Securing Non-Human Identities: Best Practices for Managing Machine Identities

Read full article from Delinea here: https://delinea.com/blog/best-practices-for-managing-machine-identities/?utm_source=nhimg

Machine identities, also called non-human identities (NHIs)—power modern IT environments. They include computer accounts, service accounts, microservices, containers, IoT devices, and cloud workloads, all of which authenticate, communicate, and share sensitive data across systems.

As technology evolves, the number of machine identities is growing exponentially. Enterprises often have 10 machine identities for every human identity, while small- and medium-sized businesses can have 50 (Microsoft’s State of Multicloud Security Risk Report). This proliferation increases your identity attack surface and attracts attackers who exploit unmanaged credentials.

This guide explains how machine identities work, why they are challenging to secure, and how to implement best practices for managing them effectively.

What Are Machine Identities and How Do They Work?

Machine identities represent a system’s identity, along with the credentials it uses to authenticate to other systems. They fall into two main categories:

1. Workloads: Virtual machines (Windows, Linux, Unix), applications, and containers.
2. Devices: User workstations, mobile devices, OT, and IoT devices.

Machine identities often include service accounts, which allow non-human access to target systems. Credentials for these accounts—such as passwords, tokens, or API keys—are frequently shared across multiple machines or workloads, creating additional management challenges.

Machine identities can also register with identity providers like Active Directory, EntraID, or cloud services, creating trust relationships that enable seamless authentication. For example:

• Kerberos, IWA, GSSAPI, SPNEGO for Windows environments.
• OAuth confidential clients for cloud servers.
• Managed identities for AWS, Azure, or GCP VMs.

Machine Identities vs Human Identities

Unlike human identities, machine identities:

• Don’t require multi-factor authentication (MFA).
• Are often long-lived, shared, and hidden in code or configuration files.
• Lack accountability and clear ownership.

This makes them prime targets for attackers and difficult to monitor. A real-world example: In 2023, high-profile open-source projects (Google, Microsoft, AWS, Red Hat) leaked GitHub authentication tokens via GitHub Actions artifacts, enabling attackers to access private repositories.

Why Machine Identities Are Difficult to Secure

Machine identities are challenging for several reasons:

1. Lack of Validation: Controls like MFA that protect humans don’t apply to machines.
2. Lack of Visibility: Machine identities can be hidden in code or ephemeral workloads, making discovery and tracking difficult.
3. Lack of Governance: Traditional IAM/IGA solutions focus on humans, leaving machine identities unmanaged, often without defined owners or lifecycle processes.
4. Weak Credential Practices: Shared secrets, reused passwords, and unrotated keys increase exposure risk.

Without proper discovery and lifecycle management, organizations struggle to apply least privilege, enforce policies, and maintain accountability.

Best Practices for Managing Machine Identities

Managing machine identities requires a structured, automated approach. Key best practices include:

1. Map and Inventory Identities

• Catalog all machine identities and their provenance.
• Understand which workloads or devices each identity represents.
• Identify dependencies, access levels, and owners.

Tip: Ian Glazer, identity security expert, emphasizes:

“Without a map, affecting controls is impossible. When you have an incident, you don’t know where to start.”

2. Automate Credential Management

• Rotate certificates, tokens, API keys, and secrets regularly.
• Use ephemeral credentials that expire automatically to minimize standing privileges.
• Avoid storing credentials locally or in code; retrieve them from secure vaults at runtime.

Tools: Delinea DevOps Secrets Vault, cloud-native vaults (AWS Secrets Manager, Azure Key Vault).

3. Enforce Least Privilege

• Assign each machine identity only the permissions needed for its task.
• Periodically review entitlements and remove unnecessary privileges.
• For cloud environments, leverage privileged control solutions to manage entitlements dynamically.

4. Federated Authentication

• Adopt federation models (Kerberos, OAuth, PKI) to separate identity from authentication credentials.
• Ensure workloads or services authenticate securely without sharing static secrets.

5. Discovery and Continuous Monitoring

• Use automated tools to discover machine identities in Active Directory, local accounts, cloud services, and ephemeral workloads.
• Monitor activity to detect misuse, excessive privileges, or anomalous behavior.
• Implement early-warning mechanisms, such as Honeytokens, to detect attackers probing for credentials.

6. Lifecycle Governance

• Implement self-service workflows for onboarding and offboarding machine identities.
• Capture ownership, dependencies, and entitlements for each identity.
• Automate rotation, expiration, and revocation using Identity Lifecycle Management solutions.

7. Integrate Human and Machine Identity Management

• Manage both human and machine identities on a single, unified platform.
• Apply consistent policies, audit controls, and risk assessments across all identities.
• Avoid disjointed solutions that complicate reporting and governance.

Getting Started

• Begin with discovery: identify unmanaged service accounts, expired credentials, and shared secrets.
• Map all machine identities and their privileges.
• Implement automated rotation and lifecycle governance.
• Monitor continuously for anomalous activity or policy violations.

Free Tools: GitGuardian Service Account Discovery Tool, Delinea Secret Server, FastPath, and other ITDR platforms.

Conclusion

Machine identities are a growing portion of your attack surface. Without proper management, they pose a serious security risk.

Best practices include:

• Mapping identities and ownership.
• Automating credential rotation.
• Enforcing least privilege.
• Adopting federated authentication.
• Continuous discovery and monitoring.
• Integrating human and machine identity governance.

By taking these steps, organizations can reduce risk, strengthen security posture, and prepare for a world where machine identities outnumber humans.