In the era of AI agents, containerized microservices, and robotic process automation, the definition of identity is shifting fast. No longer exclusive to humans, identities today extend to code-based entities such as bots, scripts, service accounts, APIs, and autonomous agents. These non-human identities operate across hybrid environments, cloud platforms, and CI/CD pipelines. They also can have privileged access to sensitive data and infrastructure.
As these machine actors proliferate, so does the complexity of securing them. That is where secrets management comes into focus. Secrets management deals with securely storing and distributing credentials, tokens, keys, and other sensitive artifacts. Organizations are increasingly realizing that managing non-human identities (NHIs) in isolation from secrets management creates fragmentation, inefficiency, and risk. At first glance, NHIs and secrets might feel like different problems as one represents an actor, the other an asset. But under the structure, the lifecycle, ownership, automation, and governance challenges are strikingly similar. Which raises the obvious question: will there be convergence of NHIs and Secrets?
The Case for Convergence
Traditionally, secrets management and identity management have evolved along separate paths. While secrets management emerged from DevOps and cloud security needs, it used to be about vaulting passwords and rotating API keys. Whereas IAM systems were designed for human users, focusing on access policies and entitlements. But NHIs are now blurring these boundaries. Bots and scripts need credentials, certificates, or tokens to perform tasks. Today, both have evolved rapidly and in parallel. And they are starting to look a lot like each other.
Secrets management has matured faster. That is partly because secrets tend to be scoped, versioned, and time-bound by design. You store them in vaults. You rotate them on schedule. You assign ownership and monitor usage.
Now compare that with NHI management. Bots, workloads, scripts, containers require access, but they rarely come with a clean lifecycle. The big questions are: who owns them? who rotates credentials tied to them? what happens when they’re decommissioned? Manual spreadsheets and tracking become challenging and unreliable when dealing with hundreds and thousands of temporary identities. That’s where convergence makes sense as an actual operating model.
Architectural Overlaps
Secrets management and identity management may have started on different paths, but in modern environments, they share overlapping responsibilities when it comes to non-human identities. Secrets tools are optimized for automation such as they issue, revoke, and rotate credentials without human intervention. Identity platforms, on the other hand, bring essential context such as ownership, entitlement mapping, lifecycle workflows, and policy enforcement. Both systems deal with access, governance, and accountability.
The overlap is also architectural. Secrets and NHIs both require discovery, classification, and lifecycle control. But when managed in silos, these overlaps lead to fragmentation and security gaps. Orphaned credentials, expired tokens, or improperly scoped secrets create security blind spots. Instead of merging tools, there is need to build workflows that coordinate across both layers. The goal is a unified operating model where secrets and identities share context, governance, and automation without duplicating effort or losing visibility.
Minimizing Unauthorized Access
Many large enterprises already manage NHIs and secrets across different teams, tools, and taxonomies. Ownership gets lost, access paths multiply, and rotation schedules drift. Non-human identities if unmanaged aggravate this problem by creating persistent, untracked access paths. A unified model translates to designing processes that recognize both secrets and NHIs as lifecycle-bound entities. Discovery, classification, and access enforcement should apply equally to both. This eliminates unauthorized access and ensures that security teams can monitor usage patterns, detect anomalies, and enforce decommissioning when identities are retired. In short, converging these domains can unify visibility across identities and secrets.
Enabling Policy-Driven Security at Scale
With several hundred thousand non-human identities and secrets, security must be automated and policy driven. A converged model can allow secrets issuance to follow the same governance lifecycle as identity. This includes approval workflows, attestation, revocation and rotation. This is particularly crucial for compliance frameworks where demonstrating control over machine access is required. Moreover, this integration aligns with zero trust architectures where access is continuously verified. Secrets become dynamic, auditable tokens governed by policy and environmental context.
Conclusion: Consolidate the Overlaps
NHIs and secrets’ convergence is more about aligning operational realities. Enterprises should start by mapping where secrets management and NHI governance intersect. This involves defining ownership of each asset, understanding how they are created, reviewed, and retired, and which tools duplicate functions like ownership assignment, credential storage, or access control. The next step is to reduce duplication, clarify boundaries, and build connectors.
IAM and secrets may not be the same thing. But in cloud-native environments, they live in the same reality. Treating them as separate problems could be a mistake. Because a vault without identity ill-defined. And an identity without secrets is an idea that does not have access.
