The Tool Desk
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Preventing untracked cloud resources requires more than registering your agents or requiring tags. Give each agent an attributable, short-lived identity; limit its APIs, tools, environments and regions; force provisioning through policy-checked paths; record owners and lifecycle state; and continuously compare cloud inventory and audit events with your approved records. The combination of prevention and reconciliation is what reveals resources that bypass a registry or deployment pipeline.
What “untracked” means and why agents create the problem
A cloud resource is untracked when it exists in a provider account or project but is missing from the records your organization uses to approve, operate and retire resources. Those records might be an infrastructure-as-code state file, a service catalog, an agent registry, a cost-allocation system or an owner inventory. A resource can be correctly tagged and still be untracked if no approved request or deployment record explains why it exists.
Agents increase this risk because they can call APIs through several layers: an orchestrator, a tool server such as an MCP server, a cloud SDK, a deployment pipeline or a direct console-like integration. A single agent task may create a database, identity, queue and network rule in different services. If the calls use a human’s broad credentials, or if one shared role is used by many agents, audit logs cannot reliably identify the actor or the business owner.
The practical objective is therefore not to block every autonomous action. It is to make authorized actions attributable, bounded and observable, while keeping a governed path fast enough that teams do not create shadow deployments.
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The control architecture that works
Distinct identities make the actor visible
Issue a dedicated workload identity or role to each agent, or to a tightly defined agent workload. Keep these identities separate from human accounts and from unrelated automation. Consistent names and tags should identify the agent, owning team, environment and application. AWS recommends dedicated agent roles, naming and tagging conventions, separate CloudTrail activity and short-lived credentials in its Agentic AI Lens guidance. Microsoft likewise calls for unique, auditable agent identities in its agentic-risk guidance.
Use temporary credentials and scoped delegation where the provider supports them. Do not let an agent reuse a developer’s long-lived access key or a general administrator role. If an agent invokes another service, preserve the originating identity or a correlation identifier so the complete execution path can be reconstructed.
Authorization must cover tools, APIs and destinations
Least privilege applies to the whole action chain, not just the final cloud role. Explicitly authorize which tools an agent may call, which APIs those tools may use, and which resource scopes, accounts, projects, subscriptions, regions and environments are in bounds. Start read-only whenever the task allows it. For write access, allow only the required create, update and delete operations.
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A registry establishes ownership and lifecycle
Maintain a central inventory of agents, orchestrators, tool servers, destinations and cloud identities. Each record should include an accountable owner, purpose, data and resource scope, environments, dependencies, approval status, expiration or review date and decommissioning state. Google Cloud describes its Agent Registry as “a centralized catalog that lets you store, discover, and govern servers, tools, and AI agents in Google Cloud” in its Agent Platform governance documentation.
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A registry is not proof that every resource is authorized. Treat it as the expected-state record that you will reconcile with provider inventories and audit logs. Review changes to an agent’s tools, identity, prompts, destinations and permissions as lifecycle events, not as informal configuration edits.
Guardrails must be enforceable at multiple layers
Put organization- or management-scope policies above individual projects and subscriptions so inherited controls apply to new environments. Restrict resource types and regions, require ownership and cost-center metadata, and reject deployments that omit mandatory tags. Express the rules as policy as code and run them before deployment, then enforce equivalent restrictions at runtime because not every API path goes through your preferred pipeline.
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Use infrastructure as code (IaC) with reviewed changes for normal provisioning. Deployment checks can verify templates, but runtime policy is still needed for direct API calls, emergency actions and services that create dependent resources on an agent’s behalf.
Logs and reconciliation expose what bypasses controls
Retain cloud audit logs and agent or tool-call logs with a common identity or correlation value. Alert on new identities, permission expansion, policy changes and resource creation outside expected deployment paths. Periodically compare provider inventory with your agent registry, service catalog and IaC state. Missing registry entries, unowned tags, unexpected regions and resources with no corresponding change record are investigation candidates.
Implementation sequence
1. Discover agents, tools and cloud entry points
Start with an inventory rather than a policy. Enumerate hosted and self-managed agents, orchestrators, tool servers, SDK integrations, CI/CD workflows, cloud accounts or projects, subscriptions, service principals, roles, API keys and environments. Include development sandboxes and experiments; they are common sources of resources that never reach a production catalog.
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For every item, record the owner, business purpose, data classification, allowed resource scope, dependencies, current credentials and lifecycle state. AWS recommends a centralized registry and explicit tool authorization in its governance-scope guidance. Microsoft’s guidance emphasizes dependency inventory, ownership and lifecycle governance, while Google’s Agent Registry provides a provider-native catalog for agents, tools and servers.
2. Create an attributable identity for each workload
- Create a dedicated workload identity or role for each agent or tightly bounded workload.
- Apply a naming convention that identifies the agent, application, environment and owner.
- Use temporary credentials or scoped delegation instead of long-lived keys.
- Separate agent identities from human identities and unrelated automation.
- Ensure logs preserve the agent identity and a request or task correlation ID when calls cross tools and services.
Test the identity by creating a harmless, low-cost resource in a sandbox and confirming that the cloud audit record identifies the intended agent rather than a developer, shared service account or generic administrator.
3. Restrict creation authority
Define an allow-list for operations, resource types, locations and environments. A data-analysis agent might read objects and publish a report but have no permission to create storage, identities or network rules. A deployment agent may create a specific application stack only in a designated account, project or subscription and only through approved templates.
Use separate roles for discovery, planning and execution when practical. Require an approval step for high-cost, externally reachable, data-bearing or identity-changing resources. Keep the elevation short-lived and record who approved it, which task received it and when it expired.
AWS documents permission boundaries and IAM conditions for constraining agent roles; its security and governance guidance also recommends scoped tools, least-privilege IAM and audit logging. Microsoft recommends least privilege and scoped access, and Google documents identity-linked IAM policies in its Agent Platform governance material.
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4. Enforce resource and deployment guardrails
- Organization scope: Apply inherited policy at the management-group, organization or equivalent level.
- Resource scope: Block unapproved resource types and require approved network, encryption and data-residency settings.
- Location scope: Allow only regions that meet regulatory, latency and cost requirements.
- Metadata: Require owner, application, environment, cost center, data classification and expiration or review fields.
- Pipeline scope: Run IaC validation and policy checks before deployment, with an exception record for approved deviations.
- Runtime scope: Detect or block direct API calls and configuration drift after deployment.
Microsoft’s cloud governance guidance covers inherited policy scope, automation, tags, region and resource restrictions, policy as code and IaC. Google’s Office of the CISO recommends policy as code, runtime controls, rollback mechanisms and cost-driver tracking in its guidance on countering shadow agents.
5. Observe execution and reconcile inventory
Store agent tool calls, authorization decisions, deployment records and provider audit events in a searchable, tamper-resistant location. Link them with the same task, identity or correlation identifier. Capture the requested operation, target resource, policy decision, approver, result and timestamp; without these fields, a log may show that a resource appeared but not why.
Build alerts for:
- New agent identities, service principals, roles or API keys.
- Permission grants, policy edits and attempts to bypass a deployment path.
- Resource creation in an unapproved region, account, project, subscription or environment.
- Missing owner, cost-center or expiration metadata.
- Resources whose creator identity does not match a registered agent or approved pipeline.
- Unexpected changes to network exposure, encryption, retention or data access.
Run scheduled reconciliation as well as event-driven alerts. Pull the provider’s resource inventory, normalize resource identifiers and compare it with IaC state, the service catalog and the agent registry. Route unmatched resources to an owner for confirmation, quarantine or deletion; never delete automatically until retention, legal-hold and dependency checks are complete.
6. Roll out controls in audit mode first
Begin with dry-run or audit behavior where the platform supports it. Review policy decisions, identify legitimate exceptions and measure which automation paths would be blocked. Google’s Agent Platform documentation describes audit and enforcement modes, while Microsoft advises starting with a small policy set, testing automation and expanding controls gradually.
After false positives are resolved, enforce the highest-value rules first: identity attribution, disallowed regions, unapproved resource types, missing ownership metadata and direct public exposure. Version policies, record exceptions with an owner and expiry, and revisit them as agent capabilities change.
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7. Keep the approved path usable
Controls fail when the sanctioned route is slower than bypassing it. Provide bounded sandboxes with quotas, preapproved templates and synthetic or masked data. Offer self-service deployment modules that automatically attach required tags and ownership records. Make approvals responsive, explain rejection reasons and publish a clear request route for new tools, regions and resource types.
AWS warns that cumbersome governance can encourage shadow AI and recommends making the governed path attractive in its governance guidance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Provider implementation options
AWS, Azure and Google Cloud provide overlapping building blocks, but the sources do not establish a neutral benchmark or a universal winner. Choose based on where your organization already centralizes identity, policy, inventory and logging, then verify the exact enforcement behavior for each service and region.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitches| Control area | AWS | Azure | Google Cloud |
|---|---|---|---|
| Identity attribution | Dedicated IAM roles, consistent names and tags, separate CloudTrail activity and short-lived STS credentials are described in the Agentic AI Lens. | Microsoft recommends unique, auditable agent identities and least privilege in its agentic-risk guidance. | Agent identities and identity-linked IAM access policies are covered in Agent Platform governance documentation. |
| Policy enforcement | Permission boundaries, IAM conditions, scoped tools and monitoring constrain agent roles. | Azure Policy can inherit from management groups and enforce tags, resource and region restrictions; policy as code and IaC support repeatable deployment checks. | Agent Platform governance includes policy audit and enforcement modes; Google also recommends policy as code and runtime enforcement. |
| Inventory and governance | A centralized registry for agents and tools, explicit authorization and invocation lineage are recommended in AWS governance guidance. | Microsoft emphasizes ownership, dependency inventory, lifecycle states and change review. | Agent Registry catalogs agents, tools and servers; gateway monitoring provides an observation point. |
| Detection and response | CloudTrail, AWS Config and EventBridge can support alerts for configuration, IAM and resource changes, supplemented by agent invocation logs. | Use policy compliance results, activity logs, deployment records and runtime monitoring; confirm which controls are audit-only or blocking for each policy. | Use audit decisions, semantic governance, Agent Gateway monitoring and provider audit logs, then reconcile with your own inventory. |
| Rollout and exceptions | Test boundaries and conditions in a sandbox, retain reconstructable execution paths and document approved exceptions. | Start with a small policy set, test automation, then expand inherited controls and exception reviews. | Use dry-run or audit modes before enforcement and define rollback procedures for runtime policy changes. |
How to investigate a resource that has no record
- Preserve evidence. Snapshot tags, configuration, relationships and relevant audit and tool-call logs before changing the resource.
- Identify the creator. Trace the creating API event to a role, service principal, workload identity or human account, then follow the correlation ID through the agent and tool logs.
- Check authorization. Compare the operation, target, region, environment and metadata with the agent’s registered scope and the policy decision at the time.
- Map dependencies. Determine whether other services, applications, data pipelines or agents depend on the resource.
- Assign or confirm ownership. Ask the recorded owner to approve retention, remediation or retirement; if no owner exists, escalate through the cloud governance process.
- Contain safely. Remove public exposure or excessive permissions first, then quarantine or delete only after dependency, retention and legal checks.
- Correct the control. Add the missing policy, identity condition, deployment check, registry field or alert, and test it in audit mode before enforcing broadly.
Operational checklist
- Every agent and tool server has a named owner, purpose, scope and lifecycle state.
- Every agent workload uses a distinct, auditable identity rather than a developer credential.
- Credentials are temporary or otherwise constrained, and permission expansion is detectable.
- Tool calls are explicitly authorized and logged with a common correlation value.
- Creation rights are limited by operation, resource type, location, account or project and environment.
- High-risk or high-cost actions require approval or just-in-time elevation.
- Organization-level policies restrict regions and resource types and require ownership and cost metadata.
- IaC and deployment checks are combined with runtime enforcement and drift detection.
- Cloud inventory is reconciled with registries, service catalogs and IaC state on a scheduled basis.
- Alerts cover new identities, permission changes, policy edits, unapproved creation paths and missing metadata.
- Policies are introduced in audit or dry-run mode, with documented exceptions and rollback plans.
- Teams have a usable sandbox, templates and approval route so governed deployment is easier than shadow deployment.
What success looks like
An agent should be able to complete an approved task without possessing broad administrative power, while every resulting resource can be tied to an identity, owner, request and policy decision. If a resource appears outside that chain, your inventory comparison and alerts should find it quickly, and your investigation process should be able to contain it without guessing about dependencies. That combination—attributable access, enforceable guardrails, complete evidence and continuous reconciliation—is the reliable way to prevent AI agents from creating cloud resources that nobody can explain or retire.
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