Token Security provides non-human identity (NHI) discovery and posture management, and its public positioning has extended toward continuous discovery, policy enforcement, and automated response for AI agents specifically. The foundation is an inventory and posture problem, solved well by a platform built for exactly that. Runtime governance of what an already-registered AI agent is allowed to do on each individual call is a related but distinct question — the two capabilities can sit in the same vendor's roadmap or in separate layers, and confirming which is the point of evaluating any specific platform.
What NHI discovery and posture platforms solve
The core problem this category addresses is real and underrated: most organizations have no reliable inventory of their non-human identities. Service accounts get created by an engineer under deadline pressure, API keys get generated for a one-off integration and never revisited, and OAuth grants accumulate from every third-party tool anyone connected over the years. Nobody owns a master list, and nobody can say with confidence which of these thousands of credentials are still needed, which are over-scoped relative to what they actually use, and which have been sitting dormant long enough to be pure attack surface.
Per its public positioning, Token Security's category answers exactly this: discover every NHI across the environment, classify what it is and where it's used, score its risk (unused, over-privileged, exposed, orphaned from a departed owner), and surface remediation paths. This is the estate-level view described in NHI credential lifecycle management — provisioning, scoping, rotation, monitoring, decommissioning — applied retroactively to years of accumulated credentials that were never managed with a lifecycle in mind to begin with.
Where posture assessment stops and runtime governance starts
An NHI posture platform's natural operating cadence is periodic: scan the estate, score what it finds, flag what needs attention, recommend remediation. That cadence is well matched to service accounts and API keys, which mostly sit still between uses — their risk profile changes slowly, so a periodic assessment stays accurate for a useful window.
AI agents don't sit still. An agent's actual risk on any given call depends on what it is doing right now: which tool it is invoking, what data that tool call touches, and whether the reasoning that produced this specific action is consistent with the task it was given. A posture platform's assessment of an agent's credential as over-scoped on Tuesday's scan is a different signal from evaluating the individual tool call the agent makes on Wednesday afternoon — the two are complementary rather than the same measurement, which is the distinction examined generally in runtime security vs. security posture.
A few specific questions worth putting to any posture vendor as agents enter the estate:
How delegation shows up in a credential inventory. When an agent delegates a sub-task to another agent, or an orchestrator fans a request out across several tool calls, no new NHI is necessarily created — the same credential, or a token derived from it, is doing the work. Ask whether the platform traces the delegation chain a single identity's authority travels through during one task, or inventories the identity as a single unit regardless of how many hops its authority passed through. That tracing is what scoped tokens for sub-agent delegation and the confused deputy problem are both about.
Where spend anomalies get flagged. An agent that starts making far more expensive tool or model calls than its task justifies is a cost incident, sometimes a symptom of a security incident (a manipulated agent looping, or being steered toward expensive external calls). Ask whether spend anomaly detection is part of the platform's scoring or a separate signal you need to source elsewhere — see anomaly detection on agent spend and budgets vs. rate limits.
Behavioral drift within a single task. An agent can start a task within scope and drift off it mid-task under prompt injection or a reasoning error, all while holding the exact credential the posture platform correctly assessed as appropriately scoped at provisioning time. Catching that requires evaluating the current action against the current context, continuously, which is the premise of continuous authorization rather than point-in-time credential review.
What each layer is actually good at
| Capability | Ask the vendor | Where it typically lives |
|---|---|---|
| Find every service account, key, and OAuth grant in the estate, including legacy ones | Does discovery cover the full estate, or only what's already registered with a runtime layer? | Posture discovery |
| Score a credential's scope against what it actually uses | How current is the scoring — continuous, or refreshed on a scan cadence? | Posture discovery |
| Authorize an individual tool call in real time | Does the platform sit in the request path for individual tool calls, or assess credentials on a schedule? | Agent runtime governance |
| Track a delegation chain across multiple agents in one task | Does the platform trace authority across delegation hops within one task? | Agent runtime governance |
| Flag anomalous spend | Is spend anomaly detection part of the scoring, or a separate signal? | Agent runtime governance |
| Produce a tamper-evident audit record naming agent, principal, and action per call | Confirm per vendor whether the audit record names the agent, the principal, and the action — not just the credential. | Confirm per vendor |
| Find a five-year-old orphaned API key nobody remembers | Does discovery reach dormant, unregistered credentials that predate any governance effort? | Posture discovery |
Composing the two layers
Most mature agent security programs need both, not one instead of the other. The posture layer does the estate-wide archaeology: finding every NHI that existed before governance became a priority, scoring the backlog, and feeding a remediation queue. The runtime layer governs the agents that are actually in production today, on every call they make, going forward. Sequencing depends on where your risk is concentrated — an estate with years of unmanaged service accounts and OAuth sprawl gets more immediate value from posture discovery; an estate whose growth is now dominated by new agent deployments gets more immediate value from putting runtime governance in front of what's shipping next, per the reasoning in machine identity vs. workload identity vs. agent identity.
Why the gap is easy to miss
The confusion is understandable, because both categories use the same vocabulary. "Non-human identity," "scoping," "lifecycle" — an NHI posture vendor and an agent governance vendor will both use these terms, and a team evaluating options can reasonably assume overlapping capability where there is actually a boundary. The practical test is to ask what happens the moment an agent makes a tool call: does the platform evaluate that specific call against current policy and context, or does it evaluate the credential the agent is holding on a schedule independent of any individual call? The first is runtime governance; the second is posture management, and knowing which one you're buying determines whether it belongs in the request path or in a periodic review cycle.
This matters operationally too. A posture platform integrated as a dashboard and a ticket queue can be adopted without touching production traffic, which is part of why it's often the easier first step. A runtime governance layer sits between an agent and the tools it calls, which means it has to meet the latency and availability bar of the traffic it's protecting — a materially different integration commitment, and one worth planning for deliberately rather than discovering under deadline once agents are already in production.
What good looks like
- Every non-human identity in the estate — legacy service accounts included — has an owner, a documented purpose, and a scheduled review, found through periodic discovery rather than tribal knowledge.
- Every AI agent additionally has a governed identity that authorizes its individual actions in real time, not just a scoped credential assessed at provisioning.
- Delegation between agents narrows authority at each hop, and that narrowing is visible in the audit trail, not just in the original credential's scope.
- Spend and behavioral anomalies for agents are monitored as a distinct signal from identity posture, because a well-scoped credential can still be misused within its scope.
- Posture findings from the discovery layer feed directly into remediation — decommissioning, re-scoping, or migrating a credential onto the governed runtime path — rather than sitting in a dashboard nobody actions.
Common questions
Do I need both an NHI posture platform and an agent governance layer? If your estate includes both a backlog of legacy service accounts and keys, and a growing population of AI agents in production, yes — they solve different problems and neither substitutes for the other. A smaller estate with few legacy NHIs but heavy agent deployment can reasonably prioritize runtime governance first.
Can an NHI posture platform be extended to do runtime authorization? Whether a given posture platform can sit in the request path and evaluate individual tool calls, versus assessing credentials on a schedule, is a direct architectural question worth putting to the vendor — the two have very different latency and availability commitments, and moving from one to the other is a substantial engineering shift, not a configuration change.
What's the fastest way to find the gap in an existing NHI program? Cross-reference your NHI inventory against your actual list of production AI agents. Agents that exist in production but never appear in the inventory are your registration gap; agents that appear in the inventory holding standing, unreviewed credentials are your runtime-governance gap. Most estates have both.