Non-Human Identity Is the Fastest-Growing Attack Surface, and AI Agents Will Accelerate It
If you ask most security leaders where identity risk lives, you will hear answers like phishing, MFA gaps, stale access, or excessive admin rights. All true. But there is a growing blind spot hiding in plain sight: the identities that are not people, running applications, powering microservices, keeping integrations alive. And now, AI agents that can take actions on behalf of users and teams.
These identities do not take security awareness training. They do not get tired and click on problematic links. They do not “log in” as humans do. And because they often sit behind the scenes, they avoid the spotlight until something goes wrong. The hard part is not that non-human identities exist. It’s that they scale faster than most governance models, and often carry the most consequential privileges in the environment.
A Practical Definition: What “Non-Human Identity” Means
Non-human identities (NHIs) are identities used by machines, workloads, applications, or automation to authenticate and act within your systems. Examples include:
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Service accounts.
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Workload identities for containers, serverless functions, and microservices.
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API keys, tokens, and OAuth clients for system-to-system calls.
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Certificates used for trust, signing, and secure integrations.
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CI/CD identities that build, deploy, and change production environments.
And now, add a rapidly emerging category:
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AI agent identities, including enterprise-managed agents, user agents, and undeclared “anonymous” agent activity.
The common thread is simple. These identities authenticate, they are authorized, and they can do things that matter.
Why This is Accelerating Right Now
Non-human identity is not new. What is new is the speed and scale of change. Across many enterprises, automation, cloud adoption, and AI agent deployment are accelerating. At the same time, the number and privilege of non-human identities continue to grow faster than most security programs can govern.
A few drivers are showing up again and again:
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The machine attack surface is exploding
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Modernization creates machine identities everywhere. Unmanaged service accounts, keys, and certificates can create high-impact breach paths.
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Credential theft and token abuse are direct paths to escalation
Stolen secrets and session tokens are a primary way attackers move laterally and escalate privilege. Wherever rotation, vaulting, and monitoring are weak, risk concentrates fast. -
Over-privilege creates hidden access paths
Enterprises accumulate excessive machine permissions and orphaned identities over time. Toxic access combinations and least-privilege gaps expand the blast radius. -
Hybrid complexity drives policy drift
Identity controls spread across Entra and AD, cloud IAM, applications, CI/CD, and secrets stores drift away from secure baselines. The result is posture gaps and misconfigurations across platforms. -
AI agents are being adopted before guardrails exist
As copilots and autonomous agents gain tool access, the organization needs controls that prevent prompt injection, exfiltration, and unintended actions.
The Uncomfortable Truth: Machines are Often Easier to Compromise than Humans
Human identity programs have matured in many places. MFA coverage improves. Conditional access becomes common. Review cycles exist. And step-up controls are increasingly standard.
Machine identity programs often lag behind because they are harder to see, harder to rotate, and harder to attribute to a clear owner.
Why Agents Expand the Non-Human Identity Problem
Most enterprise identity programs have historically managed two main identity populations: human identities and traditional non-human identities. Each has a distinct risk profile.
Human identities tend to be lower privilege (in a mature program), but their activity is inherently variable. People browse the web, open email attachments, click links, and make unpredictable decisions. The security challenge is variability and exposure, which is why controls like phishing resistance, conditional access, device posture, and user behavior analytics became central.
Traditional non-human identities have typically been the opposite. They are often higher privilege because they power applications, integrations, and automation. But their activity is usually low variability. A workload identity calls the same APIs. A service account runs the same job. A pipeline deploys the same patterns. The security challenge is privilege and credential protection, but the predictability of behavior makes it easier to baseline and detect anomalies.
Non-deterministic agents combine the worst parts of both. They are non-human identities that often receive meaningful privileges and tool access, and they can exhibit highly variable behavior because their actions depend on non-deterministic models, variable context, and changing conditions.
In practice, that means enterprises can end up with identities that are:
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High privilege, because they can query systems, take operational actions, and chain tools together.
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High variability, because what they do can differ from run to run, even with similar inputs.
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Harder to baseline, because “expected behavior” is not a narrow script anymore.
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More exposed to untrusted inputs, because agents can be influenced by content they read, tickets they parse, emails they summarize, or web pages they ingest.
This expands the non-human identity problem in two ways.
First, it increases the likelihood of unintended actions. Traditional automation fails in predictable ways. Agents can fail in surprising ways, including taking an action based on incomplete context or maliciously crafted inputs.
Second, it changes what “least privilege” needs to mean. With a conventional NHI, you can scope the identity to a small set of APIs and a small set of resources and call it done. With agents, the risk is not just what the identity can access, but what the identity can decide to do with that access across tools and systems.
Agents Create a New Accountability Gap
Agents also introduce a third problem that identity programs have not historically had to solve: accountability at scale.
In traditional models, accountability was comparatively clear:
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For human identities, the person is accountable for the actions they are authorized to perform.
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For traditional non-human identities, accountability usually maps to the system owner, architect, or developer who designed and deployed the automation.
Non-deterministic agents disrupt that clarity. An agent may operate on behalf of a user, a team, or an entire function. It may take actions based on mixed context from multiple sources. It may behave in unexpected ways. And it may use shared tools, shared integrations, or shared service identities that blur who truly owned the decision.
That is why enterprises adopting agents are increasingly pairing identity and access controls with new operating processes, such as clearer agent ownership models, explicit “on behalf of” policies, approval boundaries for high-impact actions, stronger audit trails that tie agent activity to initiating context, and defined escalation and kill-switch procedures.
In short, agents do not just expand the NHI surface area. They expand the governance surface area too.
What to do about it?
Modern non-human identity (NHI) risk management is a two-part problem: building a durable program (governance, ownership, policy, operations) and selecting the right solutions to execute that program at enterprise scale.
Build a Machine-and-Agent Identity Program Layer
The practical solution is not just “buy a tool.” It is to treat machine and agent identity as a first-class identity population with its own operating model. Start by making ownership non-negotiable: every service account, token, workload identity, and agent must map to a named business or technical owner and a clear purpose. Then make lifecycle controls normal, not heroic. New NHIs and agents should be created through a standard intake (ticket or IaC), scoped to least privilege, and issued time-bound credentials whenever possible. Existing NHIs should be discovered, risk-ranked, assigned owners, and brought under the same lifecycle discipline: rotation, review, and decommissioning when unused.
Agents require one additional layer: decision and delegation governance. Because agents can act “on behalf of” users and teams, define explicit “on behalf of” policies and approval boundaries for high-impact actions. In practice, that means carving the world into actions agents can do autonomously, actions that require a human approval step, and actions that are simply off-limits. Pair this with auditability that preserves initiating context (who or what triggered the agent, what tools it used, what data it accessed) and operational kill-switch procedures so you can pause an agent quickly when something looks wrong, without having to disable half the environment.
How Technology Supports the Program: Map Tools to Outcomes, not Features
Most solutions in the NHI and agent identity market support one or more of five program outcomes:
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Discovery and inventory
Continuously identify service accounts, tokens, certificates, workload identities, and agent activity across cloud and SaaS so you can see what you actually have. -
Posture and lifecycle management
Tie each identity to an owner, assess over-privilege and drift, and operationalize review, rotation, and decommissioning. -
Secrets management and elimination
Reduce long-lived credentials through vaulting, rotation automation, or secretless patterns that replace static secrets with short-lived, just-in-time credentials. -
Runtime access control and enforcement
Put policy in the execution path so workloads and agents get narrowly scoped access at the moment of use, and dangerous actions are blocked or routed for approval. -
Monitoring and threat detection
Baseline normal behavior for machine identities and agents, detect anomalies, and feed response workflows.
The key is sequencing. Use discovery to establish scope, posture tools to reduce standing risk, secret reduction to shrink credential theft paths, and runtime enforcement to safely expand agent capability without expanding blast radius. When those layers align, you get what leaders actually want: faster automation and agent adoption with less fear and fewer hidden attack paths.
The Takeaway: Treat Machine Identity as a First-Class Security Program
The shift underway is bigger than “more service accounts.” Automation is core infrastructure. Machine identities are becoming the dominant identity population. And AI agents will multiply operational actions fast.
Organizations that make non-human identity a first-class discipline reduce breach pathways, shrink blast radius, and move faster with less fear. The ones that do not will keep discovering, often the hard way, that the easiest way into a modern environment is through an identity nobody was watching.
If you’re curious to learn more or want to stay on top of the latest developments in Innovation, feel free to reach out to us at innovation@trace3.com.
[1] https://hbr.org/2025/09/ai-generated-workslop-is-destroying-productivity
Justin “Hutch” Hutchens is an Innovation Principal at Trace3 and a leading voice in cybersecurity, risk management, and artificial intelligence. He is the author of “The Language of Deception: Weaponizing Next Generation AI,” a book focused on the adversarial risks of emerging AI technology. He is also a co-host of The Cyber Cognition Podcast, a show that explores the frontier of technological advancement and seeks to understand how cutting-edge technologies will transform our world. Hutch is a veteran of the United States Air Force, holds a Master’s degree in information systems, and routinely speaks at seminars, universities, and major global technology conferences.