Non-Human Identities in Cybersecurity: The Silent Explosion in the AI Era 

The rapid proliferation of non-human identities (NHIs) in modern IT ecosystems presents a critical, yet often overlooked, cybersecurity challenge. NHIs such as service accounts, API keys and automation bots now outnumber human identities by over 45 times in enterprises and pose significant cybersecurity risks due to their high privileges and inadequate oversight. The rise of autonomous AI systems has further accelerated the use of NHIs. This article examines the distinct security challenges associated with NHIs and underscores the need for a comprehensive identity security strategy to manage them.

Overview of Non-Human Identities 

NHIs are digital identities used by software, services, devices, AI agents or workloads to authenticate and access resources. Unlike human identities, NHIs are typically long lived, highly privileged and often lack rigorous oversight. Below are some common types and uses of NHIs. 

Cybersecurity Challenges of NHIs

1. Volume and lack of visibility: Most organizations have no centralized inventory of NHIs. A 2024 study by Astrix Security found that the average enterprise has over 250,000 non-human identities, 45x more than human identities. Additionally, 37% of organizations reported inadequate monitoring and logging of NHI activity 
2. Overprivileged and long-lived credentials: API keys and service accounts are frequently created with excessive permissions and do not expire or rotate
3. High cost of misuse: Unmanaged non-human identities, such as service accounts or API keys with excessive permissions, can be exploited or misconfigured, leading to runaway processes that incur unnecessary high usage costs in cloud computing environments 
4. Secrets sprawl and hard coding: Developers routinely hard-code secrets in GitHub or other repositories 
5. Lack of MFA or behavioral monitoring: Traditional multifactor authentication (MFA) and user behavior analysis solutions don’t apply to machines
6. Supply chain and dependency risks: A single compromised third-party API key in a dependency can affect thousands of downstream applications

Security Implications for AI

In the AI ecosystem, most data exchange is done through machine-to-machine communication using API calls and AI agents heavily utilizing non-human identities. The following reasons make management and security of NHIs critical for AI-based applications. 

1. Blast radius in AI systems: In the AI era, compromised API keys used by autonomous agents can be exploited to cause security issues and drive massive, unauthorized token usage, resulting in significant cost and operational impact. AI agents that chain multiple tools exponentially increase the attack surface
2. Risks of MCP servers and agent swarms: Model context protocol (MCP) servers act as a bridge, enabling AI models to connect with external data sources and services, using API keys or service accounts. If an AI agent's identity is compromised or over-permissioned, an attacker could exploit it to move laterally across systems, execute financial transactions or extract customer data at machine speed and scale
3. Identity exposure through prompt injection: Attackers use prompt injection to make an agent exfiltrate its own API keys or escalate privileges

Key Practices of Comprehensive NHI Security

1. Treat humans and machines differently: NHIs operate at scale and require machine-based authentication methods such as just-in-time (JIT) access tokens and certificate-based authentication, not human-centric ones like MFA
2. Discovery and inventory: Continuously scan code repos, cloud accounts, identity providers, vaults, CI/CD pipelines etc., to maintain an updated inventory of NHIs
3. Implement least privilege access: Grant each non‑human identity only the minimum permissions required, for the shortest duration necessary to complete its task. Regularly review and remove unnecessary privileges
4. Adopt the Zero Trust model: Assume no identity, human or non-human, is trustworthy by default. Enforce strict access controls and verify every request
5. Avoid shared identities: Each service or application should have its own unique identity to ensure accountability and traceability
6. Enforce strong authentication and secure credential management: Store credentials securely in a dedicated secrets management platform, not in code or config files. Use short-lived, JIT access tokens or certificates rather than static API keys or passwords. Automate credential rotation to prevent stale or long-lived secrets from being exploited
7. Assign clear ownership: Every NHI should have a clear owner, such as a specific team or individual, responsible for its purpose, access scope and lifecycle
8. Automate lifecycle management: Use automation for the entire identity lifecycle (provisioning, rotation, deactivation) to ensure consistency and reduce manual errors
9. Monitor and audit activity: Continuously log and monitor all NHI activity for anomalies, suspicious behavior or access attempts from unusual locations. Monitor for machine IDs using interactive log ins. This may indicate a compromised account
10. Implement AI agent-specific controls: Monitor token spends and where appropriate, implement controls such as agent sandboxing, prompt security and approval gates for external calls to minimize abuse
11. Implement identity security posture management (ISPM): An ISPM tool can help implement the above points and provide continuous visibility into the entire lifecycle of machine credentials, enforce least-privilege access principles and proactively detect and remediate over-permissioned, orphaned or misconfigured identities

Conclusion

As NHIs become the backbone of machine-to-machine communication, their security is now critical for organizations. The magnitude and scale of risks, from financial loss to large scale data breaches, demand that organizations treat NHIs with the same rigor as human identities. Implementing robust discovery, least privilege, continuous monitoring and automated lifecycle management is no longer optional; it is essential for safeguarding data, operations and trust in the AI era.

To reduce risk and gain visibility into non-human identities, organizations should evaluate their identity security strategy and implement controls that address both human and machine identities. Learn more about how to solve your biggest identity security challenges here.