In March 2025, a fintech startup discovered that an autonomous coding agent had been quietly holding a production database credential for eleven months — nobody had issued a ticket for it, nobody tracked its scope, and nobody ever rotated it. That gap is exactly why AI agent identity and access management has become one of the most urgent problems in software supply chain security. By early 2026, most engineering organizations run anywhere from a few dozen to several hundred AI agents that read repositories, file tickets, call internal APIs, and push deployments — each one needing credentials the way a human employee does. Traditional identity programs, built around people who log in through SSO and get offboarded on their last day, were never designed for software that spins up in seconds, forks itself into sub-agents, and never files an exit ticket. The result is a fast-growing population of non-human identity that most security teams can't fully see, let alone govern.
What is AI agent identity and access management?
AI agent identity and access management is the set of practices and controls for issuing, scoping, monitoring, and revoking the credentials that autonomous software agents use to act on systems — applied with the same rigor traditionally reserved for human users. Where classic IAM assumes a person authenticates once per session and holds a role until HR processes an offboarding, agent IAM has to account for identities that are created programmatically, often by other agents, and that may exist for minutes rather than months. A single CI/CD pipeline in 2026 might spin up a triage agent, a code-review agent, and a deployment agent for one pull request, each requesting its own OAuth token or API key. GitHub's own 2025 State of the Octoverse data showed AI-authored pull requests rising more than 60% year over year on public repositories, and every one of those automated contributions was backed by a credential somewhere. If that credential is a static, broadly-scoped API key sitting in a CI secret store, the agent is a bigger blast radius than the developer it's assisting.
Why do AI agents need identities different from human users or service accounts?
AI agents need a distinct identity class because they don't fit either of the two buckets IAM systems were built around. Human identities assume a single accountable person who can be interviewed, trained, and terminated; service accounts assume a static, long-lived piece of software with a fixed, well-understood function. Agents break both assumptions: an LLM-driven agent can be re-tasked at runtime by a prompt, can call out to other agents or tools it wasn't explicitly provisioned to use, and can make decisions that no line of code directly authorized. A 2025 survey from the Cloud Security Alliance found that non-human identities already outnumber human identities by roughly 45 to 1 in typical enterprise cloud environments, and AI agents are the fastest-growing subset of that population. Treating an agent like "just another service account" means granting it a durable credential and hoping it behaves; treating it like a human means assuming a level of accountability and judgment it doesn't have. Agent IAM has to sit in between: identity-per-task, credentials that expire in minutes, and permissions computed from what the agent is doing right now rather than what it might need someday.
How do overprivileged AI agents create supply chain risk?
Overprivileged AI agents create supply chain risk because a single compromised or manipulated agent can touch far more of the pipeline than the human it's replacing ever could in the same amount of time. Consider a build agent given broad write access to a package registry so it can "publish when tests pass" — if that agent's prompt is manipulated through a poisoned dependency description, or if its underlying model is tricked via indirect prompt injection embedded in an issue comment, it can push a malicious package release using entirely legitimate, unrevoked credentials. This isn't hypothetical: security researchers publicly documented prompt-injection attacks against AI coding assistants throughout 2024 and 2025, including cases where agents with repository write access were coaxed into modifying CI configuration files or exfiltrating secrets through crafted commit messages. Because agents typically inherit the union of permissions needed across every task they might perform, rather than the minimal set needed for the task at hand, one injected instruction can cascade into a registry-wide or repo-wide compromise in seconds — far faster than a human attacker moving laterally would take.
What does good machine identity governance look like for agents?
Good machine identity governance for agents means every credential is short-lived, scoped to a single task, and traceable back to the specific agent invocation that used it — not a shared key living in a config file. Practically, that means replacing static API keys with workload identity federation or short-lived OIDC tokens issued per run, so an agent that finishes a deployment task has its credential expire within minutes rather than persisting indefinitely. It means maintaining an inventory of every agent identity the way you'd inventory employees, including which model backs it, which repos and APIs it can reach, and when it was last used — dormant agent credentials, like the eleven-month-old one from our opening example, should be flagged and revoked automatically after a defined idle period (say, 14 days). NIST's draft guidance on AI system security, along with OWASP's 2025 Top 10 for LLM Applications, both call out excessive agency and insecure credential handling as top risks, and both point toward the same fix: least-privilege, time-boxed, continuously monitored access rather than one-time provisioning.
How should organizations audit and monitor non-human identity for AI agents?
Organizations should audit non-human identity for AI agents the same way they audit privileged human accounts, but at a frequency and granularity that matches how fast agents act. That means logging every action an agent takes with its credential — not just that authentication succeeded, but which files it read, which endpoints it called, and what it changed — and correlating that against the task it was assigned. Quarterly access reviews, standard for human IAM, are too slow for agents that can accumulate scope creep in days; leading teams are moving to automated, continuous entitlement reviews that flag any agent whose granted permissions exceed its actual usage pattern over a rolling 7-to-30-day window. Model providers began publishing agent action logs and tool-call audit trails as a default feature through 2025, which helps, but the burden of stitching those logs into a coherent identity and access record still falls on the organization deploying the agent, not the model vendor.
How Safeguard Helps
Safeguard was built for exactly this gap: securing the software supply chain in a world where a meaningful share of the commits, builds, and deployments in your pipeline are now initiated by non-human actors. Safeguard gives security and platform teams a single inventory of every AI agent identity operating across your CI/CD pipeline, source repositories, and package registries — showing what each agent can access, what it has actually used, and how that's changed over time. Instead of static, long-lived keys, Safeguard helps teams move agents onto short-lived, scoped credentials tied to specific pipeline runs, and automatically flags dormant or overprivileged agent identities before they become the kind of eleven-month-old forgotten credential that turns into an incident. For teams practicing agent IAM as part of a broader machine identity governance program, Safeguard correlates agent behavior against granted permissions in near real time, surfacing anomalies — an agent suddenly reaching a registry it's never touched, or requesting scopes outside its task — so your team can revoke access before a manipulated agent becomes a supply chain compromise. As AI agents take on a growing share of software delivery work, Safeguard's goal is simple: make sure every non-human identity in your pipeline is as accountable, auditable, and tightly scoped as your best-governed human account.