AI Agent Auditor

Audit Methods

Top 5 Tools for Auditing Cloud Permissions Granted to AI Agents

Auditing cloud permissions for AI agents starts with a practical question: what can each agent’s service identity actually access, and what evidence can a reviewer use to assess that access? The answer depends on more than the tool’s feature list.

Identity types, cloud coverage, setup requirements, and the detail available in findings all shape whether a product fits your audit.

This shortlist compares five options without treating them as interchangeable or assigning a universal winner. Some are designed for a cross-cloud view, while others focus on AWS or Google Cloud.

I’d first match the tool to the agent identities and providers in your environment, then check whether its evidence and onboarding requirements suit your review process.

Recurring editorial scenes feature cats with clearly visible mechanical or cybernetic parts investigating AI-agent audit setups: tracing an agent's actions, examining evaluation results, or inspecting safety controls. Use crisp, atmospheric digital illustration with lime highlights and restrained purple haze against dark settings; keep subjects and props legible, and do not depict fake software screenshots, brand lettering, or logos.: Top 5 Tools for Auditing Cloud Permissions Granted to AI Agents

Quick picks

For explicitly documented AI-agent identity coverage across multiple clouds, consider Cortex Cloud CIEM: its stated coverage includes AWS, Azure, Google Cloud, and OCI. Its access API documents fields such as permission scope and last-used time, but requires a qualifying Cortex Cloud license.

Microsoft Defender for Cloud CIEM offers Azure, AWS, and GCP coverage with investigation and reporting views; it requires Defender CSPM and cloud-account onboarding. Datadog Cloud Security Identity Risks may suit teams already reviewing findings in Datadog, though its cloud integrations require provider-specific setup and collection can affect billing.

For AWS-only reviews, IAM Access Analyzer can surface unused access and resource-sharing risks, with regional setup requirements for external and internal analysis. Google Cloud Policy Intelligence is a focused option for service-account permission-use insights, but requires Premium or Enterprise activation and does not provide a cross-cloud view.

Product shortlist

1. Cortex Cloud Identity Security (CIEM)

Cortex Cloud Identity Security (CIEM): A branching stone maze fills a dim, workshop-like chamber, with a small locked glass vault at its center. One cat traces a narrow route through the maze with a paw, past several dead ends and toward the vault. The maze and vault are plain, with no writing or markings..Cortex Cloud Identity Security (CIEM)
Cortex Cloud Identity Security (CIEM) — AI editorial illustration, not a product photo or screenshot.

Cortex Cloud Identity Security (CIEM) stands out for teams that need a tool whose documentation explicitly includes AI-agent identities, rather than treating them only as generic cloud accounts. It describes analyzing effective permissions across AWS, Azure, Google Cloud and OCI, and highlighting excessive access and paths to sensitive resources.

That makes it a candidate for cross-cloud reviews, though teams should confirm coverage for their specific identity types and deployment.

What I like

  • Its stated scope includes AI agents as well as service accounts, bots and other machine identities, which aligns closely with this audit task.
  • Effective-permission analysis can help reviewers assess what an identity can actually access, not just inspect assigned policies.
  • The CIEM access API documents review-friendly fields such as resource, permission scope, last-used time and unused-action count.

Limitations

  • The public product information does not quantify setup effort; validate onboarding requirements and supported identity types with the vendor.
  • The access API requires a Cortex Cloud Runtime Security or Posture Management license, and the cited page does not specify detailed export or retention options.

My take

I’d consider Cortex when the audit needs to cover agent and other non-human identities across multiple cloud providers. Its documented access records may help reviewers trace identity-to-resource access and spot permissions that appear unused.

Best for

Teams seeking explicitly documented AI-agent identity coverage and cross-cloud permission analysis, provided the required license and deployment scope fit their review process.

Sources: Cloud Infrastructure Entitlement Management | CIEM; Search CIEM Access.

2. Microsoft Defender for Cloud CIEM

Microsoft Defender for Cloud CIEM: An oversized ring of mismatched iron keys lies on a dark workbench beside a single simple lock. One cat separates a small handful of keys from the sprawling ring with its paw. The keys and lock are unmarked, and the scene is framed closely against a near-black workshop..Microsoft Defender for Cloud CIEM
Microsoft Defender for Cloud CIEM — AI editorial illustration, not a product photo or screenshot.

Microsoft Defender for Cloud CIEM is a strong fit if your team needs to review agent identities across Azure, AWS, and Google Cloud from a shared security workflow. I like that it covers non-human identities—including service principals, managed identities, AWS roles, and GCP service accounts—and pairs permission analysis with investigation and reporting views.

It is a broad option, but it requires Defender CSPM and cloud-account onboarding rather than serving as a standalone audit tool.

What I like

  • It analyzes effective permissions and usage patterns, helping reviewers spot excessive, unused, or misconfigured access.
  • Attack-path views add context for assessing how an identity’s permissions could contribute to risk.
  • Cloud Security Explorer, recommendations, and a CIEM workbook provide useful investigation and reporting evidence.

Limitations

  • Setup requires Defender CSPM, account onboarding, and provider-specific Security Admin permissions, so enablement may involve coordination with cloud administrators.
  • Since August 6, 2026, Defender for Cloud no longer publishes detailed unused-permission actions for overprovisioned AWS and GCP identities; teams need native provider tools for those details.

My take

The main appeal is breadth: one workflow can help teams examine identity permissions across three major clouds, including identity types commonly used by agent workloads. Effective-permission analysis and attack-path context can help reviewers move beyond a list of attached policies and investigate potential exposure.

I would weigh that coverage against the setup and evidence requirements. Azure, AWS, and GCP onboarding each requires the relevant administrative access, and AWS and GCP recommendations no longer include detailed unused-action lists.

If reviewers need that action-level evidence, plan to gather it from native tools; otherwise, this is a useful choice for teams already using Defender CSPM who want cross-cloud identity-risk reporting.

Best for

Teams using Defender CSPM that need a cross-cloud view of permissions for AI-agent service identities and can supplement AWS or GCP recommendations with native tools when detailed unused-action evidence is required.

Sources: Cloud infrastructure entitlement management (CIEM) - Microsoft Defender for Cloud; Enable cloud infrastructure entitlement management (CIEM) - Microsoft Defender for Cloud.

3. Datadog Cloud Security Identity Risks

Datadog Cloud Security Identity Risks: Three thick, unmarked metal pipes meet across a shadowy utility room, their ends separated by a wide gap. One cat pushes a sturdy coupling onto the open pipe ends. A faint purple glow brushes the pipes while sharp lime light catches their edges..Datadog Cloud Security Identity Risks
Datadog Cloud Security Identity Risks — AI editorial illustration, not a product photo or screenshot.

Datadog Cloud Security Identity Risks is worth considering if your team already uses Datadog and wants cloud identity findings reviewed alongside its other security data. Its coverage spans AWS, Azure, and GCP for identity risks, while Cloud Security integrations also include OCI.

I’d treat it as a broad cloud-identity review option, not assume it provides a dedicated audit workflow for AI-agent permissions.

What I like

  • It supports identity-risk collection across AWS, Azure, and GCP, giving teams with multiple cloud providers a shared place to review findings.
  • The documented integrations cover OCI as well as AWS, Azure, and GCP, which may be useful for broader Cloud Security collection.
  • Findings can be reviewed in Datadog, so teams already working there may find it convenient to include them in their existing review process.

Limitations

  • Setup involves configuring cloud integrations, provider-specific permissions, and enabling Cloud Security on each account.
  • Full Azure identity-risk coverage requires Microsoft Graph permissions, and Cloud Security collection can affect Datadog billing.
  • The documentation describes cloud identity-risk collection, but does not establish a dedicated AI-agent permission audit workflow.

My take

I’d consider Datadog when the goal is to bring cloud identity findings into an existing Datadog security workflow. Its multi-cloud coverage is useful, but teams should confirm which identity findings they need and whether the configured integrations expose them for the agent service identities they want to review.

The setup is not entirely hands-off. AWS resource collection requires the SecurityAudit policy on the Datadog IAM role; Azure’s full identity-risk coverage needs Microsoft Graph permissions, and the documented GCP integration requires a service account and specified Google APIs.

Those requirements make permission review and billing impact part of the rollout plan.

Best for

Teams already using Datadog that want cloud identity-risk findings from AWS, Azure, or GCP available for review in the same platform. Before relying on it for AI-agent audits, verify that its findings cover the specific service identities and evidence your reviewers require.

Sources: Deploying Cloud Security using Cloud Integrations.

4. AWS IAM Access Analyzer

AWS IAM Access Analyzer: A tall mesh fence surrounds a quiet courtyard, with one open gate and a trail of pawprints crossing the threshold. One cat inspects the gate hinge from outside the fence. Keep the courtyard spare, with no signs, lettering, or other animals..AWS IAM Access Analyzer
AWS IAM Access Analyzer — AI editorial illustration, not a product photo or screenshot.

For agent workloads built on AWS, IAM Access Analyzer offers a focused way to examine what service roles can access and where resource policies allow access. I like that it can surface both unused permissions and certain external or internal access risks, with findings that identify the resource, principal or external entity, and permissions involved.

It is an AWS-specific option, not a unified view across cloud providers.

What I like

  • Unused-access analysis can flag roles and service- or action-level permissions that appear unused, giving teams concrete areas to review for least-privilege work.
  • External-access findings identify resource policies that grant access beyond the configured zone of trust.
  • The analyzer uses a documented read-only service-linked role, and findings provide useful details for investigation.

Limitations

  • External and internal access analysis must be enabled separately in each AWS Region; unused-access analysis does not require an analyzer in every Region.
  • Internal-access findings are available only through the ListFindingsV2 API, which may mean building additional tooling for a reviewer-facing workflow.
  • The service covers AWS IAM and supported AWS resources, rather than offering a cross-cloud permissions view. Unused-access analysis has charges based on IAM users and roles analyzed; external findings are free, while internal findings have resource-based charges.

My take

I would consider this a practical starting point when the agent’s identity is an AWS IAM role and the audit question is specific: what access is unused, or which resource policies expose access beyond the intended trust boundary? The findings can give reviewers a useful lead by identifying the affected resource, principal, and permissions.

Best for

AWS-focused teams auditing agent roles for unused access or resource-sharing risks, especially when they can account for regional setup and API-based review needs.

Sources: IAM Access Analyzer findings; Getting started with AWS Identity and Access Management Access Analyzer.

5. Google Cloud Policy Intelligence (IAM policy insights)

Google Cloud Policy Intelligence (IAM policy insights): A rough stone floor is crossed by one deeply worn groove and several smooth, untouched grooves leading to plain wooden doors. One cat studies the worn track through a small magnifying glass. The doors and floor have no writing or symbols, in a dim archive-like room..Google Cloud Policy Intelligence (IAM policy insights)
Google Cloud Policy Intelligence (IAM policy insights) — AI editorial illustration, not a product photo or screenshot.

For agent workloads running on Google Cloud, Policy Intelligence offers a focused way to compare granted IAM permissions with observed use. Its policy insights include service accounts, making them relevant to agent identities, and can give reviewers evidence for a rightsizing discussion.

I’d consider it when your audit is already centered on Google Cloud; it is not a cross-cloud permissions console.

What I like

  • Usage insights cover principals including service accounts, so reviewers can examine permissions granted to an agent identity.
  • Observed permission use can inform role-rightsizing reviews, rather than relying only on a list of granted permissions.
  • Documented console and CLI review paths give teams options for examining the findings.

Limitations

  • It covers Google Cloud only, and policy insights require Premium or Enterprise activation.
  • Setup requires enabling the Recommender API and granting an IAM Recommender Viewer or Admin role; recommendations may be unavailable for newly granted roles or roles without sufficient insights.

My take

The useful distinction here is between permissions a service account has and permissions it has been observed using. That usage evidence can help a reviewer identify roles worth investigating, but it should inform a review rather than be treated as proof that every unused permission is safe to remove.

Best for

Google Cloud teams auditing agent service accounts who want observed-use evidence to support IAM role reviews. Choose a different option if you need one console across cloud providers.

Sources: Review policy insights for projects, folders, and organizations; Overview of role recommendations.

How to choose

Start with the agent’s identity type and cloud footprint. A service account, role, or managed identity may need different integrations, so confirm the tool covers the exact identity and providers you use before comparing features.

Next, decide what reviewers must be able to verify: effective access, observed permission use, paths to sensitive resources, or findings tied to a specific principal and resource. Usage data can guide rightsizing, but it is not proof that an apparently unused permission is safe to remove; new roles may also lack enough history for recommendations.

Finally, price the setup effort as well as the product. Check required plans, administrative permissions, regional enablement, API access, and any extra work to export or present findings.

Don’t assume broad cloud coverage means identical evidence in every provider.

Sources: Cloud Infrastructure Entitlement Management | CIEM; Search CIEM Access; Cloud infrastructure entitlement management (CIEM) - Microsoft Defender for Cloud; Enable cloud infrastructure entitlement management (CIEM) - Microsoft Defender for Cloud; Deploying Cloud Security using Cloud Integrations; IAM Access Analyzer findings; Getting started with AWS Identity and Access Management Access Analyzer; Review policy insights for projects, folders, and organizations; Overview of role recommendations.

Which option fits?

If your review spans several clouds and needs explicitly documented AI-agent coverage, Cortex is a natural place to start, provided its licensing and deployment requirements fit. Teams already using Defender CSPM may prefer its cross-cloud investigation and reporting workflow, while Datadog is worth evaluating when identity findings belong in an existing Datadog security process.

For a single-cloud audit, AWS IAM Access Analyzer and Google Cloud Policy Intelligence offer more focused evidence for their respective environments. Before choosing, confirm that the tool covers the agent’s exact identity type and that its findings meet your reviewers’ needs; then verify required permissions, plan or license, and any regional or API setup.

Treat observed unused permissions as evidence for investigation, not automatic proof that access is safe to remove.