In this guest blog post, David Wright, Cloud Technology Lead at HashiCorp, outlines how to create secure AI applications on Microsoft Azure with HashiCorp Terraform and HCP Vault, incorporating automation with Terraform for consistent security practices. Terraform is available in Azure Marketplace.
Even as cloud investments continue to grow, the biggest benefits, according to the fourth annual HashiCorp State of Cloud Strategy Survey, are going to a small group of truly cloud-mature organizations.
Security has always been a key issue in the cloud, both as a goal and a barrier. In the HashiCorp State of Cloud Strategy Survey:
- 89 precent of respondents saw security as a key driver of cloud success;
- security was named the second most important cloud inhibitor (after cost concerns).
Those issues are still with us, although the particulars continue to evolve. Cloud success continues to be dependent on security and the tools that support it. For example, security is once again the top factor in cloud success and the most common benefit from a cloud strategy. This year, data protection (77 percent) is joined by secrets management (75 percent) and access control (75 percent) in the top tools/initiatives seen as critical to the success of the organization’s cloud strategy.
Generative AI and cloud security
Generative AI (artificial intelligence) has the potential to play a critical role in the future of cloud strategy. Though generative AI remains in the early stages, many customers believe it can help address critical cloud issues like security, skills, availability, and scalability.
There have been instances in which AI models, including ChatGPT, have inadvertently exposed sensitive data. For example, a bug in ChatGPT allowed some users to see the titles of other users' conversations, and potentially exposed payment-related information of a small percentage of users. This incident highlights the importance of implementing robust security measures when dealing with AI applications to prevent unauthorized access and data leaks.
In the current digital era, safeguarding sensitive information is crucial, particularly when handling compliance-related datasets.
Automating encryption with HashiCorp Cloud Platform (HCP) Vault plus Terraform
HashiCorp's HCP Vault is a managed service with a powerful encryption engine designed to secure data, preventing unauthorized access and accidental disclosures. It offers encryption as a service, allowing you to encrypt data at rest and in transit. This is crucial for compliance with standards like PCI DSS (payment card industry-data security standard), which mandates stringent data protection measures.
Automation is key to maintaining consistent security practices. Terraform, an infrastructure as code tool, allows you to automate the deployment and management of HCP Vault, ensuring encryption practices are consistently applied across your infrastructure. Here’s how you can automate the encryption process using HashiCorp Terraform (Cloud and Enterprise) in Azure Marketplace:
- Define infrastructure as code: Use Terraform to define your infrastructure, including the setup of HCP Vault. This ensures your encryption setup is repeatable and consistent.
- Automate key management: Automate the creation and management of encryption keys using Terraform scripts. This reduces the risk of human error and ensures keys are rotated and managed securely.
- Integrate with CI/CD (continuous integration/continuous delivery) pipelines: Integrate Terraform scripts into your CI/CD pipelines to automate the deployment of encrypted resources. This ensures all new deployments adhere to your encryption policies.
By automating these processes, you can ensure your encryption practices are consistently applied, reducing the risk of data breaches and ensuring compliance with PCI DSS requirements.
Encrypting PCI and compliance data
With HCP Vault set up via automation, you can use its encryption engine to secure PCI and compliance data. Here’s how:
- Enable the transit secrets engine: This feature allows you to encrypt and decrypt data without storing the data in Vault. It is ideal for protecting sensitive information such as PCI data.
- Create an encryption key: Generate a key that will be used to encrypt and decrypt your data. This key is managed by Vault, ensuring it is stored securely.
- Encrypt data: Use the encryption key to encrypt sensitive data before storing or transmitting it. This ensures that even if the data is intercepted, it cannot be read without the decryption key.
- Decrypt data: When you need to access the data, use the decryption key to convert it back to its original form. This process ensures only authorized users can access the sensitive information.
By using the transit secrets engine, you ensure sensitive data is encrypted before being stored or transmitted, meeting PCI DSS requirements.
Ensuring data security in AI applications
AI applications often consume large amounts of data, including sensitive information. While AI can provide significant insights, it also poses security risks. Here’s how to mitigate these risks:
- Data access controls: Implement strict access controls to ensure only authorized entities can access sensitive data. Use HCP Vault to manage secrets and credentials securely.
- Data masking: Mask sensitive data before feeding it into AI models. This prevents the AI from learning and potentially exposing sensitive information.
- Audit logging: Enable audit logging in HCP Vault to track access and usage of sensitive data. This helps identify and respond to unauthorized access attempts.
- Regular security reviews: Conduct regular security reviews and audits of your AI applications and data handling processes to ensure compliance with security standards.
Using HCP Vault's encryption engine provides a robust solution for securing PCI and compliance data. By following best practices and leveraging the capabilities of HCP Vault, you can ensure that sensitive data remains protected against unauthorized access and accidental leaks. Additionally, implementing strong security measures in AI applications helps maintain data integrity and compliance, even in complex environments.
By integrating these practices, you can build secure, compliant, and efficient AI applications on Azure, leveraging the power of HashiCorp Terraform and Vault.
