What's the Best Way to Manage Machine Identity in Kubernetes Environments?

Key Takeaways

• Machine identity Kubernetes security depends on continuously discovering non-human identities, understanding their permissions, and governing them throughout their lifecycle, not on a one-time authentication upgrade.
• Excessive permissions, long-lived credentials, and unmanaged service accounts dramatically increase attack paths inside Kubernetes clusters. Attackers who compromise a workload inherit its machine identity rather than stealing new credentials.
• According to Spectro Cloud's 2025 State of Production Kubernetes report, 90% of IT and cloud engineering teams expect the number of AI and machine learning workloads on their Kubernetes clusters to grow in the coming 12 months, expanding the machine-to-machine access surface further.
• AI agents, CI/CD pipelines, and cloud-native workloads all operate through NHIs, making kubernetes machine identity management a foundation of modern cloud security rather than a niche IAM concern.

Why does machine identity kubernetes security matter more than container security alone?

Kubernetes security conversations still center on containers: image scanning, runtime protection, network policy. But every one of those containers authenticates to something else, a service account, a workload identity, an OAuth token, using a non-human identity that quietly accumulates permissions the container itself never asked for. Machine identity kubernetes security means recognizing that the identity, not the container, is what an attacker actually inherits when a workload is compromised.

How do you manage machine identities in Kubernetes?

The best way to manage machine identity in Kubernetes environments is to replace static credentials with workload identities wherever possible, continuously discover every NHI, enforce least privilege, monitor behavior over time, and govern identities throughout their lifecycle.

This means security teams should:

• Eliminate long-lived secrets whenever supported by cloud-native identity providers
• Continuously inventory Kubernetes service accounts, workload identities, OAuth tokens, and cloud roles, the population that kubernetes machine identity management programs must track
• Regularly review permissions to remove unnecessary access before it becomes an attack path
• Monitor machine behavior to detect compromised identities that appear legitimate because every request uses valid, authorized credentials
• Govern identities from creation through retirement instead of treating them as static infrastructure

Machine identity Kubernetes risks at a glance

Why do machine identities create security risks in Kubernetes?

A retail company runs its customer-facing website on Kubernetes. One microservice processes product images uploaded by the marketing team. Initially, the container only needs permission to read and write files in cloud storage. As new features are added, it gains access to a Secrets Manager, customer metadata, an AI image-tagging service, and a message queue. Each integration adds another permission. Individually, these changes seem routine. Together, they create a machine identity with trusted access across multiple systems that is also a risk.

When attackers exploit a vulnerability in a Kubernetes container, they inherit the workload's machine identity instead of stealing employee credentials. Using its existing permissions, they can access connected systems without triggering suspicious authentication events because every request appears legitimate. MITRE ATT&CK T1078 (Valid Accounts) is the technique that describes exactly this pattern, and it has been among the most common initial access and persistence techniques for years precisely because it requires no exploit after the initial foothold.

In our example, the workload never became risky because of the container itself. It became risky because its machine identity quietly accumulated permissions across trusted systems. As Kubernetes adoption grows, the challenge shifts from managing containers to governing the non-human identities that enable them. According to the CNCF Annual Cloud Native Survey, 82% of container users now run Kubernetes in production, with another 13% piloting or evaluating it, driving rapid growth in service accounts, workload identities, and other machine identities. Maintaining visibility into those identities, their permissions, and trust relationships becomes increasingly difficult as environments scale.

Why Kubernetes workload identity is essential for cloud security

Every Kubernetes workload authenticates using an NHI, such as a service account, workload identity, OAuth token, or cloud Identity and Access Management (IAM) role. As applications evolve, those identities quietly accumulate permissions. Our image-processing service began with access to cloud storage but eventually gained permissions to retrieve secrets, call AI services, and access customer data, expanding its blast radius with every integration.

The OWASP Non-Human Identities Top 10 identifies excessive permissions (NHI4), secret leakage (NHI2), vulnerable third-party identities (NHI9), and poor lifecycle management (NHI1) as common machine identity weaknesses. These risks are especially difficult to detect in Kubernetes because workloads and their identities change constantly.

Traditional identity governance was designed for employees logging into applications. Kubernetes introduces thousands of NHIs that authenticate automatically. Without continuous visibility into those identities, their permissions, and their trust relationships, security teams quickly lose track of what can access critical infrastructure.

What happens when organizations lose track of machine identities?

As Kubernetes environments grow, NHIs accumulate permissions across cloud platforms, secrets managers, internal APIs, and third-party services. Palo Alto Networks reports 99% of cloud identities have excessive permissions, yet most of these are severely underutilized, creating a massive, vulnerable attack surface. Those permissions often remain long after they are needed because removing access could disrupt production.

Our image-processing service shows why this matters. When attackers exploit a vulnerability in the container, they inherit the workload's trusted machine identity rather than stealing employee credentials. Every API request appears legitimate because the workload is performing actions it has always been authorized to perform.

This pattern extends beyond Kubernetes. In 2025, the Salesloft Drift incident demonstrated how compromised OAuth applications gave attackers access to connected customer environments through existing trust relationships. According to a FINRA cybersecurity alert, attackers stole OAuth tokens and used them to access Salesforce, Google Workspace, Slack, and cloud credentials across more than 700 organizations. As organizations deploy more AI agents, governing NHIs becomes even more important for limiting blast radius and preventing attackers from abusing trusted machine-to-machine access.

Best practices for Kubernetes machine identity management

Strong Kubernetes security starts with effective machine identity management, including these best practices.

Continuously discover non-human identities

Maintain an up-to-date inventory of Kubernetes service accounts, workload identities, OAuth tokens, API keys, and the cloud, SaaS, AI, and CI/CD services they can access. According to Vanta, 8 in 10 companies are already betting on AI agents, but fewer than half have a policy to govern them, which means most agent-related NHIs are being created and used with no formal discovery or oversight process in place.

Eliminate unnecessary access

Replace long-lived credentials where possible and regularly review Kubernetes RBAC permissions, cloud IAM roles, API permissions, and OAuth grants. According to the Cloud Security Alliance's Enterprise AI Security Starts with AI Agents survey, only 8% of organizations reported that AI agents never exceed their intended permissions, meaning scope violations are the norm rather than the exception.

Understand blast radius

Map permissions and trust relationships to uncover hidden access paths and prioritize remediation based on business impact. A workload with narrow permissions but a hidden trust relationship to a highly privileged service can carry as much risk as one with broad direct access.

Govern identities throughout their lifecycle

Review permissions regularly, remove unused identities, rotate credentials, revoke unnecessary access, and retire obsolete identities. According to the Cloud Security Alliance's State of Non-Human Identity Security report, only 20% of organizations maintain formal processes for offboarding or rotating API keys and machine secrets, which is why so many long-lived credentials remain exploitable long after they should have been revoked.

How Token Security approaches Kubernetes machine identity security

Kubernetes environments create thousands of NHIs that accumulate permissions across cloud infrastructure, applications, AI services, and development pipelines. Managing that complexity requires more than periodic inventories. Intent-based security provides continuous visibility into which identities exist, what they can access, and whether their permissions align with their intended purpose.

The challenge

Traditional identity tools were built for human users. They were not designed to continuously discover machine identities, map their trust relationships, or identify excessive permissions across dynamic Kubernetes environments where workloads and their identities change constantly.

The approach

Token Security takes an intent-based approach to non-human identity security by continuously discovering machine identities across Kubernetes and connected cloud environments, understanding risk through entitlement mapping, blast-radius analysis, and behavioral baselines, and enforcing governance through automated remediation and lifecycle controls. This helps organizations identify unnecessary access before it becomes an attack path.

The outcome

Organizations gain continuous visibility into machine identities, reduce excessive permissions, uncover hidden trust relationships, and maintain governance as Kubernetes environments evolve.

How organizations solve Kubernetes machine identity challenges

As Kubernetes adoption accelerates, security leaders are finding that visibility into machine identities is essential for reducing risk and maintaining operational control.

Discovering hidden machine identity risk at Lemonade

Insurance provider Lemonade needed greater visibility into machine identity risk across its engineering environment. In Kubernetes clusters especially, the challenge is rarely a lack of alerts. It is knowing which of thousands of routine permission grants actually represent meaningful risk worth acting on.

"Token Security has enhanced our security by providing us accurate and relevant visibility into machine identity risks, something I have yet to see from other vendors. It's the first service I've seen that delivers on the widely made claim of finding machine identity risk. It provides us with the right amount of detail to mitigate issues without burdening us with tens of rows of useless alerts." — Jonathan Jaffe, CISO at Lemonade

This experience reinforces a common challenge in Kubernetes environments. Security teams rarely struggle to collect more alerts. They struggle to identify which machine identities actually present meaningful risk and deserve immediate attention, the exact gap between OWASP NHI4 (excessive permissions) as a category and excessive permissions as a specific, remediable finding.

Reducing operational overhead at GEHA

Healthcare organization GEHA wanted to improve visibility into secrets, API keys, and other NHIs while reducing manual effort. In a Kubernetes environment where service accounts and secrets are created continuously by automated pipelines, manual tracking cannot keep pace with the rate of creation.

"We've been using Token Security for the past few months, and it has significantly streamlined how we manage and secure our secrets, API keys, and other NHIs. The platform's visibility and discovery capabilities have helped us detect new and hidden risks while reducing operational overhead." — Eric Ullmann, Director of Information Security at GEHA

The same principles apply to Kubernetes. Continuous discovery helps uncover machine identities that would otherwise remain hidden, allowing security teams to reduce exposure before attackers can exploit trusted machine-to-machine access relationships.

What machine-first security actually requires

Kubernetes has transformed both application deployment and identity. According to the Cloud Native Computing Foundation (CNCF), 82% of container users are now running Kubernetes in production environments. Protecting Kubernetes is no longer just about securing containers. It requires continuously discovering machine identities, understanding their permissions and blast radius, and governing them throughout their lifecycle.

Our image-processing service illustrates why. The container itself was never the greatest risk. Its machine identity quietly accumulated trusted access across multiple systems. As Kubernetes and AI continue to converge, identity has become the control plane. Organizations that govern NHIs effectively will be better positioned to reduce risk across both.