How to Give AI Agents Scoped, Policy-Controlled Cloud Access (7 Platforms Compared)

AI agents should not receive long-lived cloud credentials; instead, they should authenticate through individual short-lived identities to a broker or bounded API that permits only approved actions, while separate execution identities perform changes with tightly scoped, temporary access. The proposed governance model combines five layers: per-agent identity, an allow-listed action surface, policy-as-code checks before deployment, immutable audit records tied to each agent run, and hard spending or quota limits. It argues that identity-time controls such as IAM roles and organizational policies, plan-time controls such as Open Policy Agent, Sentinel, and Pulumi CrossGuard, and runtime controls including quotas, budget actions, TTLs, and auto-stop are all necessary because no layer alone prevents security, configuration, and cost failures. The comparison finds that Open Policy Agent focuses on policy evaluation, HashiCorp Terraform with Sentinel and Vault combines policy with dynamic credentials, Pulumi offers code-based guardrails and short-lived OIDC access, cloud-native IAM supplies granular native controls, Crossplane creates constrained Kubernetes APIs, Scalr provides Terraform/OpenTofu automation with OPA, and Qovery offers a restricted deployment-oriented API for application environments. The recommended architecture uses OIDC-based identities, curated templates or modules rather than raw cloud access, pre-apply policy validation, credentials lasting roughly 15 minutes or less, resource tags containing agent and run identifiers, and human approvals for high-risk actions such as IAM changes, public exposure, production writes, data deletion, or substantial new spending.

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