Why are non-human credentials, services, and forgotten IoT devices driving the next wave of breaches? How can we detect, mitigate, and govern these identities?

While we focus on human passwords and MFA, thousands of machine tokens, keys, and certificates remain active and ready to be exploited. This invisible risk can bring down an organization.

This article explores the emerging attack surface formed by non-human identities: unrotated machine credentials, tokens forgotten in public repositories, and expired or mismanaged IoT certificates. Keep reading to learn more!

Non-Human Credentials: What They Are and Why They Increase Risk

Much of the security debate still focuses on human passwords and MFA, but another risk is growing.
In the shadows, non-human identities—defined by APIs, bots, automated services, containers, and IoT devices—carry persistent and almost invisible credentials.
Even without the authentication factors that support MFA, these identities rely on secrets such as keys, tokens, and certificates, creating a format that traditional security does not cover.

Remember the unrotated machine credentials, tokens forgotten in public source code, and expired or mismanaged IoT certificates we mentioned earlier?
That is the new attack surface. Keep reading to understand why current IAM controls designed for human users fail for NHIs (non-human identities), and the best practices in technology, processes, and governance to reduce this risk.

The Emerging Attack Surface of Non-Human Identities

Every application, microservice, or device (especially IoT) requires its own credentials.
API keys and secrets are often long-lived, broadly accessible, and sometimes allow direct authentication into an organization’s internal services.
SSH keys and CI/CD pipeline tokens are also frequently created and forgotten.

Each of these elements expands the attack surface.
Unlike users, non-human identities do not respond to MFA, lack a predictable lifecycle, and operate outside conventional IAM flows.
They are automatically created by deployments or AI, hold static privileges, and often do not even appear in security inventories.

Attack Vectors and Lessons Learned

Attacks often exploit secrets unintentionally shared through commits in public repositories—API keys that allow direct access to the cloud.
They also take advantage of service accounts with excessive privileges, where compromised automated jobs can provide wide-ranging control.

Ephemeral containers and pipelines may carry environment variables or images with embedded credentials.
AI-based code assistants sometimes suggest insecure snippets containing secrets.
Expired or poorly managed IoT and infrastructure certificates can lead to authentication loss or spoofing.

To mitigate these risks, it is essential to continuously review active secrets that may have been exposed and apply the principles of Least Privilege and Zero Trust.
It is also necessary to use secure credential storage vaults and OIDC/short-lived tokens.

All of this must be supported by continuous discovery, behavioral monitoring, and governance with human owners and periodic reviews.
The conclusion from real-world cases is clear: non-human credentials are high-value targets for attackers, enabling lateral movement, persistence, and data exfiltration.
Each example reinforces that hardcoded secrets, excessive access, and manual management do not scale.

Why Traditional IAM Fails for Machine Identities

Traditional IAM tools and processes were designed for human users, not autonomous entities.
This creates gaps that require special attention, such as onboarding and offboarding flows centered on employees and predictable HR-linked lifecycles.

Meanwhile, services, containers, serverless functions, and IoT devices emerge and scale dynamically with no “manager” or control point.
As a result, periodic reviews and MFA become inadequate, leading to fragmented visibility and insufficient automation.
Machine identity inventories often fail to cover pipelines, images, and repositories, leaving credentials hidden in silos.

Legacy practices maintain static, long-lived secrets embedded in code or scripts, rarely rotated.
This creates persistent backdoors and privilege policies that are frequently excessive, relying on implicit trust in the perimeter (VPC/datacenter).
When a machine identity is compromised, this facilitates lateral movement.

Combined, these factors make effective control unfeasible without continuous review, automated secret management, application of the principle of least privilege, and a zero-trust model adapted to automated workloads.
In short, everything learned about human IAM does not cover this scenario.

Detecting, Mitigating, and Governing Machine Identities

The first step is visibility: continuously discovering non-human identities across cloud environments, pipelines, repositories, and IoT devices.
This inventory must be enriched with context such as privileges, lifecycle, and exposure level.

From there, it becomes possible to classify risks and apply controls.
Key practices include secret vaults, rotation automation, and the use of short-lived tokens through OIDC.
Least Privilege and Zero Trust principles should be extended to machine identities as well.

Governance must ensure that each identity has a human owner responsible for its validation and periodic review.
Policies should define clear onboarding and offboarding processes for automated entities, just as already done with human users.

Finally, continuous monitoring is essential to detect anomalous behavior, such as credential misuse, lateral movement, or connections outside the expected scope.
This cycle of discovery, mitigation, and governance allows organizations to reduce the attack surface and turn machine identity management into a mature, measurable practice.

Conclusion

Non-human credentials have become one of the main blind spots in cybersecurity. What was once limited to human passwords has expanded into a massive and dynamic universe of tokens, keys, and machine certificates.

The result is clear: attacks increasingly target these elements because they are easier to overlook and harder to control.
Organizations that ignore this reality will remain exposed to incidents capable of paralyzing their operations.

Managing machine identities requires a cultural and technological shift: continuous discovery, automated governance, and the application of zero-trust principles.
Those who act now will transform a critical risk into a competitive advantage in security maturity.