Why NHIS is the most dangerous blind spot in security

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When talking about cybersecurity identity, most people think about usernames, passwords, and the occasional MFA prompts. However, lurking beneath the surface is a growing threat that does not contain any human credentials, as it witnesses the exponential growth of non-human identity (NHI).

When NHI is mentioned, at the pinnacle of the mind, most security teams think quickly Service Account. But that’s far beyond that. You have Service Principal, The role of snowflakes, Already roleand platform-specific components from AWS, Azure, GCP, etc. The truth is that NHIS can be as different as the services and environments of modern technology stacks, and managing them means understanding this diversity.

The real danger lies in how these identities are authenticated.

Secret: Machine Currency

Inhuman identity is used to authenticate most of the time secret: API keys, tokens, certificates, and other credentials that grant you access to systems, data, and critical infrastructure. These secrets are what attackers want most. And to my surprise, most companies don’t know how many secrets they have, where they are stored, or who is using them.

Secret state is sprawled in 2025 Two jaw drop statistics revealed:

23.7 million In 2024 alone, new secrets were leaked on public github
and 70% Of the secrets leaked in 2022 It’s still effective today

Why is this happening?

That’s part of the story There is no MFA for the machine. There is no verification prompt. When developers create tokens, they often allow more access than they need to be, just to make sure things work.

date of expiry? option. Several secrets have been created in the 50-year effectiveness window. why? Because the team doesn’t want to break the app next year. They choose speed over security.

This creates a large blast radius. If one of these secrets is leaking, you can unlock everything from production databases to cloud resources without triggering an alert.

Detecting compromised NHIS is much more difficult than humans. Logging in from Tokyo at 2am may raise a red flag for people, but the machines speak 24/7 from all over the world. Malicious activities blend quickly.

Many of these secrets act like an invisible background, allowing for lateral movement, supply chain attacks, and undetected violations. The Toyota Incident is a perfect example. Leaked secrets can defeat the global system.

This is the reason Attackers love the NHIS and their secrets. There are too many permits, generally low visibility, and the results can be enormous.

The rise of machines (and their secrets)

Migration to cloud-native and microservice-rich environments have been introduced Thousands NHI by organization. NHIS is now outweighing human identity 50:1 to 100:1 Ratio, this is expected to increase only when it increases. These digital workers connect services, automate tasks, and drive AI pipelines. All of them need a secret to work.

However, unlike human qualifications:

The secret is hard coded in a codebase
Shared by multiple tools and teams
Doormant in legacy system
Passed to AI Agent with minimal monitoring

They often Expiration date is insufficient, Ownedand Auditability.

result? The secrets spread. Over-access. And one small leak from a massive violation.

Why Older Playbooks Don’t Work anymore

Legacy Identity Governance and PAM tools were built for human users, in an age where everything is centrally managed. These tools do great jobs that enforce password complexity, manage your breakgrass accounts, and dominate access to internal apps. However, NHIS completely defeats this model.

Here’s why:

IAM and PAM It is designed for human identity, often associated with individuals and protected by MFA. Meanwhile, NHI is decentralized. It is created and managed by developers across the team, and is often not central IT or security monitoring. Today, many organizations operate multiple safes, with no uniform inventory or policy enforcement.
Secret Manager It helps you keep secrets, but if secrets are leaked across infrastructure, codebases, CI/CD pipelines, and even public platforms like GitHub and Postman, they won’t help you. They are not designed to detect, correct, or investigate exposure.
CSPM Tools We focus on the cloud, but the secrets are everywhere. They are found in source control management systems, messaging platforms, developer laptops, and unmanaged scripts. When secrets are leaked, it’s not just a hygiene issue – it’s Security Incident.
NHIS does not follow the traditional identity lifecycle. Often there is no onboarding, offboarding, clear ownership and expiration date. They will remain in your system under the radar until something goes well.

The security team is chasing the shadows and trying to manually stitch together where the secret comes from, what it accesses, and whether it is still in use. This reactive approach is not scaled and exposes tissue to a dangerous extent.

This is here Gitguardian NHI Governance It will appear.

Gitguardian NHI Governance: Mapping Machine Identity Mazes

Gitguardian took deep expertise in detecting and repairing secrets and turned it into something more powerful. It is a complete governance layer of machine identity and its qualifications.

This is what stands out:

Map for confusion

Think of it as end-to-end Visual graph Your whole secret landscape. The map connects the dots between the following:

Where secrets are stored (for example, Hasicorp Vault, AWS Secrets Manager)
Which services consume them?
Which system do you want to access?
Who owns them?
Is it leaking internally or used in public code?

Complete Lifecycle Control

NHI governance exceeds visibility. It will be effective True Lifecycle Management Secret – Track their creation, use, rotation, and cancellation.

The security team:

Sets an auto-rotation policy
Deprecate unused/orphaned credentials
Detect unaccessed secrets in months (aka zombie credentials)

Security and compliance, built-in

Platform includes a Policy Engine This helps teams implement consistent control across all safes and benchmark themselves against standards like the OWASP Top 10.

You can track it:

Vault coverage across teams and environments
Secret Hygiene Indicators (Age, Usage, Frequency of Rotation)
Excessive Nhis
Compliance posture drifts over time

AI Agent: The New Wild West

This is a big driver with this risk Rag (searched generation)AI uses internal data to answer questions. It is useful, but if the secrets are hidden in that data, they can be falsely surfaced.

AI agents are connected to everything: Slack, Jira, Confluence and Internal Docs to unlock productivity. However, with each new connection, the risks Secret Sprol It will grow.

The secret is not leaked from the code anymore. They appear in documents, tickets, messages, and when AI agents access those systems, they can incorrectly publish their credentials to the response or log.

What’s not going well?

Secrets stored in Zilla, Concept, Slack etc. are leaked
AI log captures sensitive inputs and outputs
Developers and third-party vendors who store inconsistency logs
Access system-wide control breakdown

One of the most positive aspects of the Gitguardian platform is that it helps to fix AI-driven secret sprawls.

Scan all connected sources, including messaging platforms, tickets, WIKIs, internal apps, etc. to detect secrets that can be exposed to AI
There is a flag on an insecure path that indicates where the AI agent is accessing the data and could lead to leaks
Clean the logs, remove secrets, then delete secrets before saving or passing in a way that puts your organization at risk

AI is moving fast. But the secrets are leaking faster.

Bottom line: You cannot protect what you do not govern

With NHI governance, Gitguardian provides a blueprint for organizations to bring order to chaos and control over identity layers that have long remained in the dark.

Whether you are trying to: