September 22, 2026
Posted by Refael Angel
Quick Answer: Machine IAM shifts security from managing credentials as standalone objects to governing the identities of workloads and AI agents directly. That means establishing who or what is requesting access, issuing short-lived authority based on policy, and maintaining ownership and lifecycle context as those identities change. For autonomous agents, that model increasingly extends into runtime, where security teams also need control over how granted authority is used.
For most enterprises, machine identities already outnumber human ones many times over, and AI agents are only accelerating that disparity. Security teams must now govern workloads that appear and disappear quickly, authenticate without a user present, and often operate with access that was designed for a very different model.
On 17 September 2026, Gartner published Innovation Insight: Strengthen Cybersecurity with Machine IAM, a research note for CISOs. Its message is direct: the way enterprises manage machine identities today is credential-centric and fragmented, and attackers are exploiting the gap.
Akeyless is named a Representative Provider in the report. The research provides useful guidance for any security leader responsible for workloads or autonomous agents. It points toward a different control model: govern the machine identity itself, then issue access dynamically based on policy.
Human IAM Creates a Structural Mismatch for Machine Identities
Traditional IAM was built around people. Users sign in interactively, hold relatively stable roles, and have their access reviewed on a predictable schedule. Machine identities do not follow that pattern.
Workloads authenticate programmatically and may exist for only minutes. Their access can change as applications are deployed or infrastructure changes. AI agents add another complication because they can act autonomously with delegated authority.
Gartner found that 47% of organizations still attempt to govern machine identities through broad IAM policies designed for human users. The consequences are already measurable: 58% of surveyed organizations reported cybersecurity incidents involving AI-linked and non-AI machine identities, with business disruption, financial loss, and reputational damage among the outcomes. And while 71% of organizations are increasing IAM spending, many still lack basic capabilities such as machine identity discovery and risk assessment.
Gartner’s prescription is a shift from credential-centric to identity-centric machine IAM.
Gartner
“Cybersecurity leaders should shift from credential-centric to identity-centric machine IAM. This shift replaces static secrets with managed workload identities and ephemeral or just-in-time (JIT) access.”
Gartner®, Innovation Insight: Strengthen Cybersecurity with Machine IAM, Steve Wessels, Erik Wahlstrom, Michael Kelley, 17 September 2026
That changes the control model. Instead of treating a static secret as the identity, security teams authenticate the workload itself and issue short-lived access based on policy. The core controls become the identity, its owner, its purpose, and the conditions under which access should exist.
Machine IAM Moves the Control Point From Credentials to Identity
Gartner’s report frames the change clearly: security teams need to govern the workload itself, not just the credential it happens to use. In an identity-centric model, each machine has its own identity, ownership, purpose, and policy context. Access can then be issued only when needed and removed when that need ends.
A credential can be rotated, replaced, or revoked, but those actions alone do not tell you which workload is using it, why that workload needs access, or whether the access still matches its current purpose. Machine IAM has to preserve that context as infrastructure changes.
This continues a shift Gartner was already describing earlier this year. In April, we wrote about Gartner research spanning Workload Access Management, Multi-Vault Governance, and Workload Identity Management. That research pointed toward fewer long-lived secrets, stronger workload identity, and centralized governance across environments where multiple credential systems already coexist.
→ Read our earlier analysis of Gartner’s workload IAM architecture
Five Directions Gartner Sets Out
Paraphrasing the report’s recommendations:
- Replace static service accounts with managed workload identities and use short-lived, least-privilege, just-in-time credentials wherever possible.
- Maintain a living inventory of machines and AI agents, with a unique identifier, accountable human owner, business purpose, defined permissions, and documented lifecycle.
- Govern AI agents as high-risk workloads, with dedicated registration, a human sponsor, scoped permissions, session limits, approved model versions, and expiring credentials.
- Assess existing platforms before adding another point tool, identifying capability gaps and opportunities to consolidate machine identity governance.
- Centralize machine IAM governance through a cross-functional working group with clearly defined accountability across security, identity, infrastructure, cloud, application development, and business teams.
AI Agents Push Machine IAM Into Runtime
Gartner’s Machine IAM report treats AI agents as high-risk workloads and calls for tighter controls around their identities, permissions, and delegated authority. Additional research on AI agent security by Gartner, SACR, and other industry experts extends that thinking into runtime. SACR’s ARISE framework, in particular, focuses on runtime identity security and enforcement for autonomous agents..
That creates a further requirement for machine IAM beyond the scope of this report. Security teams need to govern who the agent is and what it can access, while also controlling whether the actions it takes remain within the authority it was given.
How Akeyless Maps to Gartner’s Machine IAM Direction
Gartner’s recommendations describe an operating model that combines workload identity, short-lived access, lifecycle governance, and tighter controls for AI agents. The Akeyless Identity Security Platform secures secrets, machine identities, privileged access, certificates, and encryption keys for humans, machines, and AI agents through a single SaaS control plane, with customer-hosted Gateways for private connectivity and Zero-Knowledge operation.
| Gartner Direction | How Akeyless Delivers It |
|---|---|
| Short-lived, JIT credentials instead of static secrets | Dynamic Secrets are minted on demand with minimal permissions and a TTL, then destroyed. Rotated Secrets cover systems that cannot yet go dynamic. Zero Standing Privileges by default. |
| A secretless model for workloads | Universal Identity and native authentication for AWS IAM, Azure, GCP, Kubernetes, OIDC, and SPIFFE let workloads authenticate as themselves instead of presenting a stored secret. |
| A central control plane for revocation | One policy model and one audit trail across clouds, Kubernetes, SaaS, and on-premises. Revoke an identity once and it is enforced everywhere the Gateway brokers access. |
| AI agents governed as high-risk workloads | Akeyless applies identity, scoped access, expiring credentials, and audit controls to AI agents in line with Gartner’s guidance. Agentic Runtime Authority extends those controls into execution by brokering actions through the Gateway, enforcing intent-aware policy, and keeping credentials out of the agent. |
| Platform convergence over point tools | Secrets management, Modern PAM, certificate lifecycle, key management, workforce password management, and AI agent security in one platform and one codebase. |
| Protecting runtime tokens | Credentials are generated inside the customer Gateway and never travel with the workload. Akeyless DFC™ (Distributed Fragments Cryptography) means no whole key or master key exists anywhere, including at Akeyless. |
Why This Matters Now
Gartner makes the stakes clear: AI agents are turning machine IAM from an infrastructure problem into a business risk. In Akeyless’s 2026 State of AI Agent Identity Security report, 84% of respondents said their AI agents can access sensitive data. That means weak identity governance can translate directly into exposure, disruption, and audit risk.
As machine IAM capabilities converge, security teams have less reason to manage workload identity, secrets, certificates, and agent access as separate programs. The priority is to establish one governable model for machine authority before AI adoption pushes fragmentation even further.
Next steps
Gartner clients can read the full Innovation Insight: Strengthen Cybersecurity with Machine IAM on gartner.com.
Akeyless brings workload identity, short-lived access, machine identity governance, and runtime control for autonomous agents together in one Identity Security Platform. If you are moving away from static credentials or preparing AI agents for production, request a demo and we will walk through the Akeyless architecture against your environment.
Frequently Asked Questions
What is machine IAM?
Machine IAM is the practice of managing identity, authentication, authorization, ownership, and lifecycle controls for non-human actors such as applications, workloads, services, devices, and AI agents. Gartner describes machine IAM as covering identities, credentials, authorization policies, attribution, ownership tracking, and granular access controls.
How is machine IAM different from secrets management?
Secrets management protects credentials such as passwords, API keys, certificates, and tokens. Machine IAM is broader: it governs the identity using those credentials, including who owns it, what it is allowed to access, why that access exists, and when it should be changed or retired.
Why are AI agents considered machine identities?
AI agents authenticate and act programmatically rather than requiring a human to perform each action. Gartner includes AI agents within machine IAM and recommends treating autonomous agents as high-risk workloads with defined permissions, accountable ownership, session controls, and expiring credentials.
How does Akeyless support machine IAM?
Akeyless supports machine IAM by combining workload identity, short-lived access, secrets management, privileged access, certificate lifecycle management, and AI agent security in one Identity Security Platform. It also extends machine identity controls into runtime for autonomous agents through Agentic Runtime Authority.
Why are short-lived credentials important for machine identities?
Short-lived credentials reduce how long stolen or exposed access can remain useful. Gartner recommends moving away from static service accounts where possible and using ephemeral or just-in-time credentials as part of an identity-centric machine IAM model.
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