Secure AI workloads on Google Cloud with HashiCorp Vault

• Infra

As AI continues to evolve and integrate into various industries, the need for secure and efficient management of credentials becomes increasingly critical. AI workloads often involve accessing sensitive data and resources, making robust security measures essential to prevent unauthorized access and ensure compliance.

HashiCorp Vault offers a powerful solution for dynamic credential management, particularly within the Google Cloud Platform (GCP). By leveraging the Google Cloud Vault secrets engine, organizations can generate short-lived, temporary credentials that automatically expire, significantly reducing the risk of credential misuse. This dynamic approach eliminates the reliance on static, long-lived credentials, which are more vulnerable to security breaches.

In the context of AI services like Vertex AI, AutoML, and AI Platform, dynamic credential management is crucial for maintaining the integrity and security of AI models and data. Vault's integration with GCP provides a streamlined and secure method for managing access controls, ensuring that AI services can authenticate securely and access only the resources they are authorized to use. This not only enhances security but also simplifies compliance and access management across AI workloads.

Dynamic credential management with Vault

Vault's Google Cloud secrets engine generates GCP IAM credentials, such as service account keys and OAuth tokens. This approach eliminates the need for static, long-lived credentials, narrowing the timeframe in which they could be stolen and misused, and reducing risk.

Key benefits:

• Short-lived credentials: Generate temporary credentials that automatically expire, minimizing the window for potential misuse.
• Automatic revocation: Credentials are revoked upon lease expiration or manual revocation, ensuring that unused or compromised credentials do not persist.
• Granular access control: Define precise IAM roles and permissions tied to specific resources, ensuring that AI services have the necessary access without over-provisioning.
• Workload Identity Federation (WIF) Support: Vault now supports using WIF with Google Cloud, allowing the secret engine to securely authenticate to Google Cloud without the need for a password or long-lived service accounts. This approach minimizes credential sprawl and enables a trust relationship between Vault and Google Cloud, reducing security concerns associated with manually creating highly privileged security credentials when configuring the secrets engine.

Securing AI workloads

AI services often require access to sensitive data and resources. Vault's integration with GCP ensures that these services can authenticate securely and access only the resources they are authorized to use.

How Vertex AI and AutoML stay secure with Vault

Vertex AI

When deploying models with Vertex AI, Vault can generate service account keys with the necessary permissions, such as roles/aiplatform.user. These keys are used by the AI workloads to interact with GCP resources securely.

By using Vault to generate short-lived service account keys for Vertex AI, organizations can securely deploy and manage machine learning models without relying on static credentials. Vault ensures that only authorized workloads have access to required GCP resources, with automatic expiration and the ability to revoke access on demand. This reduces the attack surface, supports compliance, and provides security teams with full visibility and control over AI-related access.

AutoML

For AutoML tasks, Vault can provide short-lived OAuth tokens scoped specifically for Google Cloud access and for the task at hand — such as training, prediction, or data access. This dynamic approach enforces least-privilege access, eliminates long-lived credentials, and simplifies credential lifecycle management. As a result, teams can safely accelerate their AI development while meeting enterprise security and audit requirements.

Advanced security controls for AI workloads on Google Cloud

As AI adoption accelerates, the need for more sophisticated, cloud-native security mechanisms is growing. Vault enhances protection for AI and data-intensive workloads on Google Cloud through two key capabilities:

• In high-risk, data-intensive environments, Vault can also integrate with Google Cloud Key Management Service (KMS) to manage encryption keys that protect data at rest. This ensures that even if data is unintentionally exposed—such as by a misconfigured or over-permissive AI system—decryption is gated by Vault, providing a critical layer of control and security.
• Secrets management for Confidential Computing on GCP: Vault secures secrets and supports workloads deployed on GCP Confidential VMs by protecting data at rest, in transit, and during processing. This adds a powerful layer of runtime protection — especially important for regulated industries and organizations handling proprietary models or datasets.

Together, these features help organizations meet the highest standards for security and compliance, while simplifying operations at scale.

Getting started

Vault helps organizations move fast in AI — without compromising security. Learn how to get started with the Google Cloud Vault secrets engine and secure your GCP workloads today by signing up for HCP Vault for free.