Non-human identities have become the silent majority in enterprise environments. While security teams fortify human user access, service accounts, API keys, and automated workflows operate continuously without direct human oversight. I read a report from Gartner and found that 57% of organizations find themselves worrying about leaked secrets in their automated workflows and AI implementations. But it makes sense, no? As AI and automation reshape how organizations operate, this expanding universe of NHIs creates security blind spots that traditional approaches lack the depth to address.
Most organizations don’t even know how many digital credentials they have, let alone where they’re stored or how they’re used.
Enterprise Security for AI Agents & Non-Human Identities
The scope of non-human identities
NHIs serve as the foundational authentication layer in modern architectures.
- At their core, service accounts execute privileged operations — from infrastructure management to data processing pipelines. While these accounts enable automation, their persistent nature and elevated access make them prime targets for attackers.
- If we go deeper into the architecture, API keys facilitate the constant hum of machine-to-machine communication, authenticating countless interactions between microservices and external integrations.
- OAuth tokens take this a step further since they handle nuanced delegated access patterns that allow controlled resource sharing without credential exposure.
- Lastly, digital certificates establish trust through PKI infrastructure, securing communications and validating service authenticity.
Yet these identities rarely work alone. Consider a typical data processing workflow: a service account triggers the initial process, API keys orchestrate communication between multiple services, OAuth tokens reach out to third-party endpoints, and certificates secure the entire data path. It is a dance, really, that creates a complex web of security implications. The AI revolution further complicates this landscape, as these autonomous systems require their own identity credentials to function.
The stakes are high. A compromised service account becomes an attacker’s gateway to privileged infrastructure; exposed API keys enable silent data exfiltration across system boundaries; breached OAuth tokens compromise entire delegation chains. But for modern architectures that span hybrid environments, container platforms, and serverless functions, these risks can only multiply.
Secrets storage and exposure points
Enterprise environments house non-human identities and associated secrets across multiple sanctioned storage locations, and they all present unique discovery challenges.
Vaults
Cloud vaults serve as the first line of defense, with AWS Secrets Manager, Azure Key Vault, and HashiCorp Vault offering centralized storage solutions. Yet these vaults often operate in isolation thus creating visibility silos between teams and environments. Their native discovery capabilities typically stop at organizational boundaries, missing cross-account and cross-platform secrets.
Version control systems
Version control systems present a more complex challenge. Despite recommended security best practices, credentials persist in code, configuration files, and even documentation across GitHub, GitLab, and Bitbucket repositories. We can often find these secrets in commit histories and forks and this paves the way for a long-lasting attack surface that traditional scanning tools simply can’t address.
CI/CD pipelines
CI/CD pipelines introduce another layer of complexity. Build environments in Jenkins, GitHub Actions, and CircleCI require broad access across development and production systems. These pipelines store high-privilege credentials as build secrets, environment variables, and configuration parameters. And given the short-lived nature of these processes, the secrets tend to constantly shift between different stages and environments.
Native Infrastructure
Cloud infrastructure itself becomes a repository through native identity mechanisms. AWS IAM roles, Azure’s managed identities, and GCP service accounts often proliferate across cloud resources. These identities usually accumulate excessive permissions through role inheritance and cross-service access. Compound that with the dynamic nature of cloud environments, the auto-scaling, and infrastructure-as-code practices. How far do you think you will go tracking these identities manually?
Moreover, the interconnected nature of these storage vectors means secrets rarely stay contained within their intended boundaries. Even a single deployment pipeline might pull credentials from multiple vaults, inject them into cloud resources, and store them temporarily in build environments. So, am I overreacting when I say we need a sophisticated discovery mechanism to navigate this web of secrets propagation?
Collaboration platforms
Beyond standard storage locations, secrets frequently accumulate in the most unexpected of places. Collaboration platforms like Slack and Microsoft Teams become unofficial secrets repositories, where developers share API keys and access tokens through chat channels and shared workspaces. Say, in an instance, a Jenkins API key is shared in a Teams channel for troubleshooting or an AWS access key is pasted in a Slack thread for quick deployment.
These supposedly “temporary” solutions often become permanent fixtures and create shadow IT scenarios that bypass security controls entirely.
Container orchestration
The problem intensifies in container orchestration environments. Take Kubernetes clusters, where secrets spread like wildfire — across namespaces, within pod specifications, throughout persistent volumes, and inside etcd storage. Native secrets management helps, but credentials still end up exposed in mounted volumes and configuration files.
Helm charts add another layer of complexity, carrying sensitive values through their templates and default configurations. Meanwhile, Kubernetes operators, designed to automate cluster management, inadvertently become mechanisms to distribute secrets.
Dev and staging environments
Development and staging environments tell an all-too-familiar story. They breed secrets sprawl through sheer necessity. Each test pipeline, staging deployment, and local development instance demands unique access credentials and these multiply across environments be it local servers or CI systems, container registries, or cloud staging areas. Legacy systems amplify this risk through forgotten service accounts and API keys with extensive privileges, silently persisting in old configuration files and deployment scripts.
Beyond traditional visibility
Finding secrets is just the beginning. The real challenge lies in understanding how the non-human identities behave in your environment. A service account might look harmless until you discover it has admin access across multiple AWS accounts. An API key might seem properly secured until you notice it’s being used from unexpected IP ranges at odd hours, and so on. Static scans and periodic audits are simply not enough. So, when containers spin up and down in seconds, and infrastructure changes through code commits, you need continuous visibility. This means tracking how identities interact with resources in real-time, spotting unusual patterns, and understanding the full scope of their access.
Behavioral modeling transforms this visibility into actionable intelligence. By analyzing historical patterns, security teams can establish baseline behaviors for each non-human identity. This includes typical access times, resource interaction patterns, and permission utilization rates. Deviations from these baselines — like a service account suddenly accessing production databases in the middle of the night — should trigger immediate investigation.
Risk-based prioritization adds crucial context to this visibility. And you’d agree that not every anomaly requires immediate action. A development environment API key showing unusual patterns might be less critical than a production service account attempting privileged operations as discussed earlier. Security teams can focus on high-impact risks first by understanding the potential blast radius of each identity auch as its effective permissions, resource access, and potential for lateral movement.
Entro’s advanced discovery architecture
Entro goes above and beyond when it comes to non-human identity management. Unlike conventional solutions that focus on point-in-time scanning, Entro provides continuous visibility and control across the entire non-human identity lifecycle. The platform’s holistic approach enables security teams to discover, analyze, and secure the identities and secrets at scale, while reducing exposure risks by up to 90%. Here’s how:
Comprehensive detection and NHIDR
The platform’s Non-Human Identity Detection and Response (NHIDR) engine goes beyond basic secrets scanning. It uncovers non-human identities across code repositories, serverless applications, cloud services, and even the leaks in dark web. This thorough approach ensures no credential remains hidden, whether it’s in a Bitbucket repository or a Kubernetes config file.
Enrichment and context
Raw discovery metamorphoses into actionable intelligence through automated, intelligent enrichment. Entro analyzes over 1000 non-human identity types, examining ownership patterns, permission structures, and usage history. All discovered secrets get enriched with key metadata that paints a finer picture about their potential exposure points and security implications.
Risk analysis and proactive threat detection
The platform employs static risk analysis to detect misconfigurations across NHIs, secrets, and vaults. It is constantly on the lookout for permission drift, shadow admins, and toxic permission combinations across cloud environments. It can identify abnormal patterns in vault and cloud logs through real-time behavioral monitoring and goes on to enable proactive threat detection before breaches occur. This also includes analyzing secrets abuse, access patterns and potential attack paths.
Automated response and lifecycle management
When issues arise, Entro initiates automated response workflows. From secrets rotation to policy enforcement, the platform streamlines the entire lifecycle of non-human identities. This automation extends to provisioning and decommissioning processes, ensuring continuous compliance and security.
Integration and ecosystem
It takes discovery to the next step through a sophisticated, multi-layered architecture that integrates seamlessly with your existing security infrastructure. The platform’s one-click integration capabilities connect with AWS Secrets Manager, Azure Key Vault, GitHub secrets, Kubernetes secrets, and various cloud services including AWS, Azure, and GCP. This extends to collaboration platforms like Teams, Slack, and Zoom, as well as CI/CD workflows through Jenkins and GitHub Actions.
Furthermore, this integration also enables unified visibility and control across development and staging environments including production systems. It can also connect with SIEM solutions for enhanced threat correlation, SOAR platforms for automated incident response, and identity governance tools for comprehensive access management.
It’s worth noting that these capabilities work in concert, each serving as a building block for a comprehensive solution for managing the entire non-human identity lifecycle from discovery and monitoring to response and remediation.
So, when I say don’t let non-human identities become your security blind spot, there’s a mountain of data backing me up. Let Entro give you the visibility and control you need across your entire digital ecosystem. Schedule a demo today.
