Machine identities outnumbered humans by 82 to one in CyberArk’s 2025 Identity Security Landscape study. Some 42% held privileged or sensitive access, yet 88% of respondents said their organizations defined a “privileged user” solely in human terms. The research surveyed 2,600 cybersecurity decisionmakers at organizations with at least 500 employees.
Those findings expose a gap in how enterprises manage access. Software can have extensive permissions while receiving less scrutiny than employees. As companies automate business processes, more activity takes place through accounts that have no direct connection to an employee’s login.
Conventional identity management already supports machine accounts. However, this coverage becomes inadequate when software identities spread across cloud platforms and external services. Often, this happens without continuous ownership or review. Companies need to understand which applications can reach sensitive information and who is responsible for that access.
A machine identity is software or a device within a digital system that allows an application to authenticate and receive access according to assigned permissions. For example, an inventory service needs an identity to retrieve warehouse records.
Microsoft groups software workload and device identities under machine, or non-human, identities. Workloads include applications and scripts that communicate with other systems without someone logging in for each interaction.
Their rapid expansion is an indication of the way businesses now operate. For example, a customer order may pass through several connected services before reaching a warehouse and each of these services needs permission to perform its part of the process. Expanding automation therefore creates more access relationships for security teams to govern.
Enterprise identity programs have traditionally revolved access around employment. When a person joins a company, he or she receives permissions appropriate to their role and these are taken away from that individual when they leave the company. Human resources records allow these decisions to happen.
Software is different. Automated processes can create workloads in seconds, and temporary environments may disappear within hours. But other services continue running for years, surviving changes in staff and business ownership.
These events do not always lead to the procedures used to review employee access. Disabling a departing developer’s login may leave the service accounts they created untouched. If these accounts remain necessary, someone must take responsibility for them. Without anyone taking ownership, a functioning integration can escape review because nobody considers the permissions it has their responsibility.
Google warns that stolen service-account keys can allow authentication without the additional sign-in verifications that protect human accounts. Excessive permissions increase the potential vulnerability to attack. For example, a reporting tool may receive broader database access during development and often those permissions remain in place long after the assigned task is over. A key left in a configuration file may remain valid after the original project ends and removing the file from its current location does not invalidate copies obtained elsewhere.
OWASP’s 2025 non-human identity risk list includes improper offboarding, leaked secrets, and overprivileged accounts. These familiar failures become harder to control when credentials are distributed across applications and development environments.
Shared identities create another difficulty. If several applications use one account, investigators may see which account accessed a resource without being able to establish which application initiated the action.
AI agents add a further challenge because they can select actions as they work through a task. An agent connected to business systems may retrieve information and then use another tool to change a record.
Delegation also needs to remain traceable. When an agent acts for an employee, the company should be able to determine whose authority it is exercising. An overprivileged agent could otherwise give a user an indirect route to information they cannot access themselves.
Businesses adopting agents therefore need to examine the accounts and integrations beneath them. Existing access problems can carry into new workflows even when the AI application itself has received a security review.
The first practical task is to identify machine accounts and assign accountable owners. Each owner should understand the identity’s business purpose and review its permissions when the application changes. Retirement procedures should remove access that is no longer required.
Separate identities for different applications not only improve accountability but also make it easier to restrict permissions without disrupting unrelated services. Where supported, managed identities or workload identity federation can reduce reliance on manually stored, long-lived secrets. These approaches let workloads authenticate through a trusted platform or a configured relationship between systems. Their security still depends on correctly defined trust and access rules.
Older applications may require persistent credentials and need protected storage and a tested replacement process. Teams must understand which services depend on a credential before changing it; otherwise, routine maintenance can cause an outage and encourage future delays.
Machine identities are already an enterprise security concern. Cloud adoption and AI increase the cost of leaving them outside established controls. A useful starting point is to examine the software accounts with the broadest access and ask their owners to justify it. Where nobody can provide that explanation, the company has found a security gap it can begin closing.