Why do unmanaged service accounts and AI-related credentials create so much risk in modern environments?

Unmanaged non-human identities expand the attack surface because they often outnumber human accounts, persist longer, and escape normal governance. When credentials are scattered across cloud, servers, and code, attackers can find dormant access paths, reuse secrets, and move laterally without triggering the controls built for user accounts.

Why This Matters for Security Teams

Unmanaged service accounts and AI-related credentials are risky because they sit outside the normal controls that protect human users. They often have broad entitlements, weak ownership, and long-lived secrets that survive team changes, code deploys, and cloud migrations. That makes them ideal entry points for attackers looking for dormant access, lateral movement, or privileged automation paths.

This is not just a hygiene issue. The problem is structural: non-human identities frequently outnumber human accounts, but they are reviewed less often and rarely tied to a clear business owner. NHI Management Group’s research on the Top 10 NHI Issues highlights secret sprawl and weak lifecycle control as recurring failure modes, while the OWASP Non-Human Identity Top 10 frames unmanaged credentials as a repeatable attack surface rather than an isolated misconfiguration.

For AI-related credentials, the risk expands further because agents, pipelines, and tool-using workloads can chain actions faster than human operators can notice. In practice, many security teams encounter NHI compromise only after secrets have already been reused across environments and exposed through normal operational workflows, rather than through intentional review.

How It Works in Practice

In modern environments, unmanaged service accounts and AI credentials create risk because they are both identity and capability. A leaked API key, certificate, or token is not just a password equivalent. It can authorize workloads, invoke cloud services, retrieve data, or trigger downstream automations without a person ever logging in. That is why secret sprawl is such a persistent issue: credentials are copied into CI/CD systems, environment variables, containers, notebooks, and code repositories, then forgotten.

Good practice is evolving toward lifecycle control, short-lived access, and explicit ownership. The NHI lifecycle guidance aligns with what the NIST Cybersecurity Framework 2.0 expects in principle: identify the asset, define ownership, reduce standing access, and continuously monitor use. For AI workloads, that usually means:

• Replacing shared, static credentials with workload identity and ephemeral tokens.
• Issuing access just in time, with narrow scope and automatic expiry.
• Mapping each service account or agent credential to a business service, repository, or workflow owner.
• Logging each secret use and each privilege change so anomalous reuse can be detected quickly.

These controls matter because attackers do not need to break strong human authentication if they can steal a token that already trusts the environment. NIST controls on access enforcement and least privilege reinforce this model, but they only work when the organisation knows which non-human identities exist and where they are used. In practice, these controls tend to break down when credentials are embedded in legacy automation and rotated manually across dozens of systems because no single team can prove where each secret is active.

Common Variations and Edge Cases

Tighter credential controls often increase deployment overhead, requiring organisations to balance operational speed against reduced exposure. That tradeoff is especially visible in legacy applications, batch jobs, and multi-cloud integrations where static secrets are still the easiest way to keep production running. In those environments, guidance suggests phased migration rather than a hard cutover.

There is also no universal standard for how to govern AI agent credentials yet. Some teams treat them like conventional service accounts, but that approach can miss the autonomous nature of agents that call tools dynamically and chain actions across systems. Current guidance from the 2024 ESG Report: Managing Non-Human Identities shows how often compromised NHI exposure leads to repeated incidents, while the LLMjacking research demonstrates how quickly exposed AI-related credentials can be abused in the wild.

Security teams should treat exceptions as temporary, document the owner of every standing credential, and prioritize systems where secrets can unlock privileged data, deployment pipelines, or model tooling. The hardest cases are shared automation accounts in environments with no reliable inventory, because no one can confidently tell whether the credential is dormant, duplicated, or already abused.

Related resources from NHI Mgmt Group

• Why do static service accounts create so much breach risk in cloud environments?
• Why do unmanaged NHIs and AI agents create more risk than tracked service accounts?
• Why do personal AI accounts create so much risk in enterprise environments?
• Why do compromised service accounts and build credentials create outsized risk in supply chain environments?