Cloud Vault Key Rotation for Secure GenAI Model Weight Deployment
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Solution Overview
Problem
The rapid advancements in generative artificial intelligence (GenAI) have highlighted the challenges of governance, security, and privacy, particularly in securing the weights of GenAI models within cloud platforms to prevent unauthorized access and data breaches.
Innovation Solution
A secure integration system for GenAI platforms in cloud services, utilizing encryption keys (DEK and KEK) to encrypt and decrypt model weights, deploying them in isolated namespaces, and managing access through service accounts to ensure data privacy and security throughout the model's lifecycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If model weights are stored in cloud storage for GenAI platform integration, then model deployment and inference capabilities are enabled, but security risks and vulnerability to unauthorized access increase
Solution Approach 1:
The patent introduces cloud vault as an intermediary component that mediates between cloud storage and model weights. The cloud vault securely stores encryption keys and manages key rotation, acting as a security intermediary that enables model deployment while preventing unauthorized access to the actual model weights in cloud storage.
Solution Approach 2:
The patent implements dynamic parameter changes through key rotation mechanisms. Encryption keys are periodically rotated and updated, changing the security parameters of the stored model weights. This ensures that even if weights are accessed unauthorizedly, the encryption credentials are invalidated, maintaining security while enabling continuous model deployment.
2Reliability
If encryption keys are managed within the cloud platform, then model security is improved, but system complexity and key management overhead increase
Solution Approach 1:
The cloud vault is designed as a universal key management service that handles multiple functions: storing encryption keys, managing key rotation, controlling access permissions, and securing model weights. This multi-functional approach consolidates key management complexity into a single unified system rather than distributing it across multiple separate components.
Solution Approach 2:
The key management system implements self-service capabilities through automated key rotation and generation. The cloud vault automatically manages the lifecycle of encryption keys without requiring manual intervention, reducing operational complexity while maintaining high security standards for model weights.
3Reliability
If model weights are encrypted with multiple keys for enhanced security, then data protection is improved, but processing time and computational overhead increase
Solution Approach 1:
The encryption system is segmented into two distinct layers: data encryption keys (DEK) that encrypt the actual model weights, and key encryption keys (KEK) that secure the DEKs in the cloud vault. This segmentation allows efficient decryption by first retrieving and decrypting the DEK using KEK, then using the DEK to decrypt model weights, optimizing the time-consuming encryption operations.
Solution Approach 2:
The system performs preliminary action by pre-loading and caching decryption keys in the cloud vault before they are needed for model deployment. The cloud vault maintains ready-to-use encryption credentials, eliminating the need for time-consuming key generation and setup during critical model deployment and inference operations.
4Object-affected harmful factors
If cloud vault is used for key management and rotation, then security against data breaches is improved, but system infrastructure complexity increases
Solution Approach 1:
The cloud vault implements a nested security structure where encryption keys are nested within the vault, which itself is nested within the cloud platform infrastructure. The cloud vault contains and protects the DEKs, which in turn protect the model weights in cloud storage. This nested arrangement provides multiple layers of security while consolidating infrastructure complexity into a single integrated component.
Data Source
AI summary
The present disclosure relates to secure deployment of model weights from a generative artificial intelligence (GenAI) platform to a cloud service. The method includes accessing the model metadata and a set of weights of a GenAI model associated with a GenAI platform. These model weights may be encrypted using a first encryption key that may be provided in the model metadata. These encrypted model weights may be decrypted based on the model metadata by utilizing the first encryption key from the model metadata. Each key may be associated with the specific type of GenAI model. Before storing the model weights from the GenAI platform cloud tenancy to a cloud storage in GenAI home region, the model weights may be encrypted again by utilizing a second encryption key. This encryption by the cloud may enable independent control over the sensitive information during transit and storing.