The concept of identity has expanded far beyond human users. Non-human identity (HNI) refers to the digital identities assigned to entities that are not individual persons, such as software applications, IoT devices, AI agents, and more. As our digital ecosystems grow increasingly complex, understanding and managing these non-human identities has become crucial for security, access control, and accountability.
The history of non-human identity can be traced back to the early days of computing, with concepts like service accounts and daemon processes. However, the explosion of cloud computing, IoT, and AI has dramatically increased both the importance and complexity of non-human identity management.
Software applications and APIs are often assigned their own identities to interact with other systems securely. These identities typically use API keys or OAuth tokens for authentication.
IoT devices, from smart home appliances to industrial sensors, require unique identities to securely communicate and be managed within networks.
As AI systems become more autonomous, they need their own identities to interact with other systems, access data, and be held accountable for their actions.
RPA bots automate repetitive tasks and often require their own identities to access various systems and applications securely.
These are background processes or accounts used by operating systems and applications to perform specific functions, often with elevated privileges.
In VR and AR environments, avatars represent users or AI entities and require identities to interact within these digital spaces.
Smart contracts on blockchain platforms have their own identities, typically represented by their address on the blockchain.
Non-human identity data models often extend traditional Identity and Access Management (IAM) schemas. They may include attributes such as:
The NIST Special Publication 800-63 provides guidelines for digital identity models that can be adapted for non-human entities.
API keys are simple, long-lived tokens used to authenticate API requests. While easy to implement, they lack granular control and can be security risks if not managed properly.
X.509 certificates, based on public key infrastructure (PKI), provide strong authentication and are widely used for machine-to-machine communication. They're particularly useful for IoT devices and service-to-service authentication.
OAuth 2.0, particularly the Client Credentials grant type, is well-suited for M2M authentication. It provides secure, token-based access with fine-grained control and the ability to revoke access.
RBAC assigns permissions to roles, which are then assigned to identities. This model can be extended to non-human identities, allowing for consistent access control across human and non-human entities.
ABAC uses attributes of the identity, resource, and environment to make access decisions. This flexibility makes it well-suited for complex non-human identity scenarios.
Policy-based access control uses centrally managed policies to determine access rights. This approach can provide fine-grained control over non-human identity access.
Managing the lifecycle of non-human identities involves:
Automated lifecycle management is crucial for maintaining security and compliance, especially in environments with large numbers of non-human identities.
Major cloud providers offer specialized solutions for managing non-human identities:
AWS Identity and Access Management (IAM) roles can be assigned to EC2 instances, allowing applications running on these instances to securely access other AWS services without managing explicit credentials.
Azure Managed Identities provide an automatically managed identity in Azure Active Directory for applications, simplifying secret management.
Google Cloud uses service accounts as identities for non-human entities, allowing fine-grained access control to Google Cloud resources.
Kubernetes uses Service Accounts to provide identities for pods. Workload Identity extends this concept to allow Kubernetes applications to securely access cloud services.
Serverless platforms like AWS Lambda, Azure Functions, and Google Cloud Functions provide managed identities for individual functions, allowing secure access to other services without explicit credential management.
Service meshes like Istio provide identity and access management for microservices architectures. They offer features like mutual TLS authentication and fine-grained access policies between services.
Threat modeling for non-human identities should consider:
The STRIDE model can be adapted for non-human identity threat modeling.
HSMs provide a physical computing device that safeguards and manages digital keys for strong authentication. They are particularly useful for high-security non-human identity scenarios.
Vault systems provide a centralized solution for managing secrets, including those used by non-human identities. They offer features like dynamic secret generation, leasing, and revocation.
Regular rotation of credentials (e.g., API keys, certificates) is crucial for maintaining security. Automated rotation processes should be implemented to ensure consistency and reduce human error.
Immediate revocation capabilities are necessary for responding to security incidents. This often requires a centralized identity management system with real-time revocation features.
Continuous monitoring of non-human identity activities is essential for detecting anomalies and potential security breaches. This includes:
Tools like Elastic Stack (ELK) or cloud-native solutions like AWS CloudTrail can be used for comprehensive logging and monitoring.
Zero Trust principles should be applied to non-human identities:
The NIST SP 800-207 provides a comprehensive framework for implementing Zero Trust Architecture.
DIDs, as specified by the W3C, provide a decentralized approach to identity management that can be applied to non-human entities. This allows for more autonomous and self-sovereign non-human identities.
SSI principles, when applied to non-human identities, can provide greater autonomy and control. This is particularly relevant for AI agents and IoT devices that may need to operate independently.
AI and machine learning are being leveraged to enhance identity governance for non-human entities. This includes anomaly detection, automated access reviews, and predictive access modeling.
As quantum computing advances threaten current cryptographic methods, quantum-safe algorithms are being developed to secure non-human identities in the post-quantum era.
The General Data Protection Regulation (GDPR) has significant implications for non-human identities, particularly when they act as data processors. Key considerations include:
The National Institute of Standards and Technology (NIST) provides several guidelines relevant to non-human identity management:
These guidelines offer frameworks for secure identity management that can be adapted for non-human entities.
Various industries have specific regulations that impact non-human identity management:
As non-human entities become more autonomous, questions of liability and accountability become more complex:
Non-human identity management is a critical component of modern digital ecosystems. As we continue to develop more complex, autonomous systems, the importance of securely managing these identities will only grow.
Key takeaways:
As organizations increasingly rely on non-human entities to drive innovation and efficiency, investing in robust non-human identity management will be key to maintaining security, compliance, and operational effectiveness.
The field of non-human identity is rapidly evolving. Staying informed about new technologies, best practices, and regulatory changes will be crucial for organizations looking to leverage the full potential of non-human entities while managing associated risks.
*** This is a Security Bloggers Network syndicated blog from Meet the Tech Entrepreneur, Cybersecurity Author, and Researcher authored by Deepak Gupta - Tech Entrepreneur, Cybersecurity Author. Read the original post at: https://guptadeepak.com/non-human-identity-in-the-ai-age-a-technical-deep-dive/