Managing identities is no longer limited to employees logging into applications.

Modern enterprise environments include a growing number of non-human identities – workloads, containers, APIs, and other digital entities that interact autonomously. These identities enable applications and services to communicate securely and reliably across dynamic, distributed environments. However, they also introduce complexity and risks that traditional identity and access management (IAM) tools weren’t designed to handle.

Recognizing these challenges, Aembit has built its Workload IAM Platform to address the complexities of dynamic, cloud-native environments. By ensuring secure, seamless interactions between workloads, APIs, and third-party services, Aembit simplifies the management of non-human identities while scaling to meet the demands of modern enterprises. Designed in collaboration with some of the world’s largest companies, our platform helps organizations securely manage workload access across distributed, API-driven systems without introducing operational bottlenecks.

This post explores how Aembit, a platform purpose built for workload identity and access management, helps organizations secure and manage non-human identities at scale. We’ll delve into Aembit’s manageability features, real-world use cases, and practical implementation strategies, concentrating on emerging scenarios such as managing access to AI/LLM APIs, multi-cloud scenarios, and securing CI/CD pipelines.

The Problem: Managing Identities in a Distributed Ecosystem

In the past, enterprise IAM focused primarily on human users – employees, contractors, and partners. Non-human identities have changed the game; today, they far outnumber human users in most organizations. These identities include:

• Workloads: Applications, microservices, and containers that must communicate with databases, APIs, and external services.
• Infrastructure Components: Resources like Kubernetes pods, serverless functions, and cloud services that require secure communication.
• Automation Pipelines: Continuous integration and continuous delivery (CI/CD) systems deploying services at high velocity.
• Access to AI/LLM APIs: Workloads that interact with language model APIs like OpenAI, Anthropic, or Azure OpenAI to support advanced applications such as Retrieval-Augmented Generation (RAG), where systems retrieve external knowledge to augment AI-generated responses.

Managing these identities is challenging due to their unique characteristics:

1) High Volume and Dynamic Scale: Automated systems create and destroy thousands of ephemeral workloads daily.

2) Dynamic Relationships: Workloads frequently change their interactions with other resources as systems evolve.

3) Varied Credentials: Different workloads use various authentication methods, such as tokens, certificates, and API keys.

4) Specialized API Needs: Workloads require tailored access policies to securely retrieve sensitive data from APIs like those exposed by AI/LLM systems.

Organizations risk misconfigurations, unauthorized access, and privilege escalation without an effective and scalable solution.

The Aembit Solution: A Platform for Workload IAM at Scale