Cremit | Human vs. Non-Human Identity: The Key Differentiators

Article

Felipe Araujo

March 25, 2025

4-minute read

Digital identities have evolved far beyond usernames and passwords for employees and customers. Behind every modern organization's firewall lurks a vast, often unmanaged population of service accounts, API keys, bots, and machine identities that outnumber human users by orders of magnitude. These non-human identities (NHIs) represent both the backbone of digital transformation and an expanding attack surface that security teams must urgently address.

Understanding Human and Non-Human Identities

Human identities represent individuals with specific roles and responsibilities within an organization. They include employees, contractors, partners, and customers who interact with systems based on their job functions or relationships with the company.
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Non-human Identities encompass all digital identities not directly tied to an individual person. Here's some examples below:

Infographic defining 6 types of Non-Human Identities: Service Accounts, API Keys, Bots, etc.

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While human identities typically follow traditional Identity and Access Management (IAM) frameworks, non-human identities operate under different paradigms that require specialized security approaches. The most significant security challenges emerge not from treating each type separately, but from failing to recognize their fundamental differences.

Core Differentiators Between Human and Non-Human Identities

Operational Characteristics

Human identities are characterized by:

Predictable usage patterns: Typically work during business hours with consistent access needs
Cognitive decision-making: Can interpret contextual security factors and exercise judgment
Self-management capabilities: Can reset passwords, request access, and report issues
Limited parallel operations: Only one session or action at a time
Natural velocity limits: Human-speed interactions with systems

Non-Human Identities operate with:

Programmatic behavior: Follow defined algorithms without discretion
Continuous operation: Often run 24/7 without breaks
High-volume automation: Can execute thousands of operations per second
Parallel processing: Multiple simultaneous connections and actions
No inherent self-management: Cannot independently manage their own credentials

Authentication & Authorization Differences

Human identities authenticate through:

Knowledge factors: Passwords and security questions
Possession factors: Mobile devices, security tokens
Inherence factors: Biometrics (fingerprints, facial recognition)
Context validation: Location, device, and behavior patternsNon-human identities rely on:

Non-Human Identities rely on:

Embedded credentials: Hardcoded or environment variables
Certificate-based authentication
Token-based mechanisms: OAuth, JWT tokens
Key-based validation: API keys, encryption keys
IP-based restrictions: Network location validation

Risk Exposure Contrasts

Human Identities present risks through

Social vulnerability: Susceptibility to phishing and social engineering
Behavioral inconsistency: Variations in security practices
Privilege escalation attempts: Deliberate attempts to gain unauthorized access
Credential sharing: Password sharing between colleagues
Termination gaps: Access that persists after employment ends

Non-Human Identities create risks through:

Credential persistence: Long-lived, rarely changed secrets
Privilege concentration: Often has extensive system access
Invisibility: Frequently operates outside normal monitoring
Orphaned accounts: No clear ownership or accountability
Embedded secrets: Credentials stored in code or configuration files
Rapid exploitation potential: Once compromised, can be leveraged at machine speed

Lifecycle Management Distinctions

Human Identities Follow:

Structured onboarding/offboarding: Formal processes tied to employment
Role-based evolution: Changes aligned with job responsibilities
Regular certification: Periodic reviews of access rights
Self-service elements: Password resets and access requests
Clear ownership: Direct accountability for actions

Non-Human Identities Experience:

Ad-hoc creation: Often created outside formal processes
Unstable existence: May exist for minutes to months
Function-based access: Rights tied to technical functions, not roles
Unclear termination points: Often lack defined end-of-life
Distributed responsibility: Ambiguous ownership across teams
Automated provisioning: Created through CI/CD pipelines and infrastructure-as-code

Scale and Proliferation Differences

Human Identities:

Stable population: Growth tied to workforce expansion
Predictable quantity: Aligns with organizational headcount
Centralized management: Typically managed by HR and IT
Visible presence: Recorded in employee directories
Natural constraints: Limited by organizational size

Non-Human Identities:

Exponential growth: Often 45x more numerous than human identities
Shadow creation: Generated outside governance processes
Decentralized management: Created by developers, operations teams, and automated processes
Hidden existence: Often undocumented and untracked
Limited constraints: Can multiply rapidly with new technology adoption
Environment-specific proliferation: Multiple identities for different environments (dev/test/prod)

Monitoring Capability Differences

Human identities are monitored through:

Behavioral analysis: Unusual login times or locations
Activity thresholds: Number of actions or sessions
Authentication anomalies: Failed login attempts
Device profiling: Tracking authorized devices
Training effectiveness: Response to security awareness initiatives

Non-Human Identities require monitoring of:

Volume metrics: Unusual API call frequency
Resource utilization: Abnormal compute or data access
Permission utilization: Using dormant or rare privileges
Connection patterns: New or unusual connection sources
Execution anomalies: Deviations from expected operational patterns
Credential age: Identifying stale or long-lived secrets

The Critical Role of Secret Detection in Identity Security

The distinctions between human and non-human identities extend far beyond access patterns and authentication methods; they shape the very foundation of modern security strategies. While traditional identity management focuses on protecting human credentials, the explosive growth of non-human identities introduces a far greater challenge: the proliferation of embedded secrets scattered across code, infrastructure, and automation workflows.

This expanding attack surface demands a dedicated approach to secret detection. Hardcoded API keys, long-lived service account credentials, and mismanaged tokens are among the most common sources of breaches, yet they often go unnoticed until it’s too late. Consider these risks:
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Over 6 million secrets leak on GitHub annually.
The average enterprise has thousands of exposed credentials lurking in its code repositories.
85% of breaches involving non-human identities originate from leaked secrets.
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Without continuous, proactive secret detection, organizations risk silent but devastating compromises. Modern security solutions, like Cremit, integrate automated scanning across development environments, collaboration tools, and runtime systems to identify and remediate exposures before attackers exploit them.
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Secure Your Non-Human Identities Now

NHIs outnumber human users in most environments, yet they often go unprotected. Embedded secrets in code, infrastructure, and automation workflows create serious security risks.

Start securing your secrets today with Cremit’s solution. Get started now or schedule a demo to see how Cremit helps protect non-human identities at scale.

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A dark-themed cybersecurity dashboard from Cremit showing non-human identity (NHI) data analysis. Key metrics include “Detected Secrets” (27 new) and “Found Sensitive Data” (58 new) from Jan 16–24, 2024. Two donut charts break down source types of detected secrets and sensitive data by platform: GitHub (15k), GetResponse (1,352), and Atera (352), totaling 16.9k. The dashboard includes a line graph showing trends in sensitive data over time, and bar charts showing the top 10 reasons for sensitive data detection—most prominently email addresses and various key types (API, RSA, PGP, SSH).

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