Secure the Unseen: Cracking the Code on Machine Identity Management

Whether you realize it or not, machine identities have become the silent backbone of modern enterprise infrastructure. While human identity and access management (IAM) has received significant attention, non-human entities — such as service accounts, cloud instance identities, workload identities and artificial intelligence (AI) agents — now outnumber human identities by ratios upwards of 5:1 in traditional environments and to 1000s:1 (or more) in cloud deployments.

This rapid growth of machine identities is an operational necessity for most businesses, but it can bring with it major security risks as well — especially with the explosion of emerging AI agents. While most organizations use machine identities to enable the automation and integration essential for digital transformation, these identities are often poorly managed and understood.

When it comes to maintaining a strong, agile enterprise security strategy, establishing effective machine identity management is no longer optional.

The challenge that comes with managing machine identities goes beyond basic credential management. The term, “machine identities” has evolved from specific use cases like service accounts to now include AI agents, certificates, service accounts, API tokens and cloud instance identities.

Unlike human accounts, machine identities operate programmatically, often requiring automated credential rotation and different authentication mechanisms. Encompassing authentication infrastructure, infrastructure identity, access control mechanisms and more, each machine identity segment brings distinct governance and security needs with it. Key segments include:

• SSL/TLS certificates: These are essential for secure communications, protecting HTTPS traffic and establishing trusted connections. Managing thousands of certificates across web servers and microservices requires automated discovery, renewal and policy enforcement to prevent outages and compliance failures.
• SSH keys: Widely used in Unix, DevOps and cloud environments for server-to-server authentication, unmanaged SSH keys can become persistent backdoors for attackers.
• Cryptographic keys: Used for encryption, digital signing and identity verification, these should be securely stored in hardware security modules (HSMs) or cloud-based key management services (KMS) with thorough auditing and monitoring.
• API keys and tokens: Critical for service-to-service authentication in microservices, these tokens bundle access rights that can be exploited if exposed.
• Device identities: IoT devices, endpoints and desktops require authentication to access networks.

Non-human identities (NHI) are a subset of machine identities, specifically focusing on credentialed entities that operate without direct human interaction. NHIs include:

• Service accounts: The most common machine identity; these accounts often accumulate excessive privileges or lack clear ownership, creating significant security risks.
• Cloud instance identities: Built-in cloud identities for virtual machines (VMs), containers and services provide resource access but can lead to privilege creep without cloud infrastructure entitlement management (CIEM).
• Workload identities: These identities authenticate containers and applications in dynamic, cloud-native environments.
• AI agents: Combining service account authentication with autonomous decision-making, AI agents will eventually blur the line between machine and human identities. New governance frameworks are needed to manage their unique requirements.

As machine identities proliferate at an exponential rate, especially in the cloud, tracking all privilege levels can be a challenge. NHIs can include testing IDs, cloud service IDs, application IDs and generic admin IDs, which often lack proper governance and visibility. These unmanaged identities can lead to significant security vulnerabilities, especially for organizations with a large cloud and multi-cloud estates.

NHIs have become prime targets for attackers due to weak governance and unknown privilege levels and access pathways. During assessments, security teams regularly identify millions of access points managed by non-human identities, often created programmatically without proper oversight.

Attackers can use any ID, including NHIs, to establish an entry point in the system and escalate privileges, leading to deeper penetration into networks. For instance, during the Target breach of 2016, it was later discovered that attackers phished third-party credentials from an HVAC vendor to gain initial access, allowing them to gain initial access to Target's network. While these credentials were not directly involved in accessing or siphoning credit card data, they instead served as a foothold for the attackers to move laterally within the network.

This is not uncommon. Organizations may have thousands or even millions of orphaned and ungoverned NHIs, posing significant security risks. In the case of one recent client, for example, CDW’s IAM team discovered nearly 26,000,000 forms of access along with 2,000,000 distinct identities just in the service account space with only one third of the organizational estate inventoried. This lack of oversight creates significant risks, as orphaned and ungoverned identities can be exploited without detection.

Other risks are:

• Credential exposure: Hardcoded private keys or API tokens are a frequent vulnerability, providing persistent access if exposed.

• Privilege escalation: Over time, machine identities often acquire unnecessary permissions, enabling lateral movement.

• Shadow IT: Developers and admins frequently create unmanaged identities to address immediate needs, bypassing governance processes.

• Operational disruptions: Expired certificates, rotated credentials and authentication failures can cause system outages and compliance penalties.

Securing machine identities requires extending traditional IAM principles to non-human entities while considering their specific operational needs. This includes:

• Automated discovery and inventory: Continuous discovery tools can identify machine identities across on-premises, cloud and hybrid environments, cataloging ownership, types and access patterns.

• Lifecycle management: Machine identities require automated provisioning, rotation and deprovisioning processes tied to application deployments and infrastructure changes.

• Policy enforcement: Apply security policies consistently across all identities, including expiration controls, access reviews and automated remediation.

• Monitoring and auditing: Real-time monitoring of identity usage can help detect unusual behavior and potential compromises. Integrating with security information and event management (SIEM) systems enables threat correlation across multiple identity types.

• Centralized vaulting: Secure storage of sensitive credentials eliminates hardcoded secrets and enables automated credential rotation.

Whether it’s unmanaged NHIs or third-party access risks, a comprehensive identity governance and administration (IGA) strategy is essential to address governance gaps while managing both human and non-human identities. Without this foundational layer, efforts to manage complex machine identity ecosystems will inevitably lead to vulnerabilities and operational inefficiencies.

Establishing a strong governance foundation before tackling specialized machine identity categories will ensure that all machine identities are properly vetted, provisioned, maintained and deprovisioned, minimizing the attack surface in the process. Your governance strategy should define comprehensive policies, roles and processes for identity lifecycle management, access controls and compliance. It should also include classifying all machine identities, understanding their purpose and mapping their access privileges across the entire IT environment.

From there, it’s important to prioritize the most high-risk areas of your business. For most organizations, this means securing service accounts first, as they are often the most vulnerable and widespread.

The next step is to implement security controls across environments. Because hybrid or multi-cloud deployments can dramatically increase the number of machine identities, advanced tools like CIEM may be necessary to manage entitlements across platforms. CIEM helps control user access and permissions in multi-cloud environments, providing unified visibility and governance across multiple cloud providers while ensuring consistent security policies.

Finally, real-time continuous monitoring will help detect any anomalies within the machine identity landscape, helping to effectively enforce all identity policies.

Of course, no matter how comprehensive your machine identity management strategy is today, the rise of AI agents and other NHIs will ultimately require evolving governance frameworks. While agentic AI is still considered “emerging technology,” it’s important to stay flexible and adopt new AI security standards as they emerge to manage these evolving identity types.

Building a secure machine identity management strategy has never been more important — especially as technology evolves and identities become more distributed every day. By establishing comprehensive governance practices today, your organization will be better equipped to adopt emerging technologies without compromising your identity security strategy tomorrow.