How to Secure Machine Identities Without Passwords

Key Takeaways

Passwordless authentication reduces reliance on long-lived credentials, shrinking the attack surface available to threat actors. OWASP's NHI Top 10 identifies excessive permissions and secret leakage as the most common causes of machine identity exposure.

According to the Palo Alto Networks 2026 Unit 42 Global Incident Response Report, 99% of cloud identities have excessive permissions yet most are severely underutilized, creating a large and largely unmonitored attack surface.

The GitGuardian 2026 State of Secrets Sprawl Report found 28.65 million new hardcoded secrets in public GitHub commits in 2025 alone, a 34% year-over-year increase. Machine credentials are not just persisting — they are proliferating.

AI adoption is accelerating the growth of machine identities, making continuous discovery, risk analysis, and governance essential security practices rather than optional ones.

What is the best way to secure machine identities without passwords?

Organizations secure machine identities without passwords by replacing static credentials with trusted workload identities and continuously governing the permissions those identities accumulate over time.

In practice, that means four things:

• Replace embedded passwords and long-lived secrets with workload identities or other short-lived, federated credentials
• Continuously discover and inventory machine identities across cloud, Kubernetes, CI/CD, and SaaS environments
• Understand permissions, trust relationships, and access paths to identify excessive privilege and reduce blast radius
• Enforce least privilege through automated credential rotation, access reviews, and lifecycle governance

Machine identity security risks and how to address them

The hidden risks of machine identity passwords

A common deployment workflow illustrates why securing machine identities without passwords has become a growing priority. Consider a GitHub Actions service account created initially to automate deployments to a single AWS environment.

Over time, additional integrations expanded its role. The account gained access to production infrastructure, container registries, cloud storage, monitoring tools, SaaS applications, secrets stores, and internal APIs. Three years later, dozens of critical business processes depended on the same machine identity, yet it was still authenticating with the same static credential created on day one.

This pattern is common. According to the GitGuardian 2026 State of Secrets Sprawl Report, researchers found 28.65 million new hardcoded secrets in new public GitHub commits, a 34% increase from 2025. Attackers increasingly target machine credentials because they often provide direct access to infrastructure and data.

The credential is only part of the problem. Over time, a single credential can become a gateway to an expanding web of permissions, systems, and trust relationships. This is why organizations are increasingly looking for ways to rotate machine credentials automatically and eliminate static passwords for machine access. The choice to operate a machine identity without passwords, backed by workload identity federation and short-lived token issuance, is what turns that gateway from a permanent exposure into a time-limited one.

What are the risks of password-based machine authentication?

Human users can change passwords, complete MFA challenges, and respond to suspicious activity alerts. Machines cannot.

Instead, machine identities authenticate through credentials such as API keys, service account credentials, OAuth tokens, certificates, and Kubernetes secrets. When those credentials remain active for months or years, attackers can steal and reuse them, maintain persistent access, and exploit permission growth that increases blast radius.

Returning to the GitHub Actions example, the risk is not simply the long-lived access key. Over time, that single credential became the gateway to production infrastructure, SaaS applications, internal APIs, and other critical resources. If an attacker obtains the key from a source code repository, CI/CD log, or developer workstation, they inherit all of the permissions attached to that machine identity. Because the activity originates from a trusted deployment account, the compromise may appear legitimate.

This challenge is reflected in the OWASP Non-Human Identities Top 10, which identifies secret leakage, excessive permissions, insecure third-party integrations, and weak lifecycle management among the most common machine identity security issues.

What happens when machine identities go unmanaged?

A compromised machine identity can expose far more than a single credential. In the GitHub Actions example, the deployment account gradually accumulated access to production databases, Kubernetes clusters, cloud storage, SaaS applications, and internal APIs. What began as a deployment identity had become a gateway to multiple business-critical systems. MITRE ATT&CK T1078 (Valid Accounts) is the most common initial access technique for exactly this reason: legitimate credentials with too much access require no lateral movement to exploit.

The 2024 BeyondTrust breach illustrated how a trusted credential or third-party integration can provide attackers with legitimate access to interconnected systems. A zero-day vulnerability gave attackers access to an AWS account infrastructure API key, which they then used against BeyondTrust's Remote Support SaaS infrastructure, affecting 17 customers. The Verizon 2026 DBIR reinforces that trend: credential abuse remains one of the most common breach vectors year after year.

AI agents raise the stakes further. If the same GitHub Actions account is later connected to an AI-powered development agent, its permissions remain unchanged but its ability to use them expands dramatically. Because every AI agent operates through identities, permissions, and credentials, the more access a machine identity accumulates, the greater the potential impact if that identity is compromised.

Best practices for securing machine identities without passwords

Leading security teams focus on eliminating static credentials wherever possible while maintaining visibility into the identities, permissions, and trust relationships that enable machine access.

Replace static credentials with workload identities

Returning to the GitHub Actions example, the deployment workflow would not rely on a long-lived access key stored in a CI/CD pipeline. Instead, it would authenticate using passwordless mechanisms such as AWS IAM Roles Anywhere, Azure Entra Workload Identities, Google Cloud Workload Identity Federation, or Kubernetes workload identities. These approaches issue short-lived credentials dynamically and expire them automatically after use. Even if an attacker obtains a credential, the opportunity for misuse is significantly reduced.

Reduce exposure through credential governance

Not every environment can eliminate secrets immediately. When static credentials remain necessary, organizations reduce risk through automated rotation, shorter credential lifetimes, usage monitoring, and removal of inactive identities. The Datadog State of Cloud Security 2024 found that 60% of AWS IAM users and 62% of Google Cloud service accounts have access keys older than one year, making the decision to rotate machine credentials regularly one of the highest-impact controls available. Rotating credentials limits the value of stolen secrets and helps organizations maintain stronger control over service accounts, API keys, and other NHIs.

Understand permissions before attackers do

Authentication alone does not define risk. Security teams must understand which machine identities exist, what permissions they hold, which systems they can access, and how those relationships change over time. This is especially important for OAuth applications, API tokens, and AI agents, which often create hidden trust relationships across cloud, SaaS, and development environments. Intent-based security continuously aligns permissions with each identity's intended purpose by inventorying integrations, monitoring token activity, removing unused applications, and eliminating unnecessary access. According to the IBM Institute for Business Value, surveyed organizations experienced an average of 54 AI agent incidents in 2025 requiring human correction, 37% of which resulted in data exposure or security breaches, underscoring the operational cost of unmanaged AI identities.

The most effective machine identity security programs typically focus on:

• Continuous discovery of NHIs
• Passwordless authentication through workload identities
• Automated rotation to rotate machine credentials on a defined schedule without manual intervention
• Regular permission reviews to right-size access
• Monitoring for unusual machine identity behavior
• Governance of OAuth applications, API tokens, and AI agent permissions
• Lifecycle management from creation through retirement using NHI lifecycle controls
• Blast-radius analysis across cloud, SaaS, development, and AI environments

How Token Security approaches secure machine identities

Securing machine identities without passwords requires more than replacing credentials. It requires continuous visibility and governance.

The challenge

Most organizations cannot secure machine identities effectively because they lack visibility into the full population of NHIs operating across their environments. According to the Cloud Security Alliance Securing Autonomous AI Agents report, only 18% of security leaders are highly confident that their current IAM systems can effectively handle agent identities. Unknown identities, excessive permissions, and unmanaged credentials create blind spots that traditional IAM and PAM programs were not designed to address.

The approach

Token Security applies an intent-based approach to non-human identity security centered on three core capabilities:

• Discover: continuously discover NHIs across cloud, SaaS, development, and AI environments
• Understand: map entitlements, analyze blast radius, establish behavioral baselines, and identify risky access relationships
• Enforce: support automated remediation, lifecycle governance, and ongoing control of machine identity risk

Unlike traditional identity programs built primarily for human users, Token Security is designed specifically for NHIs, machine identities, and AI agents.

The outcome

Organizations gain the visibility to discover machine identity risk, the context to understand excessive permissions, and the control to reduce exposure and govern identities throughout their lifecycle. Token Security continuously discovers machine identities, maps permissions and trust relationships, analyzes blast radius, establishes an expected behavior profile, and helps organizations reduce unnecessary access through automated remediation and lifecycle governance.

How organizations improve non-human identity management

The experiences of security leaders reveal a common theme: visibility is the foundation of effective machine identity governance.

Machine identity visibility must be actionable

A growing number of security teams are finding that machine identity visibility is the prerequisite for effective governance. But not all visibility is equal. For organizations managing thousands of non-human identities, the challenge is not finding risk, it is getting signal that is specific enough to act on without generating so much noise that nothing gets prioritized.

"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 captures the core operational challenge in machine identity security. Discovering that 99% of your cloud identities have excessive permissions is not an actionable finding on its own. Knowing precisely which identities, what they can reach, and what the remediation impact is, without generating thousands of undifferentiated alerts, is what turns visibility into a working control.

How visibility enables safe AI adoption

As AI adoption expands, organizations increasingly need governance that extends beyond traditional service accounts and credentials to include AI agents and their associated identities. HiBob needed visibility into AI agents before it could effectively govern them.

"Token Security gives us visibility we simply didn't have before. We can now automatically identify and control custom GPT agents running in our environment and ensure the required security level. Knowing that no AI agent is operating beyond our oversight means we can confidently accelerate our AI adoption, allowing our business to quickly evolve, which is a game-changer for both our security and growth."
— Tamir Ronen, Global CISO at HiBob

The HiBob situation illustrates how the machine identity problem scales with AI adoption. Custom GPT agents, MCP-connected tools, and AI-powered workflows are all forms of agentic AI operating under non-human identities that most security programs have not yet inventoried. Governing them starts with knowing they exist, and that discovery has to be continuous because new agents appear without waiting for the next security review cycle.

Why identity is the control plane for agentic AI

The GitHub Actions account began as a simple deployment identity. Over time it evolved into a gateway to cloud infrastructure, applications, data, and automated workflows. This scenario is increasingly common across enterprise environments. As organizations expand cloud automation and AI adoption, machine identities accumulate permissions, integrations, and access that can be difficult to track.

Securing machine identities without passwords is only part of the challenge. Organizations must also continuously discover identities, understand permissions and risk, and govern access throughout the identity lifecycle. Identity has become the control plane for modern cloud and AI environments. The organizations that can govern machine identities effectively will be better positioned to support AI adoption, reduce attack surface, and maintain visibility as their environments grow more complex.