AI Exposure Report Sample

You can't govern what you can't see.

Most enterprises can't answer four questions: how much AI is running, how many agents act alone, what it costs, and how many machine identities hold access. The AI Exposure Report answers all four in one board-ready view — what's exposed, who owns it, what to do next.

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Figures below are illustrative of a typical mid-size enterprise estate. Your report reflects your environment.

Estate exposure at a glance

SAMPLE · last 30 days
AI Sprawl 247 AI systems discovered No complete inventory means an unknown risk surface.
Agent Sprawl 63 Autonomous agents found Unclear accountability for decisions made without sign-off.
Token Sprawl $184K Monthly AI spend Runaway cost with weak budgets and no chargeback.
NHI Sprawl 2,438 Machine identities (NHI) Excessive machine identities with unclear permissions.
11
Unapproved agents
17
High-risk AI applications
8
Open compliance gaps
14,231
Autonomous decisions (30d)

If an AI agent made a $1.4M decision at 2:47 this morning, would you know which agent made it — and who owns it? Most organizations measure the agents. Almost none measure the humans supposed to be supervising them, or who's accountable when an autonomous action goes wrong.

What keeps your CIO awake

The exposure above, expressed as business risk.

RiskExposureWhy it's flagged
Token wasteCriticalAI spend rising with no budget owner or chargeback.
AI sprawlHighNo single inventory of apps, models, and Copilots.
Agent oversightHighAutonomous agents acting without consistent review.
NHI riskHighThousands of machine identities, many over-permissioned.
Excessive permissionsHighAgents and integrations holding more access than needed.
Accountability gapHighNo clear owner for many autonomous decisions.
Shadow AIMediumUnsanctioned tools receiving sensitive data.
Compliance (EU AI Act)MediumInventory and evidence not yet audit-ready.
Data exposureMediumClassified data flowing into AI tools without inspection.

What to do next

Five steps, in order. The first three are visibility; the last two are control.

  1. Run AI discovery across SaaS, cloud, and Copilots — apps, models, agents, and machine identities in one inventory.

  2. Map owners and accountability for every system and agent, so each autonomous decision has a name attached to it.

  3. Set budgets and thresholds for token spend, with alerts and chargeback by department before the overage.

  4. Instrument supervisor behavior for agents — approval fatigue, override effectiveness, and human-in-the-loop health, not just agent metrics.

  5. Enforce policy through the controls you already own, compiling intent into native configuration rather than adding another silo.

Discover AI. Govern AI. Control AI.

Govern360 produces this report from your own environment — and keeps it live. Continuous discovery, scoring, ownership, and audit-ready evidence across AI sprawl, agent sprawl, token sprawl, and NHI sprawl. Not a one-time assessment; a standing answer to "what AI do we have, what does it cost, and who's accountable?"

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How these metrics are defined

AI systems discovered
Distinct AI apps, services, models, and Copilots in use across the estate.
Autonomous agents found
Agents configured to take actions without human review in at least some workflows.
Machine identities (NHI)
Non-human identities holding access — service principals, API keys, and agent identities.
Autonomous decisions (30d)
Actions taken by agents in the last 30 days without human sign-off.
Monthly AI spend
Token and AI-service cost across providers for the trailing month.