closing the gap. PAM’s architectural primitives, which were designed for interactive human sessions, do not cleanly map to how workloads authenticate and consume access. That mismatch is why workload identity and access management (workload IAM) emerged as a distinct discipline rather than a feature inside existing PAM suites.
Why PAM Was Built for People, Not Workloads
Traditional PAM was designed around a specific user: a named human administrator who periodically needs elevated rights to fix a problem, deploy a change or investigate an incident. The tooling reflects that reality. Privileged sessions are interactive and recorded. Credentials live in a vault and are checked out through a request. Approvals route to a manager. Session lengths are measured in minutes to hours, and when something goes wrong, you can tie the action back to a specific person in your directory.
Those controls work because they match the behavior of the thing being controlled. A human admin can tolerate a 10-second approval step, can authenticate with multifactor authentication (MFA) and can be held accountable after the fact.
Workloads do none of that. A Kubernetes pod may live for less than a minute before it is replaced. A Lambda function can authenticate hundreds or thousands of times in a short window. A CI/CD job pulls a credential, does its work and terminates before any human could approve anything. There is no session to record in the conventional sense, no interactive prompt to wait on and no person on the other side of the request. Time-based one-time password (TOTP) authenticators, SMS codes and push notifications were all designed around a human being physically present at the moment of access. None of them translate to workload-to-workload communication.
That gap widens as environments grow more automated. Machine identities outnumber human identities by 82 to 1 in enterprises, according to CyberArk’s 2025 Identity Security Landscape. AI agents, which take action autonomously and call APIs on behalf of users, compound the scale further and introduce a new wrinkle: the agent itself is a privileged machine identity, but it often acts using credentials delegated from a human.
Where Traditional PAM Falls Short for Nonhuman Identities
PAM vendors have been extending their platforms to cover nonhuman identities for several years, and the 2025 Gartner report shows measurable progress on that front. But three architectural gaps continue to hold these tools back when the identity in question is a workload rather than a person.
Secrets management is a static answer to a dynamic problem. The default PAM approach to securing a nonhuman identity (NHI) is to store its credential in a secure vault and rotate the secret on a schedule. That works when the credential has a long and predictable lifecycle. It starts to fail when a workload needs a credential for 30 seconds in the middle of a pipeline run, or when thousands of ephemeral services all need scoped access to different backends at the same time. Vaulting the secret does not answer the more important questions: is the workload requesting this credential the one it claims to be, and should it have access right now? The same CyberArk study found that 61% of organizations lack identity security controls for cloud infrastructure and workloads, a gap that secrets management alone cannot close.
Granular access for short-lived workloads is hard to retrofit. PAM policies are typically session-scoped and user-scoped. Adapting them to a workload that exists for a few minutes, at a call volume measured in thousands per second, is possible but awkward. Fine-grained, per-request authorization, the kind that decides whether a specific service call should succeed based on runtime context, lives much more comfortably in an architecture built for that purpose from the start.
The third gap is multicloud policy fragmentation. Most organizations run workloads across AWS, Azure, GCP, SaaS and on-premises systems in some combination. Each cloud has its own identity primitives. PAM tools can cover pieces of this, but keeping a single, coherent access policy across all of them, especially when workloads move between environments, is still the exception rather than the rule.
Underneath all three sits a definitional issue. CyberArk’s research found that 88% of organizations still define “privileged user” as applying solely to humans, even though 42% of machine identities already hold privileged or sensitive access. The tooling problem is real, but the framing problem runs deeper: if your org chart of privileged identities only has people on it, you will not protect the machine half with the same rigor no matter what tools you deploy.
What Workload IAM Does That PAM Cannot
Workload IAM is not a replacement for PAM. The two disciplines address different problem shapes, and most mature security programs will run both. PAM covers human privileged access to sensitive systems. Workload IAM handles the service-to-service layer where humans are not involved.
The capabilities that distinguish workload IAM line up against the gaps above.
Attested, secretless authentication. A workload does not check a credential out of a vault. It proves its identity through attestation, meaning the environment it runs in, such as a Kubernetes service account, an AWS IAM role or a GitHub Actions OIDC token, cryptographically vouches for what it is. Access is then brokered based on that verified identity. Because no long-lived secret sits in the code or a config file, there is nothing for an attacker to harvest.
Just-in-time, ephemeral credentials. When a workload needs to reach a downstream service, it gets a short-lived credential issued at runtime, scoped to that specific call and expiring in minutes. An attacker who steals one gains almost nothing. The attack economics change: harvesting credentials that expire before you can weaponize them is not worth the effort.
The third capability is policy enforcement at the request level, across environments. Workload IAM evaluates each access request against a single policy that lives outside the cloud provider, the Kubernetes cluster or the secrets vault. A service in AWS reaching a database in GCP, or a workload on-premises calling a SaaS API, hits the same policy engine. That eliminates the patchwork of cloud-specific rules that makes multicloud governance so painful today.
Because the policy layer is centralized, workload IAM can also make access decisions that take the current state of the workload into account. If the host is flagged by your endpoint security tool, the cluster is unhealthy or the request is coming from an unexpected region, the credential is never issued. Conditional access on the workload side works much like it has for human IAM, with signals tuned to machine context instead of human context.
Agentic AI makes the mismatch harder to ignore. An AI agent acting on behalf of a user is, from a security standpoint, a privileged machine identity that also carries a human’s authority. Session-based PAM controls struggle with this because the agent is not a human and not a traditional workload either. Workload IAM handles it cleanly: the agent authenticates with its own attested identity, and the user’s authorization is evaluated at request time as a separate policy input.
Start With What You Already Know Is Sensitive
The practical question for most security teams is how to close the NHI gap without getting stuck in an open-ended discovery exercise. Some organizations start by trying to inventory every service account, API key and machine identity across every environment before taking action. That is a long road, and by the time you finish, the inventory is already out of date.
A faster approach is to start with what you already know is sensitive. Your payment systems, customer data stores, identity providers, production database clusters and cloud control planes are already on a short list somewhere in your organization. Securing the nonhuman access paths into those systems first, with attested identity and short-lived credentials, delivers disproportionate risk reduction while the broader discovery work continues in parallel.
PAM will keep evolving, and the 2025 Gartner MQ makes clear that the market expects machine identity coverage to be table stakes going forward. Even so, a tool designed around human sessions can only stretch so far to cover ephemeral workload access. Aembit was built for the workload side from the start. It enforces attested identity, ephemeral credentials and centralized policy across hybrid and multicloud environments, so the controls you apply to machine access work the same way as the ones you already trust for the human side.
The change starts with definitions. When a privileged identity can be a workload as readily as a person, and each is secured with controls matched to its behavior, the gap Gartner has been tracking for years narrows where it matters operationally.
