Identity has always been about trust. In the Middle Ages, kings and merchants used wax seals to authenticate messages. Then came handwritten signatures, passports, and government-issued IDs — all ways to verify and manage human identity. As the digital era unfolded, identity governance expanded beyond employees to include contractors, business partners, and other third parties, ensuring secure access across an increasingly interconnected ecosystem. But as organizations embraced automation, a new challenge emerged: securing and governing machine identities.
AI-generated content may be incorrect. Machine identities aren’t new. IT teams have relied on service accounts, API keys, and certificates for decades to enable machine-to-machine communication. What is new is the sheer volume, velocity, and complexity of managing them. As automation, cloud computing, and AI-driven workflows accelerate, machine identities have proliferated beyond what traditional identity security strategies can handle.
The problem isn’t just growth—it’s the lack of governance structures to keep up with it. Without a clear strategy, organizations find themselves at a crossroads—either treat machine identities with the same governance and security as human identities or ignore them until a breach forces action.
The emergence of machine identities
In the early days of IT, machines were simple. A handful of service accounts ran background processes in on-premises data centers. IT teams manually created these accounts and often stored credentials in plaintext or shared folders. Governance wasn’t a priority because there were fewer moving parts—machines weren’t spinning up and down dynamically, and most infrastructure was confined to on-prem environments.
Then came virtualization, cloud computing, and containerization. Suddenly, machine identities weren’t just a handful of accounts—they were hundreds or thousands, created on the fly to support ephemeral workloads. Microservices architectures made it common for one business process to involve dozens of machine identities interacting across multiple environments. With this explosion of automation and connectivity, identity management strategies designed for human users proved inadequate.
Organizations now rely on machine identities to run mission-critical applications, integrate systems, and execute automated workflows. But without structured governance, these digital entities become a security blind spot. Research shows that:
- 72% of identity professionals say machine identities are harder to manage than human ones.
- 75% of machine accounts lack a designated owner.
- 83% of enterprises experienced a machine account takeover in the past year.
These statistics highlight a growing identity crisis. Without clear ownership and governance, machine identities remain vulnerable to exploitation, privilege creep, and compliance failures.
Neil McGlennon, Global Field CTO at SailPoint, describes it best at a recent Navigate event: “Customers came to us asking, ‘What do we do with all this?’ They see the sprawling complexity—bots, RPAs, service accounts, workloads—and realize that their automation success has created a challenge they’re struggling to contain.”
The complexity grows as automation feeds into itself. Workloads spin up new workloads. Bots manage other bots. And yet, there’s no structured lifecycle for machine identities like there is for human employees.
Lessons from human identity for machine identity security
If we’ve learned anything from decades of human identity management, it’s that governance must be proactive, not reactive. Organizations need structured policies that:
- Enable centralized governance: Treat machine identities as first-class citizens, distinct from human accounts but governed under a unified security model.
- Automate discovery and classification: Reduce reliance on manual processes by using AI-driven analysis to identify and group machine identities.
- Apply zero trust principles: Enforce least privilege access, ensuring machine identities only have the permissions they need.
- Establish ownership and lifecycle policies: Assign human owners to machine identities and implement structured decommissioning processes.
Organizations that fail to establish these foundational controls often find themselves dealing with:
- Shadow IT risks: Unmonitored machine accounts can proliferate across environments, leading to security gaps.
- Compliance failures: Auditors increasingly expect machine identities to be governed with the same rigor as human accounts.
- Operational inefficiencies: Without automation, teams spend excessive time manually sorting, categorizing, and managing machine identities.
- Security gaps: Without governance, attackers can exploit machine identities as attack vectors, using compromised service accounts and API keys to move laterally across environments undetected.
SailPoint Machine Identity Security (MIS) is designed to solve these exact problems. MIS eliminates the need for manual machine identity management by automating discovery, classification, and ownership assignment. By enforcing security policies at scale, organizations can ensure that machine identities are properly governed, reducing risk and improving overall security posture. Our system can automatically classify them, assign ownership, and enforce security policies at scale.
The cost of ignoring machine identity governance
Organizations that fail to govern machine identities are already seeing the consequences. In high-profile breaches like SolarWinds, attackers exploited poorly managed service accounts to infiltrate networks. Without governance, machine identities become a weak link in security.
Beyond the risk of breaches, organizations also face:
- Broader attack surface: Machine identities now represent the largest attack surface, outpacing human identities with the rise of microservices, containers, bots, RPAs, and automated workloads. Attackers target unmanaged service accounts, exposed API keys, weak authentication, and overprivileged access to move laterally across networks. Without proper visibility and automated lifecycle management, machine identities become a critical security blind spot.
- Excessive privilege accumulation: Machine identities often retain permissions far beyond their necessary function, increasing the attack surface and potential for privilege escalation.
- Identity sprawl: Without governance, machine identities multiply rapidly, creating confusion over which accounts are active, necessary, or orphaned.
- Costly investigations and remediation: When an attack occurs, organizations without structured machine identity governance spend significantly more time and resources identifying and mitigating the issue.
- Lack of visibility: Without a platform that consolidates machine identity management alongside human identity governance, security teams struggle to gain a unified view of identity risk.
- Regulatory penalties: As regulations tighten around identity security, unmanaged machine identities can lead to audit failures and compliance fines.
The problem isn’t going away. Some enterprises have an estimated 45 machine identities for every human user. With that level of scale, a manual approach simply doesn’t work.
The question isn’t if organizations should manage machine identities—it’s how soon they can do it effectively. The time for ad hoc, manual processes is over. Just as human identity management evolved to support growing complexity, machine identity security must follow suit.
Next steps
Want to dive deeper into machine identity security?
- Read Part 1 of this article series: What is a machine identity?
- Watch our webinar on managing machine identities at scale.
Identity security isn’t just a human problem anymore. The future belongs to those who can secure all identities—human and machine alike.
