CloudSentry AI continuously discovers, evaluates, and helps you govern every AI agent running across your cloud — grouped into six areas of risk, from foundational inventory to continuous compliance.
Three complementary methods, running continuously and working together.
Read-only connections to Azure, AWS, and GCP continuously inventory every AI-related resource and check its configuration, permissions, location, and tags against policy.
For instrumented agents, we observe what they actually do at runtime — tools called, data touched, destinations reached — and compare it to their normal pattern.
Approval, incident-response, and compliance-evidence workflows built into the platform itself, so decisions that need a human are tracked and enforced — not assumed.
Every finding below draws on real data — cloud configuration, agent behavior, or CloudSentry's own governance workflows.
What we find: Every AI agent running across your connected cloud accounts, and specifically which ones have no registered owner, no documented business purpose, or no completed approval record.
Why it matters: You cannot govern, secure, or budget for what you don't know exists. This is the foundation every other protection in this document is built on — a single, always-current inventory instead of a spreadsheet someone updates twice a year.
What we find: Agents holding wildcard or administrator-level permissions that go beyond what their actual job requires.
Why it matters: The same principle that limits how much damage a single compromised employee credential can do, applied to AI — it caps the worst-case outcome of any one agent going wrong.
What we find: High-risk actions (financial transactions, destructive operations, permission changes, direct customer communication) that an agent has taken, or is proposing to take, without a recorded human sign-off.
Why it matters: Keeps a person accountable for the decisions that matter most, even as agents do more on their own — essential for both risk management and for defending your practices to a regulator or auditor.
What we find: Agents calling tools or APIs that fall outside your organization's approved catalog.
Why it matters: Prevents “capability creep” — an agent gradually gaining abilities well beyond what it was ever approved to do, often without anyone deciding that should happen.
What we find: Secrets embedded directly in agent configuration, and credentials that haven't been rotated within your policy window.
Why it matters: Credential compromise is one of the single most common ways any system gets breached — this closes that door specifically for your AI agents.
What we find: Prompt injection attempts that were not successfully blocked by the agent's protective layer.
Why it matters: This is one of the most novel risks unique to AI agents, with no equivalent in traditional software. Left unmanaged it can turn a trusted internal tool against you.
What we find: Agents classified as handling sensitive data that are communicating with external AI models or services outside your approved list.
Why it matters: Stops your most sensitive data from silently leaking through an AI agent — a critical control for regulatory compliance (GDPR, HIPAA, PCI) and for customer trust.
What we find: Agent memory kept longer than your retention policy allows, or memory contents that look like they contain a secret or personal information.
Why it matters: Closes an overlooked but very real privacy and data-retention gap that's unique to AI agents with memory — one most organizations haven't thought to look for yet.
What we find: Agent workloads with unrestricted outbound internet access, and production workloads sharing a network with non-production ones.
Why it matters: The network equivalent of watertight compartments on a ship — it limits how far any single problem can spread.
What we find: Any outbound call an agent makes to a destination outside your approved list of models and services.
Why it matters: A broader safety net than data-loss prevention alone — this is about controlling where your AI agents are allowed to talk to at all, not just what they send.
What we find: Agents running on a model that isn't on your approved list, or deployed in a cloud region outside your approved list.
Why it matters: Keeps AI sprawl in check — the same governance discipline already applied to cloud spend and vendor selection, extended to AI models specifically.
What we find: Missing required tags, deployment outside approved regions, exposed public endpoints, and logging or encryption not enabled.
Why it matters: Catches misconfiguration before it becomes an incident — far cheaper and safer than discovering it after something has already gone wrong.
What we find: Abnormal call volume (more than three times an agent's typical baseline), traffic to new or unusual destinations, and activity outside normal business hours.
Why it matters: Turns AI oversight from a one-time check into ongoing behavioral security — the same principle behind fraud detection and intrusion detection, applied to AI agents.
What we find: Knowledge-based answers delivered without a supporting source, and outputs touching a regulated topic (financial, medical, or legal advice) that weren't routed through review.
Why it matters: Protects against the reputational, legal, and financial fallout of an agent confidently saying the wrong thing to a customer, employee, or regulator.
What we find: Agents with no recent activity that are still marked active, and agents that are overdue for their periodic re-certification.
Why it matters: Closes the classic “orphaned account” problem — a well-known source of breaches in traditional IT — for AI agents specifically.
What we find: Active incidents involving an agent where the incident-response playbook (disable, revoke access, isolate, preserve evidence) has not yet been triggered.
Why it matters: AI agents need their own incident-response muscle — this makes sure that muscle actually fires when needed, instead of only existing on paper.
What we find: Agents processing data in a region outside your approved or legally required list.
Why it matters: Data residency violations carry real regulatory and financial consequences — this gives you continuous, provable compliance instead of a point-in-time audit finding.
What we find: Production agents missing a completed intake record covering purpose, data handled, model used, tools granted, autonomy level, business owner, and risk rating.
Why it matters: Moves AI governance from cleanup-after-the-fact to review-before-launch — the difference between prevention and damage control.
What we find: Agents holding production-impacting capabilities (administrative access, deployment rights, payment execution) that are still classified at a low-oversight tier.
Why it matters: Ensures the level of oversight is proportional to the level of risk — the agents that could do the most damage automatically get the most scrutiny.
What we find: Agents missing the audit evidence trail required for ongoing compliance reporting, or retaining that evidence for less time than required.
Why it matters: Turns compliance from a stressful annual scramble into an always-current, ready-to-show record — real evidence, not a snapshot assembled under deadline.
A single client can run agents on Azure and GCP at once and see everything in one unified dashboard. A managed services provider running dozens of clients gets full data isolation between every one of them — verified at the database layer, not just the interface.
Every cloud account you connect resyncs automatically on an hourly cycle. Stale, deleted, or renamed agents are pruned automatically — no one has to remember to click "Sync."
See how it works →
Bedrock agents, tagged IAM identities
Cognitive Services, ML workspaces, Entra ID
Vertex AI, labeled service accounts
Per-tenant, database-verified
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