Why Gartner is Right About Machine IAM, and What Security Leaders Can Do About It

By now, most security teams have come to grips with a key truth: machine identities outnumber human ones by orders of magnitude. In fact, in many enterprises, they already make up more than 90% of all identities accessing systems and data. But even if we’ve accepted that reality, most of us are still catching up when it comes to managing it.

Gartner recently weighed in with a pointed assessment in theirreport, Gartner® Report: The Software Supply Chain Security Playbook. The headline? Most organizations are dangerously underprepared. Governance is fragmented, secrets are poorly managed, and visibility and automation are sorely lacking. Gartner’s Leaders’ Guide to Modern Machine IAM expands on this diagnosis, outlining the foundational capabilities needed to close the gap.

If you’re in InfoSec and that feels familiar, you’re not alone.

The Machine IAM Mess: It’s Not Just You

One of the most valuable contributions of the Gartner report is simply naming the problem: most teams don’t even have a clear definition of what “machine IAM” is supposed to cover.

Is it about certificate management? Secrets rotation? Workload identity? Privileged access for AI agents, bots and scripts?

Yes, yes, yes, and yes.

This confusion often leads to ad hoc solutions—Vault here, homegrown scripts there, some manual cert handling tucked in a corner. Each team does what it can. And governance becomes a patchwork of well-intentioned efforts with no cohesive strategy.

Gartner calls this out directly: without centralized policies and tooling, organizations fall into inconsistent enforcement, delayed provisioning, and a larger attack surface.

Moving Beyond Stopgap Solutions

This isn’t just a matter of cleaning house. Machine identities are becoming more critical, not less. Microservices, ephemeral infrastructure, and AI agents are changing the nature of access. Secrets and credentials are created and destroyed in seconds. Static credentials aren’t just dangerous—they’re obsolete.

So how do we move forward?

Gartner’s vision is pretty clear:

• Treat machine IAM as a first-class security domain
• Centralize control
• Automate wherever possible
• Replace static credentials with ephemeral, tightly scoped alternatives
• Monitor continuously, not just periodically

This isn’t just about operational efficiency—it’s about keeping pace with the speed and scale of modern systems.

Akeyless: An Architecture Built for the Challenges of Machine IAM

This is where Akeyless comes in—not just as a tool, but as a response to the architectural challenge Gartner describes.

Rather than patching over IAM with a dozen plugins and scripts, Akeyless offers a platform-level rethink. It’s a SaaS-based Secrets and Machine Identity Management solution designed specifically for multi-cloud, dynamic environments.

What stands out is its unified approach:

• Secrets are injected at runtime via ephemeral credentials—no static values to leak
• Secretless machine authentication uses built-in identity providers (like AWS IAM, Kubernetes, Azure AD)
• Certificate lifecycle management is fully automated
• And all of this is governed from a single control plane, with policy, logging, and audit trails centralized across all environments

This isn’t a bolt-on—it’s a base layer.

What This Looks Like in Practice

Let’s say you’re spinning up short-lived Kubernetes workloads across cloud environments. Traditionally, you’d need some ugly combination of init containers, hardcoded secrets, and overly-permissive service accounts to get them talking to each other.

With Akeyless, you use dynamic secrets and identity-based authentication. Credentials don’t exist until they’re needed. They expire within minutes. Access policies are enforced through RBAC and scoped to exactly what the workload requires.

Meanwhile, every access is logged and visible in your SIEM of choice via webhook or log forwarding.

This is what Gartner is advocating for when they talk about “least privilege,” “automation,” and “centralized governance.” It’s not just about control—it’s about control that’s practical and scalable.

What About AI Agents?

Gartner’s report closes with a nod to the future: as AI agents increasingly perform autonomous tasks, they too will need identity management. That’s not a future problem. It’s a now problem.

Whether it’s an LLM-based assistant triggering workflows or an ML job accessing sensitive training data, those agents need authentication, authorization, and secrets just like any other workload.

The same tools and principles apply. And with Akeyless, secretless AI agent workflows and management are already built in.

My final thoughts on this – Security leaders are used to chasing best practices after the fact. Machine IAM offers a rare opportunity to do it right from the start—because it’s still being defined. Check out Gartner’s Innovation Insight on Machine IAM. Ask vendors, who claim to offer solutions to this problem, on how many of the capabilities listed in the innovation insight can they truly deliver. You will find that a majority of them are more observability providers or those with limited functionality to comprehensively manage the full lifecycle of NHIs and machine identities. 

Gartner has put a spotlight on the problem. The question is whether we’re going to meet it with the same old fragmented solutions—or finally implement the centralized, automated, and secure foundation we actually need.

Akeyless was built for this moment.

Curious how Akeyless maps to your machine IAM challenges? Connect with a solutions expert.