As enterprises increasingly turn to automated systems, artificial intelligence, and cloud services, a new security challenge emerges that's flying under the radar of many: the exponential growth of machine, or non-human, identities (NHIs). These digital identities—ranging from service accounts and API tokens to AI agents—are multiplying at an unprecedented rate, creating both operational opportunities and significant security risks.
The scale of this challenge is staggering. Research firm Gartner estimates there are approximately 45 machine identities for every human identity, with industry projections only pointing to increased machine identity growth. Unlike human identities, which grow linearly as the workforce expands, machine identities proliferate exponentially as organizations embrace automation, microservices architectures, and AI-powered tools. Every API call, automated process, and AI agent demands its own identity to authenticate and access resources.
This raises new identity governance challenges.
"There's definitely a different governance process for non-human identities," explained Jared Atkinson, chief technology officer at identity attack path software and services provider SpecterOps. "You need to be more deliberate about how you govern the use and the lifecycle of those non-human identities."
That advice is crucial because machine identities operate without human oversight in their day-to-day functions, making them both powerful and potentially dangerous if compromised or misconfigured.
Why Attackers Target Machine Identities
From an attacker's perspective, machine identities are attractive. Security researchers have observed that cybercriminals often prefer targeting service accounts and automated systems over human users for several strategic reasons. "Attackers don't care if they're targeting a human or machine identity—in fact, they may prefer to target a machine identity because it often has more access than a person," added Atkinson. "You have less likelihood of accidentally exposing yourself to a real human user," he said.
This preference stems from a practical reality: attackers are frequently detected not through sophisticated security tools, but when they accidentally disrupt a human user's standard workflow. Machine identities eliminate this risk while often providing that broader system access, Atkinson noted.
Machine identities typically operate with elevated privileges, allowing them to perform their automated functions efficiently. This creates what security professionals call an "identity snowball attack," where compromising one machine identity can provide access to multiple systems and resources.
The Cloud Identity Challenge
The shift to cloud computing has fundamentally changed how organizations think about identity management, and as a senior analyst at Forrester, put it: "Cloud is all identity—how you build your entire cloud environment is wrapped around identity," he said.
This identity-centric approach to cloud infrastructure means that vulnerabilities in identity management can have far-reaching consequences. Unlike traditional network security models that relied on perimeter defenses, cloud environments depend entirely on proper identity and access management. And the complexity increases as organizations operate across multiple cloud platforms and services, where each platform may have its own identity management system, creating a fragmented landscape where machine identities proliferate without centralized oversight or consistent security policies.
Emerging Risks with Agentic AI
The rise of agentic AI—artificial intelligence systems that can make autonomous decisions and take actions—increases machine identity risks. "Agentic AI is growing into a huge problem because AI agents are going to have identities, or maybe several identities, that allow it to do all kinds of things," warned Atkinson. "And if you give them the privilege to do something, given enough time, they're going to do it eventually."
The challenge is particularly acute because AI agents can operate at machine speed and scale, potentially causing widespread damage before human operators can intervene. Organizations must implement strict least-privilege principles for these agentic AI systems.
Microsoft Copilot highlights the challenges of managing machine identities. The AI assistant operates within the context of the user employing it, which means it can access decades of SharePoint data that users may have forgotten about or that should have been retired under proper data retention policies. "Many organizations are afraid to turn on Microsoft Copilot because of all of that SharePoint data, because Copilot is going to go through and index it," explained Brian Golumbeck, director, strategy and risk management at cybersecurity services provider Optiv.
Best Practices for Machine Identity Management
Security experts recommend several key strategies for managing machine identities effectively:
Implement NHI Governance: Just as human identities require proactive lifecycle management, machine identities also necessitate proactive management. Organizations should establish clear processes for creating, monitoring, and retiring machine identities. This includes regular audits to identify orphaned or unnecessary accounts that may have accumulated over time.
Embrace Zero-Trust Principles: The zero-trust security model is particularly relevant for machine identities. Rather than relying on identities based on location or origin, organizations should continuously verify and validate every access request, regardless of whether it originates from a human or machine identity.
Use Short-Lived Tokens: Where possible, implement token-based authentication with short time-to-live (TTL) values. This limits the window of opportunity in the event of compromised credentials and reduces the risk of long-term unauthorized access.
Monitor and Audit Continuously: Machine identities should be subject to continuous monitoring and regular access reviews. Organizations need visibility into what resources each machine identity can access and how those permissions are being used in practice.
Implement Contextual Controls: Advanced identity management systems can apply contextual controls that consider factors such as the requesting system's behavior patterns, the sensitivity of the requested resources, and the time and location of access requests.
As enterprises continue to embrace automation and AI, the challenge of managing machine identities will only grow. And organizations that proactively address machine identity management today will be better positioned to leverage emerging technologies, such as agentic AI, securely. Those that treat machine identities as an afterthought may find themselves vulnerable to increased level of attacks that exploit the very automation systems designed to improve efficiency and security.
