Machine identities are overtaking human users in identity security

At a glance

What this is: This is a vendor-published industry trends report showing that machine identities, AI-driven attacks, and identity complexity are reshaping the security baseline.

Why it matters: It matters because IAM, IGA, PAM, and cloud security teams now need controls that govern machine-scale access as rigorously as human access, or risk losing visibility and response speed.

By the numbers:

• The report benchmarks responses from 512 identity and security professionals.
• The report says 43 percent of incidents could have been prevented by specific identity controls.

👉 Read Bravura Security's 2025 identity security trends report

Context

Machine identities are now a core identity governance problem, not a niche infrastructure concern. As enterprises add more automation, APIs, service accounts, and AI-driven workloads, the attack surface grows faster than traditional identity programmes can inventory or review.

This report frames that shift through current survey data and practitioner priorities. The central issue is not whether identity security matters, but whether existing IAM, PAM, and governance models can keep up when the majority of access is no longer human-centred.

The question for identity leaders is straightforward: can your programme see, classify, and control non-human access before adversaries or internal sprawl turn it into a breach path?

Key questions

Q: How should security teams govern machine identities at enterprise scale?

A: Start with discovery, ownership, and lifecycle control. Security teams need a complete inventory of service accounts, API keys, tokens, and certificates, plus clear accountability for each identity. Then enforce rotation, expiry, and least privilege based on workload need, not human convenience. Without that baseline, governance becomes guesswork and review cycles will miss the identities that matter most.

Q: Why do machine identities increase identity risk compared with human accounts?

A: Machine identities increase risk because they are numerous, long-lived, and often embedded in applications or pipelines where they are hard to see. They can accumulate privilege quietly and remain active after the original use case changes. That combination raises blast radius, weakens accountability, and makes detection and remediation slower than with human access.

Q: What do teams get wrong when reviewing non-human access?

A: The most common mistake is using human-centric review processes for machine identities. A service account does not need a recertification conversation, but it does need evidence that the workload still exists, the privilege is still justified, and the identity is still owned. If the review cannot answer those questions, the control is not actually governing the risk.

Q: Should organisations prioritise Zero Trust for machine identities before broader IAM changes?

A: Yes, if machine identities are already driving a large share of your access surface. Zero Trust is most useful when it constrains the identities that can move laterally, call services, or carry long-lived privileges. If those identities remain over-privileged or unmanaged, broader IAM improvements will still leave the highest-risk access paths exposed.

Technical breakdown

Why machine identity sprawl breaks traditional IAM visibility

Machine identities include service accounts, API keys, tokens, certificates, and workload credentials. Unlike humans, they multiply quickly across cloud platforms, CI/CD pipelines, and application integrations, often without a clear owner or lifecycle record. That creates a visibility problem first and an enforcement problem second: if you cannot reliably discover the identity, you cannot govern its privilege, rotation, or offboarding. Traditional IAM programmes were built around people, apps, and periodic reviews. Machine-scale identity changes the operating model because access is distributed, persistent, and often embedded directly into systems.

Practical implication: build complete inventory and ownership mapping for machine identities before trying to tighten policy.

Why AI-driven identity attacks increase the speed of exposure

AI-assisted attackers compress reconnaissance, credential harvesting, and exploitation into shorter decision cycles. That matters because identity controls depend on detection, review, and response windows that were designed for slower human operations. When exposed secrets, misconfigured access, or weak governance are found faster, the defender's time to act shrinks. This is especially dangerous in environments where credentials are long-lived or reused across systems, because a single exposed identity can unlock multiple services. In practice, the issue is not just volume of attacks, but the speed at which identity weaknesses are converted into usable access.

Practical implication: reduce standing credential exposure and shorten response paths for any identity that can be used non-interactively.

How zero trust changes when the identity is not a person

Zero Trust for machine identities is less about user login flows and more about continuous verification of workload, service, and API trust. The report's emphasis on near universal Zero Trust adoption reflects a broader reality: policy language alone does not stop misuse if the underlying identity state is over-privileged or poorly governed. For non-human identities, trust must be tied to workload context, issuer integrity, and runtime access scope. That means identity assurance, access boundary design, and least privilege need to be enforced at the machine layer, not only at the network perimeter.

Practical implication: align Zero Trust controls to workload identity, secret governance, and runtime authorization, not just employee access.

Breaches seen in the wild

• Cisco DevHub NHI breach — IntelBroker exploited exposed Cisco credentials, API tokens and keys in DevHub.
• DeepSeek breach — DeepSeek breach exposed 1M+ log lines and sensitive secret keys.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Machine identity growth is now the pacing factor in identity security programmes. The report reinforces a structural shift that many teams still understate: access no longer scales primarily through users, it scales through service accounts, tokens, and machine-to-machine integration. That means inventory, ownership, and lifecycle governance become the real control plane. The practical conclusion is that identity programmes that still centre human users will continue to miss the largest part of their exposure.

Identity governance is becoming a machine-scale control problem, not a login problem. IAM programmes were designed around authentication events and user administration workflows, but machine identities rarely behave like people. They are provisioned into pipelines, copied across environments, and left active long after the original use case changes. The implication is that recertification, offboarding, and privilege review must be redesigned around non-human ownership and system context.

43 percent of preventable incidents is the signal that control effectiveness, not control count, is the issue. The report's claim that specific identity controls could have prevented a large share of incidents points to a familiar governance failure: organisations often add tools without proving that ownership, visibility, and remediation are actually working. In NHI governance, the problem is rarely the absence of a control category. The problem is that the control does not reach the identities that matter most.

Zero Trust only becomes meaningful when it includes non-human identities in the trust boundary. Teams often adopt Zero Trust language while leaving machine credentials outside the same rigor applied to human access. That creates a false sense of maturity, because the identities with the broadest blast radius can still be over-privileged, unowned, or hard to rotate. Practitioners should treat machine identity governance as a prerequisite for real Zero Trust rather than a downstream add-on.

Identity complexity is now a programme risk, not just an operational inconvenience. As environments add more automation, the number of identity objects, trust relationships, and review points expands faster than most governance teams can process. The result is slower remediation, weaker accountability, and more drift between policy and reality. The conclusion for practitioners is clear: simplification, ownership, and lifecycle discipline are now security controls, not administrative preferences.

From our research:

• 80% of identity breaches involved compromised non-human identities such as service accounts and API keys, according to Ultimate Guide to NHIs.
• Only 5.7% of organisations have full visibility into their service accounts, which shows why machine identity inventory remains a structural weakness.
• The 52 NHI Breaches Analysis shows how those visibility gaps turn into real incidents when ownership and lifecycle controls fail.

What this signals

Machine identity governance is moving from an inventory problem to a resilience problem. As automation expands, the teams that can tie each identity to an owner, lifecycle state, and review path will recover faster from drift and exposure. The organisations that cannot do that will keep discovering risk only after it has already affected multiple systems.

The report's direction of travel also suggests that identity programmes will be judged less by tool count and more by how well they reduce hidden access. That shifts the conversation toward measurable control outcomes such as fewer unmanaged secrets, shorter remediation cycles, and clearer accountability for non-human access.

For practitioners, the next maturity step is not adding another point product. It is proving that machine identities are governed with the same discipline applied to privileged human access, so Zero Trust and PAM do not stop at the user boundary.

For practitioners

• Inventory machine identities by owner and use case Build a living register of service accounts, API keys, tokens, and certificates with business owner, technical owner, system dependency, and renewal path. If no accountable owner exists, treat the identity as unmanaged until that is corrected.
• Reduce long-lived credential exposure Prioritise the identities most likely to be copied into code, config files, and automation jobs, then move them into managed secrets workflows with explicit rotation and expiry. The goal is to shrink the window in which hidden credentials can be abused.
• Rework recertification for non-human access Do not reuse human access review templates for machine accounts. Review whether the identity is still needed, whether its privileges match the current workload, and whether the provisioning path still reflects the active system architecture.
• Tie Zero Trust to runtime identity state Validate workload trust, issuer provenance, and access scope at the point of use rather than assuming a one-time approval is enough. This is where Zero Trust becomes operational for systems rather than theoretical for people.

Key takeaways

• Machine identities have become the dominant governance challenge in many enterprises because they expand faster than human identity controls were built to handle.
• The report's 43 percent preventable-incident figure points to a control effectiveness problem, not simply a tooling gap.
• Security teams should focus on ownership, lifecycle, and runtime scope for non-human access before the attack surface becomes unmanageable.

Key terms

• Machine Identity: A machine identity is a non-human credentialed entity used by software, services, or infrastructure to authenticate and authorise actions. In practice it includes service accounts, API keys, tokens, and certificates. These identities need ownership, lifecycle management, and privilege boundaries just as much as human accounts do.
• Identity Sprawl: Identity sprawl is the uncontrolled growth of identities, credentials, and trust relationships across environments. It becomes a security issue when teams can no longer reliably inventory, own, review, or retire access. For machine identities, sprawl often hides in pipelines, cloud services, and application-to-application connections.
• Lifecycle Governance: Lifecycle governance is the discipline of managing identity from creation through review, change, and retirement. For non-human identities, it means tying access to a workload or business function, then revoking it when that function ends. Strong lifecycle governance reduces standing access and prevents stale credentials from lingering.
• Standing Privilege: Standing privilege is access that remains available all the time instead of being granted only when needed. For non-human identities, it is especially risky because unattended credentials can be reused, copied, or abused without a human present. Reducing standing privilege narrows the blast radius of compromise.

Deepen your knowledge

Machine identity governance and lifecycle control are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building a governance programme from the same starting point, it is worth exploring.

This post draws on content published by Bravura Security: the 2025 IDSA Trends in Identity Security Report. Read the original.