What Is a Non-Human Identity? NHI Meaning and Security

What Does NHI Mean in Cybersecurity? 

NHI stands for non-human identity. In cybersecurity, a non-human identity is any digital identity used by something other than an individual person to authenticate, access systems, or perform actions. A non-human identity can represent a service account, API key, OAuth token, workload, cloud resource, device, automation script, bot, certificate, service principal, MCP server, or AI agent. 

The National Institute of Standards and Technology (NIST) uses a related concept of a non-person entity: an entity with a digital identity that acts in cyberspace but isn’t a human user. It’s a useful frame because it keeps the focus on identity, access, and accountability, whatever the actor is: an app, a workload, a token, a device, or an agent. 

Security teams should be able to answer six questions about every NHI: who owns it, why it exists, what it can access, what behavior is expected, what data it can touch, and how its actions are audited. 

Examples of Non-Human Identities 

Non-human identities appear anywhere systems need to authenticate, connect, or act without a person signing in directly. Common examples include: 

• Service accounts used by applications, databases, infrastructure jobs, and SaaS integrations. 

• API keys that allow systems to exchange data or trigger actions. 

• OAuth tokens and refresh tokens that grant delegated access. 

• Service principals and cloud roles used by workloads and resources. 

• Containers, serverless functions, Kubernetes workloads, and virtual machines. 

• Automation scripts, CI/CD jobs, bots, and scheduled tasks. 

• Devices, certificates, IoT systems, and other machine identities. 

• MCP servers and tool connectors that expose actions to apps or agents. 

• AI agents that authenticate, call tools, retrieve data, or act on behalf of a user or business process. 

The category is expanding as organizations connect more cloud services, SaaS apps, automation frameworks, and AI-enabled workflows. 

Human Identities vs. Non-Human Identities 

Human and non-human identities both need authentication, authorization, monitoring, and lifecycle governance. Where they differ is ownership, behavior, accountability, and how risk shows up. 

At a glance: non-human identities are owned by teams instead of people, behave in automated or agent-driven patterns instead of variable human judgment, and carry different risks — stale access and unaudited automation instead of account takeover and insider misuse. 

A human identity usually maps to a known person, role, manager, device, location, and employment status. A non-human identity can outlive the project that created it, run in the background, and have no obvious owner. That's why ownership, lifecycle governance, and monitoring are as important as authentication does.

Why Non-Human Identities Create Security Risk

Non-human identity security matters because these identities often hold persistent access to sensitive systems and data. They can run without interactive login, MFA, or direct human review. If an NHI is compromised or misconfigured, attackers can use it for durable access, lateral movement, privilege misuse, and data exposure.

Common NHI risks include:

• Overprivileged access granted for convenience or broad compatibility.

• Stale credentials that stay active after a project, app, or owner changes.

• Weak ownership that makes access reviews inconsistent or incomplete.

• Secrets exposure in code repositories, tickets, logs, chat tools, or local files.

• Hard-to-audit activity across APIs, SaaS apps, cloud resources, and data stores.

• Lateral movement when one identity can call many services.

• Data exposure when an NHI can read, move, summarize, or export sensitive content.

Proofpoint's 2025 Data Security Landscape Report found that 85% of organizations experienced a data loss incident in the past year, and that 1% of users were responsible for 76% of data loss events. That second finding describes human risk, not non-human identity risk specifically, but it points to the same underlying lesson: identity activity and data exposure need to be governed together, whoever or whatever is behind the identity.

How AI Agents Are Changing NHI Security

AI agents are a new and increasingly common class of non-human identity. Like traditional service accounts, they authenticate, access systems, and perform actions. Unlike most traditional service accounts, they can also interpret goals, reason over context, choose tools, retrieve data, and take multi-step actions across applications.

That changes the risk model for AI agent identity. An agent can pass a permission check while still exceeding the intent of the person or process it's acting on behalf of. It might retrieve more data than it needs, call the wrong tool, chain actions across systems, or generate an output that exposes sensitive information. Identity controls are necessary, but on their own, they aren't enough to govern what an agent actually does.

Proofpoint's 2026 AI and Human Risk Landscape Report found that 87% of organizations have AI assistants deployed beyond the pilot stage, and 76% are piloting or rolling out autonomous agents. It also found that 52% of organizations aren't fully confident their controls would detect a compromised AI agent, and 42% have already seen a suspicious or confirmed AI-related incident. Together, those numbers make AI agent identity a practical non-human identity security issue today, not a future edge case.

Learn more about what an AI agent is and what agentic AI security means, or see how Agentic AI Security applies these controls in practice.

Non-Human Identity Management vs. Machine Identity Management

Machine identity and non-human identity overlap, but they aren't the same thing. Machine identity security typically focuses on identities used by devices, workloads, services, and certificates. Non-human identity is broader: it includes machine identities, plus service accounts, API keys, OAuth tokens, service principals, bots, automation, MCP servers, SaaS integrations, and AI agents.

That broader definition matters because modern access doesn't always come from a traditional machine. A SaaS integration, a cloud workflow, or an agentic AI process can behave like a trusted actor even when no employee is clicking through each step directly.

How to Manage and Secure Non-Human Identities

A strong non-human identity management program combines identity governance, secrets management, data access controls, and runtime visibility. Start with a complete inventory, then hold each identity accountable throughout its lifecycle.

Practical controls include:

• Inventory every NHI across SaaS, cloud, infrastructure, code, and AI tools.

• Assign a business owner and a technical owner for each identity.

• Document its purpose, systems accessed, data accessed, and expected behavior.

• Apply least privilege and remove broad standing access.

• Rotate credentials and eliminate long-lived secrets where possible.

• Use secrets management instead of embedding keys in code or collaboration tools.

• Review access regularly and retire stale identities.

• Monitor behavior for unusual calls, tool use, data access, and privilege changes.

• Govern access to sensitive data, especially where agents can retrieve or summarize content.

• Maintain audit trails that connect the identity, originating user, action, data, tool use, and outcome.

• For AI agents, extend these controls to include intent validation, tool-use monitoring, data access governance, and auditability. The question isn't only whether an identity is allowed to act — it's also whether that action matches the user's intent and the organization's data protection rules.

See the Unified AI Security Platform and what AI data security means for more on how these controls come together.

How Non-Human Identities Affect Data Security

Non-human identities often reach sensitive data through applications, APIs, cloud services, collaboration tools, repositories, and AI workflows. Risk increases when an NHI has broad access to data that's overshared, abandoned, unclassified, or poorly governed. AI adds urgency here because copilots and agents can retrieve, summarize, transform, or move data faster than traditional user workflows allow.

Proofpoint's 2025 Data Security Landscape Report found that 1% of users were responsible for 76% of data loss events — a finding about human behavior, not a claim about non-human identities specifically, but a reminder that behavior-aware controls matter regardless of who or what is acting. Proofpoint's AI data exposure research also found that 27% of data volumes across AWS, Microsoft Azure, and Google Cloud Platform are abandoned. Abandoned data is easy to overlook and, once it's reachable by an AI tool or an automated identity, easy to expose.

Learn more in 5 Unseen Ways AI Exposes Your Data and see how Data Security for AI helps close that gap.

How Proofpoint Helps Reduce NHI and AI-Agent Risk

Proofpoint helps organizations reduce non-human identity and AI-agent risk by connecting identity, behavior, data, and agent activity into one picture instead of four separate ones. Identity Security helps teams understand identity exposure and risky access paths. Agentic AI Security and the broader AI Security platform help organizations govern AI use, agent behavior, tool access, and auditability. And Data Security for AI helps protect sensitive information as users, applications, and agents interact with enterprise data.

The goal is a security model that answers more than “was this identity allowed to act?” It should also answer who or what owns the identity, what it intended to do, what data it touched, which tools it used, and whether the action can be explained after the fact — for every person, and every agent.

Read the Agent Integrity Framework to see how Proofpoint applies these principles to AI agents specifically.