Non‑human identities probably run a large share of your core processes. Service accounts, integration users, API keys, workload identities, and AI agents already outnumber human users in many critical systems. They post journals, update vendor data, trigger payments, and change configurations across ERP and SaaS.
Most identity governance programs still focus on people—HR events, human roles, manager approvals—so this population sits at the edge of the model. Dashboards show campaigns completed and users certified, but a growing share of real financial and operational risk lives in non‑human identities that are over‑privileged, poorly owned, and rarely reviewed.
In this paper, non‑human identities means the technical identities behind those automations and integrations. The issue is no longer whether they exist; it is whether you can see which of them can execute high‑risk actions, under which controls, across all systems that move money or change critical data.
The Real Problem: Risk Lives in What Non-Human Identities Do
Traditional identity governance was built for a world where risk was mostly about who could log in to which system. It focused on joiner–mover–leaver workflows, directory-driven provisioning, and role models that map people to entitlements. That model still works reasonably well for human users in a handful of core platforms.
It breaks down when large portions of the identity population are non-human, when risk is concentrated in actions such as payments, approvals, master-data changes, and configuration changes, and when automations and integrations cross boundaries between ERP, finance, CRM, SaaS, and custom APIs. Recent years have seen a fundamental inversion of the identity mix. In earlier enterprise environments, machine-to-human ratios were often much lower; today, non-human identities heavily outnumber humans.
The risk is not that these identities exist; it is what they do. Agents now post general journal entries directly into ERP, AP and finance integrations automatically pass data and approvals between systems, and AI-powered agents are increasingly being used to take action across enterprise workflows. These emerging ‘agentic identities’ introduce additional governance challenges because they may initiate actions dynamically, interact across multiple systems autonomously, and create chains of delegated access that traditional role models were never designed to evaluate. If the governance model can only answer “who has access to what role?” in a handful of systems, but cannot show “what did this identity actually execute, where, and under which controls?”, then a growing share of the real risk surface is invisible by design.
Why HR-Driven JML and Roles Don’t Fit Agents and Service Accounts
Most organizations still try to fold non-human identities into the same joiner–mover–leaver and role models they use for humans. That tends to fail for structural reasons.
HR-anchored JML processes still matter, but as non-human identities have grown to outnumber humans by factors of 40:1, 50:1, or more, that HR-centric model now covers only a slice of the population.
Non-human identities do not follow an HR-driven human lifecycle. They are created during projects, proofs-of-concept, vendor integrations, or automation initiatives—not when HR hires someone. They do not map neatly to business roles, either. They are granted technical permissions such as system admin, full API access, or bypass workflow checks, which do not fit standard business role hierarchies. And they rarely sit cleanly inside an org chart. The right owner may be an application team, a platform team, a business process owner, or even a vendor. Trying to squeeze them only into HR-driven lifecycles and person-centric role models creates blind spots.
Where Incidents Happen: API Chains and Ownerless Identities
As non-human identities have exploded, so have the weak spots around them. Recent analysis argues that in organizations where machine identities vastly outnumber humans, a large share of those identities still lack strong ownership and security posture. Other research shows non-human identities grew 44% year over year, which means the governance problem is compounding faster than most teams can manually absorb.
That shows up in a few recurring patterns:
- API-to-API access chains. Integrations connect ERP, finance, procurement, CRM, and operational systems through APIs. The identities behind those connections can traverse boundaries human users cannot, often with broader privileges and weaker oversight.
- Over-privileged service accounts. Many service accounts were given broad access during implementation because it was expedient. Those permissions tend to become permanent, even when the original business need changes.
- Credential sprawl. API tokens, secrets, certificates, OAuth credentials, and signing keys are often created outside formal governance processes, embedded in scripts or pipelines, and rarely rotated or reviewed consistently
- Ownerless identities. Ask who owns an integration user and the answer is often unclear. The identity may have been created by a vendor, used by an application team, configured by a platform team, and assumed to be covered by security.
- Missing lifecycle and review processes. Non-human identities routinely sit outside access certifications, SoD analysis, and ITGC controls, or they appear only as unexplained rows in a spreadsheet.
- Unmanaged workload identities. Containers, Kubernetes workloads, serverless functions, and CI/CD pipelines increasingly create ephemeral identities that traditional IGA systems were never designed to inventory or govern continuously.
Governance metrics may look strong, but the issues tell a different story. Security findings, audit gaps, and operational incidents can trace back to poorly governed non-human identities, service accounts, integration users, API credentials, and AI agents, operating in and across integrations, APIs, and automation layers.
Why Centralized IGA Struggles With Non-Human Identity at scale
Most organizations respond by trying to do more with their central IGA or access review tools: more connectors, more roles, more workflows. That approach has hard limits.
Central identity teams were never designed to keep pace with this rate of growth. When machine and non-human identities are growing 40–50% year over year and already outnumber humans by wide margins, or even higher in some environments, no amount of extra workflow configuration will close the gap.
Every new SaaS app, API, and automation introduces more non-human identities. Treating each one as a bespoke onboarding project turns central IT into a permanent bottleneck. The result is predictable: the first 20 to 30 systems get onboarded, while the long tail of applications, integrations, and agents never does.
Federated Identity Governance: One Control Plane for Identities and Actions
Once non-human identities outnumber humans by orders of magnitude, the governance problem changes shape. This is no longer about managing thousands of people and their roles in a handful of systems. It is about governing very large populations of identities, potentially hundreds of thousands or more, interacting across ERP, SaaS, custom applications, APIs, and automations.
At that scale, it is not enough to centralize logins and entitlements. Organizations need a control plane that can see three things together:
- the identities involved,
- the high-risk actions they perform,
- and the controls or approvals around those actions.
To do that effectively, they also need continuous verification, policy-based authorization, least-privilege enforcement, and clear identity security posture visibility across both human and non-human populations. Federated identity governance is the operating model built for that reality. Policy intent, guardrails, and visibility stay centralized, while decisions and enforcement are distributed to the domains where those identities actually operate. This does not eliminate central oversight; it separates centralized policy definition and risk visibility from local execution and operational accountability.
This matters because the problem is no longer simply “How do we get every service account into IGA?” The better question is: “How do we govern the combination of identities, high-risk actions, and controls across all critical systems without centralizing every decision?” That is the shift a federated model makes possible.
What a Federated Control Plane Looks Like in Practice
A workable control plane for non-human identity governance has to do more than inventory accounts. It has to connect identities to actions and actions to evidence.
In practice, that means:
- Discovering service accounts, integration users, agents, and bots across ERP, finance systems, SaaS, integration platforms, and directories.
- Normalizing identity, entitlement, transactional, and configuration data into a common model.
- Assigning clear ownership so each non-human identity has a named accountable party. Ownership alone is not enough, however. Effective governance also requires visibility into how identities authenticate, what privileges they hold, what actions they perform, and whether those privileges remain justified over time.
- Applying lifecycle patterns that fit technical identities, such as register, activate, change, and retire. Some workload and cloud-native identities may also be short‑lived, existing only for minutes or hours, which demands continuous discovery and automated governance rather than relying solely on periodic certification.
- Bringing non-human identities into access reviews, SoD analysis, and ITGC processes wherever they can execute high-risk actions.
- Measuring coverage and time-to-coverage across both human and non-human populations.
- Continuously evaluating the organization’s identity security posture, including overly broad privileges, dangerous access combinations, inactive identities, exposed credentials, and gradual policy erosion.
Platforms such as SafePaaS illustrate this broader architectural pattern. For example, SafePaaS uses a connect–transform–govern model to normalize identity, entitlement, transaction, and control data from ERP, SaaS, IDM, and custom applications into a federated governance model. That allows organizations to connect a non-human identity not just to an account record, but to the transactions it executes, the controls it touches, and the evidence auditors expect to see.
What CISOs are realizing
As one industry perspective puts it, CISOs now treat non-human identities as part of the core attack surface, not a niche IAM concern. The issue goes further than authentication; it is operational trust at scale.
Enterprises now depend on APIs, automation, SaaS integrations, CI/CD pipelines, cloud workloads, and AI‑driven processes that act autonomously across systems. In many environments, these non-human identities hold privileged access, sit outside traditional governance processes, and change faster than manual controls can adapt.
Because of this, identity governance is moving away from periodic certification exercises toward continuous visibility, posture assessment, policy-based enforcement, and action‑level accountability across both human and non-human populations.
Where to Start: Map What Agents and Service Accounts Actually Do
The uncomfortable truth is that many organizations still cannot accurately list their non-human identities, let alone explain what each one does. That is why the first step is not another generic access review. It is a focused effort to map how non-human identities execute high-risk actions today in a few critical domains, such as finance, ERP, and the surrounding SaaS and integration platforms.
A practical starting point is simple:
- Pick a narrow but critical scope, such as finance plus adjacent SaaS systems.
- Inventory the service accounts, integration users, agents, and bots in those systems.
- For each identity, document the systems it connects, the transactions or configuration areas it can touch, who believes they own it, and whether it is inside any lifecycle or review process.
- Use that map to identify the highest-risk blind spots and test whether current tools can realistically govern them.
A non-human identity inventory starter sheet is useful because it captures more than names and IDs. It helps tie each technical identity to the systems, workflows, permissions, and high-risk actions that actually matter. From there, a federated governance model has real data to work with.
Once the full picture is visible, the issue becomes clear. The problem is not simply “too many service accounts.” The problem is an architectural and operating-model gap that identity-only tooling cannot close. As non-human identity volumes continue to grow, organizations need governance models that combine centralized policy visibility with distributed enforcement and domain-level accountability. Traditional human-centric IGA alone is unlikely to provide sufficient coverage for modern machine, workload, and agentic identity ecosystems.
