Executive Summary
As enterprises deploy AI agents with increasing autonomy—orchestrating workflows, moving capital, and reconfiguring systems—traditional controls face a fundamental gap: the AI cannot reliably tell you where it is, nor can network topology provide trustworthy physical grounding. This paper examines how physics-anchored location assurance can serve as a governance primitive for AI systems, enabling organizations to constrain high-risk autonomous actions, both human-supervised and fully autonomous, to verified physical zones without relying on the AI's self-reported context.
1. The Emerging AI Enterprise and Its Control Gap
In a mature AI-enabled enterprise, most high-value actions are initiated, planned, or executed by AI agents rather than humans clicking buttons. Humans increasingly serve as approvers, governors, and exception handlers. Three shifts define this transition:
- Principals change. Instead of "user X on laptop Y," enterprises now manage Agent A running on cluster C, triggered by pipeline P, acting on system S. Humans remain in the loop, but as policy authors and escalation handlers rather than direct operators.
- Actions become faster and broader. A single misdirected AI workflow can push configuration across thousands of systems, move substantial capital, or rewrite access controls at scale within seconds.
- Traditional intuitions break. Organizations can no longer rely on implicit physical context, such as "they're in the control room" or "they badged into the building," as a sanity check. Nor can they depend on what the AI claims about its own operational context.
This direction is consistent with how broader AI governance frameworks are evolving. The European Union's AI Act explicitly requires that many high-risk systems support effective human oversight, proportional to their autonomy and risk profile. National Institute of Standards and Technology (NIST) guidance on AI risk management frames "human–AI configuration" as a deliberate design choice that must align with risk appetite and context. And the Organisation for Economic Co-operation and Development (OECD) AI Principles emphasize human-centred values and appropriate human determination as part of trustworthy AI. In that landscape, physics-anchored location is one of the few governance levers that does not depend on what the AI says about itself.
2. Physical Guardrails for AI Agents
Location assurance provides physical constraints that remain valid regardless of what the AI believes or reports about itself. Several patterns emerge:
Pattern 1: Actions restricted to physically trustworthy zones
For certain high-consequence actions—such as changing production infrastructure, approving large financial transfers, or reconfiguring safety-critical systems—organizations can require that the control surface reside in a known, hardened physical location. Even when orchestration, models, and data live in the cloud, the final actuation point, where a supervisor or hardened terminal confirms and transmits the command, can be bound to a verified physical zone. This provides a hard "only from here" constraint in a world where the rest of the stack is highly fluid.
Pattern 2: Physical human-in-the-loop enforcement
As AI autonomy increases, many organizations will retain human approval for a narrow class of actions above certain risk thresholds, in line with emerging expectations for meaningful human oversight rather than perfunctory "rubber-stamping." Location assurance can then enforce where that human must be when they approve—which prevents "rubber-stamping from the couch" for safety-critical decisions and provides evidence that overrides were issued from intended environments.
Pattern 3: Location as a feature in AI risk models
Policy engines, risk scorers, and guardrail models surrounding AI agents can incorporate physics-anchored location as an input feature: Is this device in an expected zone for this role? Is this action being proposed from a place consistent with our continuity plan state? Because this signal reflects real physical structure rather than claimed context, it provides something harder to game than Internet Protocol addresses or self-reported coordinates.
Pattern 4: Physically-aware autonomy states
AI autonomy can be conceptualized as a gearbox, ranging from low-autonomy suggestion mode through high-impact execution authority. Location can be part of the shift logic: in supervised high-autonomy mode, the AI may execute significant actions only if operating through terminals in resilient zones or with a physically-present supervisor in a designated control environment. This ties autonomy levels to physical posture.
Not every critical decision will keep a human in the last mile. For some workflows, organizations will deliberately allow fully autonomous execution by AI agents. In those cases, physics-anchored location still matters, but the control lever shifts. Instead of constraining where a human approver may sit, location assurance constrains which physical zones are allowed to host agents with a given autonomy level, and which clusters or cages are permitted to originate certain command types.
3. Integration Points in AI Architecture
Even in a "pure AI enterprise," physical infrastructure persists: devices, clusters, control planes, and humans who design, approve, and override. Location assurance integrates as a service that receives short bursts of environmental sensor data from devices with appropriate sensors, returns zone-level assertions with quantified uncertainty, and feeds those assertions into policy engines, governance layers, and logging pipelines.
In practical terms, a physics-native location assurance service ingests short bursts of environmental sensor data from endpoints or nearby sensor nodes and returns zone-level assertions with explicit uncertainty and confidence. Those assertions are then consumed by policy engines, AI orchestration layers, and governance systems as one more attribute alongside identity, device health, and behavioral signals.
The key insight is that certain "choke points" exist where AI-driven logic becomes "Agent X is about to authorize action Y from terminal T in room R at time t." Location assurance provides a truthful, physics-anchored view of R at that moment, so governance systems reason with verified context rather than assumed or claimed context. In both human-in-the-loop and fully autonomous modes, the essential question is the same: from which attested physical zone is this command actually being sent? Location assurance answers that question in a way that is independent of the agent's internal state and independent of easily manipulated network indicators.
4. Forensics and Accountability
When incidents occur in AI-heavy systems, post-event questions become more pointed. An agent may have pushed a cascading configuration change, and investigators need to know where that activity was controlled from. They must determine whether anyone approved the action from a secure site or whether the process was purely remote, and which sites were actually used for critical actions during the incident.
If location assertions are part of standard telemetry, organizations gain a timeline of where critical AI-mediated decisions were physically exercised. This enables separation of "the AI made a poor decision but from the correct control posture" from "this was executed from a place that should never have had that level of authority." These distinctions feed policy tuning, model guardrails, and decisions about which zones are trusted for hosting certain AI agents or terminals.
5. Honest Boundaries
From a geophysical standpoint, several limitations merit acknowledgment. Location assurance does not evaluate whether the AI's decision is good; it establishes where that decision is being exercised from, and with what certainty. It does not prevent misuse on a fully compromised endpoint; rather, it raises the bar by requiring physical co-presence and consistent environmental signatures for certain classes of actions.
The capability is most valuable when organizations have already classified actions by risk, defined physical zones intended to carry that risk, and wired AI governance to care about that distinction. Location becomes one more constraint in a defense-in-depth model, not a silver bullet.
Conclusion
As AI agents assume greater operational authority, the question of physical grounding becomes strategic. Networks do not provide it. Global Positioning System (GPS) signals can be spoofed or may be unavailable. The AI cannot reliably self-report it. Physics-anchored location assurance offers an independent layer. It provides a way to bind high-consequence AI actions to verified physical context without depending on the AI itself or on easily manipulated signals.
ALIS, iDvera's physics-native location assurance platform, is designed to provide exactly this class of zone-level attestations into AI policy engines and governance layers, without requiring changes to model architectures themselves.