Why Machine Identities Are the Next Big Compliance Challenge

Key Takeaways:

• Machine identities are digital credentials that allow non-human systems to authenticate and interact securely. As their numbers grow, many go unmanaged, creating hidden vulnerabilities that can be exploited if not properly tracked and governed.
• Traditional identity governance tools were built for human users, not machines. Static roles, manual approvals, and periodic reviews cannot keep pace with the speed and scale of automated systems.
• Compliance frameworks now treat machine identities like human ones. Organizations must monitor machine actions, enforce least-privilege access, and automate governance to meet accountability standards.

 

Machine identities such as service accounts, bots, APIs, and IoT devices now outnumber human users in enterprise systems. Unlike people, machines do not retire or log off. They can be created instantly, run continuously, and remain active long after they are no longer needed. These traits make them easy to overlook but risky to ignore.

Security incidents such as the 2023 CircleCI breach have shown how compromised machine credentials embedded in automation pipelines can lead to widespread exposure. Yet many organizations still underestimate their importance and, in some cases, fail to track them altogether.

As automation and cloud systems scale, machine identities have multiplied across networks and applications. Each one represents a potential access point that must be verified, monitored, and retired responsibly. From forgotten API keys to unmonitored service accounts, many organizations still lack the visibility and controls needed to govern non-human access effectively.

This article explores what machine identities are, why they matter, and how governing them with the same rigor as human identities has become essential for cybersecurity and compliance.

What Is a Machine Identity?

A machine identity is a digital credential that allows a non-human system to identify itself and interact securely with other systems. These credentials function much like user logins, confirming that a machine is authorized to access data, perform tasks, or exchange information.

Common examples include:

• Service accounts that enable software to communicate with other software
• API tokens that connect backend services
• TLS certificates that verify server identities
• Credentials used by containers, microservices, or IoT devices
• Automation bots and chat assistants, which also rely on machine identities to operate

Unlike human accounts, which are manually created and reviewed, machine identities are often generated automatically and operate continuously. A single application might use hundreds of credentials that authenticate thousands of times per day. Databases, servers, and IoT devices communicate constantly—checking in, exchanging data, and triggering automated processes in the background.

This activity is expanding rapidly as organizations adopt more connected systems and automated workflows. Research from CyberArk shows that machine identities now outnumber human ones by at least 45 to 1 in large organizations, and the gap continues to grow.

Why Traditional Identity Governance Doesn’t Work for Machines

Understanding how machine identities differ from human accounts helps explain why traditional identity systems struggle to manage them effectively.

Most organizations still rely on identity governance tools designed for people. Traditional identity and access management (IAM) systems assign roles, enforce logins, and monitor activity based on departments or job functions. These methods work well for employees but fail to scale to the volume and speed of machine-generated credentials.

Machine identities are created automatically through scripts, deployment tools, or services. They can appear and disappear within seconds or remain active for months as part of long-running processes or integrations. This variability makes them difficult to manage with systems designed for predictable, human-driven workflows.

Even when machine credentials are recorded within IAM platforms, oversight often falls behind. Manual approvals, static role assignments, and periodic reviews cannot match the pace of automated environments. Many credentials are also embedded directly into configuration files or pipelines without documentation, leaving security teams with limited visibility or control.

As automation expands, the number of machine-to-machine interactions quickly exceeds what traditional IAM tools can handle. Managing these identities effectively requires governance frameworks built around continuous discovery, automated lifecycle management, and clearly defined ownership for every credential.

The Compliance Risks of Unmanaged Machine Identities

When machine identities are not properly governed, the issue extends beyond security and becomes a compliance challenge. Credentials that operate without oversight can violate data protection standards, create audit gaps, and expose organizations to regulatory penalties.

These problems often start quietly. A machine identity may remain active after a project ends, or an API key may be reused without review. Over time, these small oversights accumulate, turning ordinary system processes into compliance liabilities.

Below are some of the most common ways unmanaged machine identities have led to real-world incidents and regulatory failures:

1. Expired Certificates and Service Disruptions

Certificates confirm system identities and secure communication between machines. This includes SSL and TLS certificates for websites, or authentication tokens that allow devices to connect across environments. These credentials expire on a set schedule, and if not renewed, they can disrupt critical operations.

In May 2023, Cisco’s SD-WAN hardware experienced major disruptions after expired certificates prevented devices from communicating. The outage affected more than 20,000 enterprise customers. Beyond operational losses, such downtime can raise compliance issues if it breaches service-level agreements or interrupts regulated services.

2. Hardcoded API Keys in Source Code

A common security mistake is embedding credentials directly into code or configuration files. These “secrets” often include API keys, SSH keys, or database credentials, all of which act as machine identities. Once exposed, these keys can be exploited without detection.

Researchers recently found nearly 1,500 active Mailchimp API keys embedded in public code repositories. These could have allowed unauthorized access to customer data, creating potential violations under privacy regulations such as GDPR and CCPA. When machine credentials are not properly secured, the risk extends beyond security to legal compliance.

3. Zombie Credentials That Never Expire

Zombie credentials are machine identities that remain active long after their purpose has ended. They often originate from test environments, decommissioned systems, or accounts tied to employees who no longer work at the organization. Because they are rarely reviewed or rotated, they become easy targets for attackers.

In June 2025, The Hacker News reported  that unmonitored Microsoft 365 service accounts were used in multiple breaches. Attackers leveraged these forgotten credentials to move laterally across networks and gain persistent access to sensitive systems. Each incident underscored how unmanaged machine identities create both security gaps and compliance failures.

4. Over-Privileged Machine Accounts

To simplify deployments, teams often grant machine identities broad permissions that are never reduced later. These excessive privileges increase the potential impact of any compromise.

Toyota’s 2022 data breach illustrates this risk. A subcontractor’s exposed server contained a private key that provided access to internal systems. The associated machine identity had more permissions than required, leading to the exposure of personal data from more than 296,000 users. Regulators cited the incident as a failure of access control—highlighting how over-privileged machine accounts can quickly become compliance liabilities.

5. Lack of Auditability and Accountability

Unlike human users, many machine identities lack assigned ownership or traceability. They are often generated automatically and never tracked, making it difficult to determine which system performed a given action.

Without proper logging, organizations struggle to meet compliance standards. Regulations such as GDPR, HIPAA, SOX, and PCI DSS require detailed records of who accessed what and when. If a machine identity initiates a security event and there is no record of its activity, it becomes nearly impossible to investigate or demonstrate control to auditors.

What Compliance Frameworks Now Require for Machine Identities

The risks posed by unmanaged machine identities are not only operational but also regulatory. Modern compliance standards now treat machine access with the same level of scrutiny as human access. Organizations must demonstrate that every credential—whether used by an employee, contractor, or automated system—is properly secured, monitored, and governed.

Compliance today goes beyond ticking boxes. It focuses on proving that both people and machines act responsibly under clear safeguards. Overlooking machine identities within governance frameworks creates blind spots that can lead to penalties, service disruptions, and lasting reputational harm.

Below are three key expectations regulators now place on organizations, and why machine identities must be included in compliance programs.

1. Complete Visibility Into Data Access

Regulators expect organizations to know exactly who or what accessed sensitive information. That includes employees, third-party vendors, and automated systems such as APIs, bots, and service accounts.

The SolarWinds breach is a clear example. Attackers used compromised machine credentials to move across systems undetected. The lack of visibility into machine activity created a blind spot—one that frameworks like GDPR and HIPAA now explicitly require companies to address.

2. Defined Permissions and Least-Privilege Access

The principle of least privilege applies equally to human and machine identities. Each should have only the access necessary to perform its function. In practice, however, machine accounts are often created with excessive permissions and rarely reviewed. Over time, bots, containers, and scripts accumulate privileges that increase the risk of misuse or noncompliance.

3. Auditable Logs for Every Machine Action

If a system action cannot be traced, it cannot be trusted. Privacy and security regulations worldwide require detailed audit trails showing what happened, when it happened, and who—or what—initiated the event.

When an IoT device sends a medical alert or an API processes a financial transaction, every action must be logged and verifiable. Yet many organizations still lack centralized logging for machine identities, leaving critical activities unrecorded. These gaps not only weaken security but also make it difficult to demonstrate compliance during audits.

Best Practices for Managing and Governing Machine Identities

Once compliance expectations are defined, the next step is implementing the right controls to manage machine identities at scale. Effective governance depends on structure, automation, and continuous visibility across every system that uses non-human credentials.

Strong oversight and consistent processes are essential to maintain both security and compliance. As organizations grow, the number of machine credentials expands rapidly across cloud environments, APIs, and automation pipelines. Managing this complexity requires frameworks that standardize how identities are created, used, rotated, and retired in real time.

Below are four core pillars of machine identity governance that help organizations maintain control, reduce risk, and meet regulatory requirements:

1. Maintain a Complete Inventory

Visibility is the foundation of machine identity governance. Organizations must be able to discover and catalog every identity in use, including service accounts, API keys, certificates, bots, containers, and any other non-human actors with access privileges. Without an accurate inventory, it becomes impossible to assess risk, rotate credentials, or apply consistent security policies.

2. Automate Credential Lifecycle Management

Automation is critical to keeping pace with the volume and speed of machine credential use. Tokens, certificates, and API keys should be issued, rotated, and revoked automatically. Built-in expiration policies and time-to-live settings help ensure that credentials do not persist beyond their intended purpose, minimizing the risk of forgotten or orphaned identities.

3. Enforce Access Controls and Least Privilege

Machine access should follow the same security principles applied to people. Role-based (RBAC) or attribute-based (ABAC) access controls can limit machine permissions to only what is required for a specific task. These permissions should be reviewed periodically and updated as workflows change to prevent excessive privileges from accumulating over time.

4. Centralize Logging and Auditing

Every action performed by a machine identity should be recorded and traceable. Detailed logs showing what occurred, when it happened, and which identity initiated it are essential for compliance, monitoring, and incident response. Centralized audit logs not only demonstrate accountability to regulators but also help identify anomalies or unauthorized activity more efficiently.

Applying Decentralized Identity to Machine Identity Management

The practices outlined above—inventory, automation, access control, and logging—form the foundation of secure machine identity management. However, as systems become more distributed and machines interact across multiple networks and organizations, even well-managed credentials can reach their limits.

Machine identities now need to operate seamlessly across platforms and partners, without depending on centralized authorities or static secrets that can be stolen or expire. This is where decentralized identity provides a critical next layer.

Decentralized identity gives machines unique, verifiable, and self-managed identifiers that can be trusted across systems. Just as decentralized identity enables people to prove who they are without oversharing, it allows machines to authenticate securely, confirm authorization, and exchange data without exposing sensitive information.

Below are three key technologies making this possible:

1. Verifiable Credentials for Machine Compliance and Authorization

Verifiable credentials are digitally signed claims that confirm specific attributes or permissions. For machines, they can verify compliance, operational readiness, or completion of a security audit. These credentials can be checked instantly, without contacting a central authority.

This model is already being tested in supply chains. U.S. Customs and Border Protection, for example, has piloted verifiable credentials and decentralized identifiers to create digital twins of goods such as oil and steel. These credentials verify an item’s origin, inspection status, and chain of custody—without exposing unnecessary data to every participant.

A similar approach applies to machines. A vehicle’s onboard system, for instance, could present a verifiable credential at a customs checkpoint to prove it passed inspection and is authorized to transport goods. This method strengthens both privacy and efficiency by reducing manual checks and database queries.

2. Decentralized Identifiers for Persistent Machine Identity

Decentralized identifiers  (DIDs) are globally unique, cryptographically verifiable identifiers that operate without a central authority. They give machines a persistent, self-managed identity that works across automated and peer-based systems.

With a DID, a machine can prove its identity without relying on usernames, passwords, or static API keys. This is especially important in cloud environments, where machines interact constantly and at high speed. A service, agent, or API can use a DID to verify that it belongs to an organization and has permission to perform specific tasks.

By eliminating embedded credentials, DIDs make machine interactions traceable, auditable, and less vulnerable to misuse. Identity becomes part of the system’s logic, improving long-term security and reducing reliance on manual oversight.

3. Cryptographic Proofs for Secure, Data-Minimized Trust

Cryptographic proofs, including zero knowledge proofs, allow machines to prove they meet certain conditions without revealing the underlying data. This replaces the traditional model of trust based on stored secrets with trust based on mathematically verifiable claims.

For example, an IoT device may need to prove it is running approved firmware before joining a secure network. Instead of transmitting the firmware or sharing a certificate, the device can use a cryptographic proof to demonstrate compliance. In 2024, researchers demonstrated this approach through a system called zk-IoT, which lets devices prove they are using authorized firmware while keeping the code private.

This approach protects sensitive intellectual property and strengthens compliance. Machines can prove authorization without revealing internal processes, reducing data exposure and improving the security of machine-to-machine communication.

Conclusion

Machine accounts are no longer just supporting infrastructure. They are taking on active roles across systems, from triggering financial transactions to managing real-time data flows. As these responsibilities grow, regulators and security teams will expect the same level of accountability from machine identities as they do from human ones.

Meeting that expectation does not have to come at the cost of efficiency. We already have the tools to bring oversight, transparency, and trust to machine-driven processes—without slowing them down. The organizations that start building this accountability now will be better prepared for future compliance and better equipped to operate securely at scale.