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 these machine identities multiply, they often go unmanaged, creating hidden vulnerabilities that can be exploited if not properly tracked.
• Traditional identity governance tools were not built for machines. Static roles, manual approvals, and periodic reviews cannot keep up with the speed and scale of automated systems.
• Regulations now hold machine identities to the same accountability standards as human users. Organizations must ensure compliance by tracking machine actions, limiting permissions, and automating governance.

 

Machine identities such as service accounts, bots, APIs, and IoT devices now outnumber human users in most enterprise systems. This imbalance is not a minor detail. Unlike people, machines do not quit or retire. They spin up quickly, run in the background, and often stay active long after they are no longer needed. These characteristics make them easy to overlook but risky to ignore.

Many security breaches, including the CircleCI incident in 2023, have been traced back to compromised devices and machine credentials embedded in automation pipelines. Even so, many organizations still underestimate their importance, and some do not track them at all.

As automation and artificial intelligence reshape how systems operate, machine identities have multiplied. This creates a growing compliance risk. From forgotten API keys to unmonitored service accounts, many organizations lack the controls needed to manage non-human access. This article looks at what machine identities are, why they matter now, and what it takes to govern them with the same level of oversight applied to human users.

What Is a Machine Identity?

A machine identity is any digital credential that allows a non-human system to identify itself and securely interact with other systems. These credentials serve the same purpose as a login for a person. They confirm that the machine is authorized to access a system, perform a task, or share data.

Machine identities come in different forms, depending on their role. 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

These systems do not authenticate once and remain static. They continuously authenticate, trigger workflows, and move sensitive data between environments. A server might access a database every few seconds. An IoT device might send constant updates to a cloud service. All of these interactions rely on trusted machine identities operating in the background.

What makes machine identity especially important today is its scale. As organizations adopt more automation, cloud-based applications, and artificial intelligence, the number of machine interactions has surged. In large enterprises, machine identities now outnumber human users many times over. According to research from CyberArk, machine identities already outnumber human ones by at least 45 to 1, and the gap continues to grow.

Without proper controls, these identities can create serious blind spots. Machines that aren’t tracked, updated, or secured are vulnerable to exploitation. A forgotten token or expired certificate might disrupt services—or even worse, an exposed service account could open the door to attackers. As organizations become more reliant on machines, managing their identities is becoming a critical part of both cybersecurity and compliance.

Why Traditional Identity Governance Doesn’t Work for Machines

Despite their growing role in modern systems, machine identities are still often managed with tools designed for people. Traditional identity and access management (IAM) systems assign roles to employees, enforce login policies, and monitor activity based on job function or department. These methods work well for human users but do not translate easily to machines.

Unlike human users, machine identities are generated programmatically—by scripts, deployment tools, or services—and scale rapidly. A single application can generate hundreds of credentials in minutes. These identities vary widely in lifespan and function, making governance especially complex.

Even when they’re technically registered within IAM platforms, governance practices often lag behind. Scheduled access reviews, manual approvals, and static roles cannot keep up with the speed and fluidity of modern machine workflows. Some identities live only for a moment. Others persist for years, with access needs that shift dynamically over time.

In many cases, machine credentials are never recorded at all. They’re hardcoded into scripts, embedded in configuration files, or passed along automation pipelines. This leaves them invisible to security teams and disconnected from traditional monitoring tools.

As they accumulate, these unmanaged identities introduce mounting risk. Many lack clear ownership or expiration timelines. When issues arise, teams may not know what a credential does, who created it, or whether it’s still in use. Human-centric IAM tools weren’t built for this complexity. Managing machine identities effectively requires a governance model rooted in automation, visibility, and system-level adaptability.

The Compliance Risks of Unmanaged Machine Identities

As traditional governance tools fall short, many machine identities operate without proper oversight. They’re frequently provisioned with broad permissions and left running beyond their intended use. Without routine checks, these credentials can persist unnoticed, accumulating risk over time.

What may seem like routine infrastructure can quickly become a liability. Below are common ways unmanaged machine identities contribute to real-world incidents and regulatory failures:

1. Expired Certificates

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

For instance, in May 2023, Cisco’s SD-WAN hardware experienced major disruptions after expired certificates stopped devices from communicating. The outage affected over 20,000 enterprise customers. Beyond the loss in productivity and revenue, incidents like this can trigger compliance concerns due to downtime, especially if service-level agreements are breached.

2. Hardcoded API Keys 

A frequent mistake involves hardcoding credentials directly into source code, scripts, or configuration files. These “secrets” include API keys, SSH private keys, and database credentials—each acting as a form of machine identity. When exposed, these credentials can be used without detection.

A recent example involved researchers uncovering nearly 1,500 active Mailchimp API keys embedded in public-facing code. These could have allowed unauthorized access to sensitive systems or customer data. Breaches like this can lead to violations of privacy regulations such as GDPR or CCPA, especially when customer data is at risk.

3. Zombie Credentials

Zombie credentials are machine identities that remain active long after their intended use has ended. They often originate from test environments, decommissioned tools, or accounts once tied to employees who have since left. Because these credentials are rarely monitored or rotated, they become easy entry points 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.

4. Over-Privileged Machine Identities

During deployment, it is common to grant broad permissions to machine accounts in order to avoid delays. But these access rights are rarely scaled back after the initial setup. Over time, this leaves systems exposed to unnecessary risk.

Toyota’s 2022 data breach is one example. A subcontractor’s exposed server contained a private key with access to internal systems. The associated machine identity had more permissions than necessary, resulting in a leak of personal data affecting over 296,000 users. Regulators cited it as a failure of access control, highlighting how overly permissive machine accounts can become compliance liabilities.

5. Lack of Auditability and Accountability

Unlike human users, machine identities often lack assigned ownership or clear accountability. Many are generated automatically and never tracked. This makes it difficult to determine which machine performed a given action or accessed a specific resource.

Without proper logging and visibility, organizations struggle to meet basic compliance requirements. Frameworks like GDPR, HIPAA, SOX, and PCI DSS demand clear records of who accessed what and when. If a machine identity is responsible for a security event and there is no traceability, it becomes nearly impossible to respond effectively or demonstrate control to auditors.

What Compliance Now Requires for Machine Identities

The risks tied to unmanaged machine identities are not just operational, they are also regulatory. As compliance standards become more demanding, organizations must prove they can secure all forms of access, not only those connected to employees or contractors. This includes APIs, bots, service accounts, and other automated systems that handle sensitive data.

Compliance today is not about checking boxes. It is about demonstrating that both users and machines act responsibly, with the right safeguards in place. Excluding machine identities from that framework creates blind spots and exposes businesses to legal, operational, and reputational risk.

Here are three core expectations that compliance frameworks now place on organizations, and why machine identities must be included:

1. Clear records of who or what accessed data

Regulators expect organizations to know exactly who or what accessed sensitive information. That applies not only to employees and third-party vendors but also to automated systems like APIs, bots, and service accounts. Yet many of these identities operate silently in the background, and few companies can answer basic questions about their behavior.

A clear example is the SolarWinds breach, where attackers used compromised machine credentials to move across systems undetected. The lack of visibility into machine activity created a blind spot—one that modern regulations such as GDPR and HIPAA now explicitly address.

2. Defined permissions and least privilege access

Just as employees should not have more access than they need, machine identities must follow the principle of least privilege. This means granting only the permissions required for the task at hand. In practice, though, machine accounts are often created with broad access and rarely reviewed. Over time, bots, containers, and scripts accumulate unnecessary privileges, which increases the risk of misuse or compromise.

3. Auditable logs for every action

If a system action cannot be traced, it cannot be trusted. Global privacy and cybersecurity frameworks require organizations to maintain detailed audit trails that capture what happened, when it happened, and who—or what—initiated the action. This includes machine identities. When an IoT device triggers a medical alert or an API processes a financial transaction, the system must log and verify the event. Yet many organizations still fail to centralize logging for machine identities, allowing critical actions to go unrecorded and leaving accountability gaps unresolved.

Best Practices for Managing and Governing Machine Identities

Meeting compliance requirements starts with a structured approach to managing machine identities. Visibility, control, and auditability are essential, but manual processes cannot keep up. As machine-based systems grow, organizations need scalable frameworks that bring order to the complexity.

What works for a few identities quickly breaks down at scale. Managing thousands across cloud environments and automated systems requires consistent, automated practices that can adapt in real time.

Below are four core pillars of effective machine identity governance:

1. Inventory

The foundation of machine identity governance is visibility. Organizations must be able to discover and catalog every machine identity in use. This includes service accounts, API keys, containers, certificates, bots, and any non-human actor with access privileges. Without a complete and current inventory, it’s impossible to manage risk or enforce security policies.

2. Automation

Manual processes cannot keep up with the scale and speed of machine identity use. Credentials such as tokens and certificates should be issued, rotated, and revoked automatically. Built-in expiration policies and time-to-live settings help ensure that credentials do not linger past their usefulness or become forgotten attack vectors.

3. Access Controls

Machine access should be governed by the same principles applied to people. Role-based access control (RBAC) or attribute-based access control (ABAC) can limit machine permissions to only what is needed for a specific function. These controls must be applied consistently and adjusted as workflows evolve to reduce the risk of over-privileged accounts.

4. Logging and Auditing

Every action taken by a machine identity must be traceable. Organizations should maintain detailed logs showing what was done, when it occurred, and which identity was responsible. These records are essential not just for meeting compliance requirements, but also for internal monitoring, incident response, and overall system integrity.

Applying Decentralized Identity to Machine Identity

The practices above—inventory, automation, access control, and logging—form the foundation of effective machine identity management. But as systems become more distributed and machines interact across organizational boundaries, even well-managed credentials can hit limitations. Machine identities need to work reliably across networks, platforms, and partners—without depending on centralized control or static secrets.

This is where decentralized identity adds an essential next layer. It gives machines unique, verifiable, and self-managed identifiers that can be trusted across different systems. Just like decentralized identity helps individuals prove who they are without oversharing, it allows machines to authenticate securely and interact without exposing sensitive data or relying on stored secrets.

Key enabling technologies include:

1. Verifiable Credentials for Machine Compliance and Authorization

Verifiable credentials are digitally signed claims that can prove specific attributes or permissions. For machines, these might confirm a passed security audit, a compliance certification, or operational readiness. These credentials can be verified instantly without contacting a central authority at the time of use.

This model is already active in global supply chains. U.S. Customs and Border Protection has piloted verifiable credentials and decentralized identifiers to create digital twins of goods like oil and steel. These credentials let systems prove an item’s origin, inspection status, and chain of custody without exposing excess data to every party along the route.

A similar approach applies to machines. For example, a vehicle’s onboard system can present a verifiable credential at a customs checkpoint to confirm it passed inspection and holds authorization to transport goods. This approach improves both privacy and efficiency by reducing reliance on manual checks or database queries.

2. Decentralized Identifiers for Persistent Machine Identity

Decentralized identifiers, or DIDs, are globally unique identifiers that are cryptographically verifiable and do not rely on a centralized authority. They give machines a persistent, self-managed identity that can be used in automated and peer-based systems.

With a DID, a machine can authenticate itself without relying on usernames, passwords, or static API tokens. This is especially valuable in cloud environments, where services interact frequently and at high speed. An automated agent or API can use a DID to prove it belongs to the organization and is authorized to carry out specific tasks.

This reduces the need for embedded credentials, makes machine interactions traceable and auditable, and lowers the risk of misuse. Identity becomes part of the system logic, supporting 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 protects sensitive intellectual property and ensures strong security and compliance. Machines can demonstrate what they are allowed to do without revealing how they do it, reducing data exposure and strengthening the integrity 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.

Identity.com

Identity.com helps many businesses by providing their customers with a hassle-free identity verification process through our products. Our organization envisions a user-centric internet where individuals maintain control over their data. This commitment drives Identity.com to actively contribute to this future through innovative identity management systems and protocols.

As members of the World Wide Web Consortium (W3C), we uphold the standards for the World Wide Web and work towards a more secure and user-friendly online experience. Identity.com is an open-source ecosystem providing access to on-chain and secure identity verification. Our solutions improve the user experience and reduce onboarding friction through reusable and interoperable Gateway Passes. Please get in touch for more information about how we can help you with identity verification and general KYC processes using decentralized solutions.