April 27, 2026
Posted by Refael Angel
In a span of twelve days this April, Gartner® published two research notes that together redraw the map of the secrets management category. The first, Innovation Insights: Secrets Management, named Akeyless in two distinct vendor lists. The second, a Reference Architecture Brief on IAM for AI Agents and Other Workloads, named Akeyless as an example technology for Workload Identity Management.
That is three category placements, in two reports, in less than two weeks.
We do not take this lightly. Gartner research is independent and prescriptive. Being named once is a signal. Being named in three distinct categories is a thesis.
What Gartner Actually Said
In Innovation Insights: Secrets Management, Gartner advises cybersecurity leaders to “avoid using static credentials” and to “evaluate alternative mechanisms that keep secrets away from workloads entirely.” Gartner names a small group of vendors driving this shift in the Workload Access Management category. Akeyless is on that list.
In the same report, Gartner identifies a separate group of vendors offering “governance across vaults through orchestration or sync capabilities” — a capability Gartner says organizations need because “most organizations need to establish processes and tooling to manage multiple secret management solutions.” Akeyless is on that list too. We are one of the only vendors named in both lists in this report.
Twelve days later, in the Reference Architecture Brief: IAM for AI Agents and Other Workloads, Gartner introduces a category called Workload Identity Management — tools that “register workload identities, as well as discover, inventory, monitor, manage, and administer workloads.” Gartner lists example technologies. Akeyless is among them.
Why Three Placements Matter More Than Three Times One
Each of these categories solves a different problem. Workload Access Management is about how a workload — an application, a container, an AI agent — gets the credential it needs at runtime. Multi-Vault Governance is about controlling secrets that already live in AWS, Azure, GCP, HashiCorp Vault, and Kubernetes without forcing a rip-and-replace migration. Workload Identity Management is about discovering and governing the identities of AI agents and other non-human workloads at the inventory level.
Most vendors do one of these. Some do two. We were built to do all three on a single platform, with a single audit trail, governed by a single policy engine, on top of patented Distributed Fragments Cryptography™ that means we cannot see the data we protect.
The Shift Gartner Is Describing
Gartner
“Rotating legacy symmetric strings across domains at enterprise scale is not operationally viable. Furthermore, every new static symmetric string, such as an API key, represents a failure of the IAM program and tooling.”
Gartner®, Reference Architecture Brief: IAM for AI Agents and Other Workloads, Erik Wahlstrom, 20 April 2026
This is the strongest statement we have seen from Gartner on the inadequacy of the legacy vault model. The implication is direct: the future of this market is not bigger vaults. It is fewer secrets, more identities, ephemeral credentials, and centralized governance over the multi-vault reality that already exists in every enterprise we talk to.
That is exactly the architecture Akeyless ships.
What This Means for You
If you are a CISO or security architect: the question of whether to consolidate on a single secrets vault is the wrong question. The right question is whether you have a Workload Identity Provider, a governance plane over the vaults you already run, and a path to eliminating long-lived API keys for your AI agents. Gartner’s two notes, read together, are an unusually clear endorsement of that architectural shift.
If you are an IAM leader: the Reference Architecture Brief is worth reading in full. It introduces the CeDeSec pattern, centralized governance, decentralized enforcement, and positions Workload Identity Management alongside Workload Access Management and the Authorization Management Platform as the three runtime controls. Akeyless is built for this model. The Akeyless Gateway is the decentralized enforcement point. The Akeyless SaaS is the centralized governance plane. The Customer Fragment, generated and held only inside your environment, is what makes that split safe.
If you are running AI agents in production: stop hardcoding API keys. Akeyless authenticates agents using their inherent cloud or Kubernetes identity, then issues a Just-in-Time, ephemeral credential that is automatically revoked when the task completes. There is no secret to leak, no key to rotate, no token to harvest.
Read Gartner’s Research, Apply It to Your Environment
Talk to your Gartner advisor to access:
- Innovation Insights: Secrets Management (G00847589, 8 April 2026)
- Reference Architecture Brief: IAM for AI Agents and Other Workloads (G00850819, 20 April 2026)
Book a 30-minute architecture review with an Akeyless solutions engineer to map your current secrets footprint to the Gartner reference architecture.
FAQs
What is Workload IAM and how is it different from secrets management?
Secrets management focuses on storing and rotating credentials like API keys, passwords, and certificates. Workload IAM shifts the model entirely. Instead of managing secrets, it assigns identities to workloads (applications, services, and AI agents) and issues short-lived, policy-bound access at runtime. The goal is not better secrets. It’s fewer secrets.
Why does Gartner recommend managing multiple secrets managers instead of consolidating to one?
Most organizations already run multiple vaults across cloud providers, platforms, and teams. Forcing consolidation often creates friction, migration risk, and shadow usage. Gartner’s guidance reflects reality: the problem isn’t too many vaults, it’s the lack of governance across them. That’s why centralized policy and visibility across distributed environments matter more than standardizing on a single tool.
What does Workload Identity Management (WIM) actually include?
WIM goes beyond authentication. It includes discovering workloads, inventorying identities, mapping credentials and access paths, and enforcing governance across environments. In practice, that means knowing which services and agents exist, what they can access, and whether that access is appropriate, continuously, not just at deployment time.
How is this different from traditional vault or PAM solutions?
Vaults and PAM systems were built around managing credentials: storing them securely, rotating them, and controlling access. Workload IAM assumes that model breaks down at scale, especially with cloud-native systems and AI agents. Instead of distributing credentials, it issues them dynamically based on identity and policy, then removes them when no longer needed. The control point moves from storage to runtime.
Do AI agents require a different identity and access model?
Yes. AI agents operate autonomously, interact across multiple systems, and often generate or consume credentials dynamically. Static API keys and long-lived tokens don’t hold up in that model. Each agent needs a verifiable identity, short-lived access, and clear policy boundaries. Without that, access becomes difficult to track, govern, and revoke in real time.
