AI Development Tools that Security Teams Should Know About and How to Secure Them

Following the rush to Artificial Intelligence (AI), many companies have introduced new tools and services to the software supply chain. Some of today’s most popular AI development tools include:

• OpenAI: A research and development company that’s spearheading AI technologies such as ChatGPT and Codex.
• Chroma: A software platform for managing AI projects.
• Weaviate: A vector database for storing and retrieving AI data.
• Hugging Face: A platform for sharing and accessing AI models.
• Cohere: A company that provides AI services like text generation and translation.
• Claude: An AI-assistant by Anthropic.
• Weights & Biases: A platform for tracking and managing AI models.
• LanceDB: A database for storing and retrieving AI models.
• Supabase: A backend-as-a-service platform that supports AI development.
• Pinecone: A vector database for storing and retrieving AI data.

This assortment of tools can be used to develop a wide range of AI applications, such as chatbots, virtual assistants, and image recognition systems.

What are the security challenges of developing AI with these tools?

While AI development tools can offer many benefits, they also introduce another layer of risk, complete with its own security challenges. The top risks include:

• Proliferation of of secrets and credentials: AI development tools often require access to sensitive data like API keys. However, it’s of paramount importance to keep secrets and credentials out of source code and SaaS apps.
• Rapidly evolving landscape: It can be difficult to keep up with the latest AI development tools—but it’s important to understand the unique security implications of using each of them.

What can you do to develop AI securely?

There are a number of things that organizations have to keep in mind while developing AI. However, the first step is to implement a continuous secret scanning program to identify and remediate secrets and credentials in source code and SaaS apps. As an AI-powered leader in cloud data leak prevention (DLP), Nightfall is not only familiar with the risks posed by AI development tools, but also can help developers take action by automating detection and remediation of secrets.

Nightfall uses a variety of techniques to detect secrets and credentials right out of the box. These techniques include:

• Natural language processing (NLP) to identify patterns in text.
• Machine learning (ML) to identify patterns in data.
• A dynamic knowledge base of known patterns.

As a result of this quick and accurate detection, organizations don’t have to write, update, or rely on endless lists of static regexes and custom validators.

In addition to implementing a continuous scanning program, organizations should also take the time to educate their developers about the importance of security in AI development. Developers should be aware of the risks of exposing secrets and credentials, and they should take steps to protect themselves, their colleagues, and their companies.

How do I implement secret scanning?

If you're using GitHub, you can integrate Nightfall directly with GitHub to scan new commits.

Similarly, you can use Nightfall APIs, documented at docs.nightfall.ai, to scan any text or file payloads using our detectors for secrets & credentials. Sign up for Nightfall’s Firewall for AI, then complete the Quickstart to make your first API request.

What are some additional tips for securing AI development?

Here are a few more tips to stay secure while developing AI:

• Use a least privilege approach when granting access to AI development tools and services. Only give developers the access they need to do their job.
• Use a strong authentication and authorization system to control access to AI development tools.
• Implement a security monitoring solution to detect and respond to security incidents in AI development environments.
• Keep AI development tools and services up to date with the latest security patches.

By following these tips, organizations can help to protect their AI development environments from security threats.

Conclusion

The development of AI has introduced a number of new tools and services into the software supply chain. These tools and services can be used to develop a wide range of AI applications, but they also introduce a number of new security challenges.

Organizations can secure AI development by implementing a continuous scanning program to identify and remediate secrets and credentials in source code and SaaS apps. They should also educate their developers about the importance of security in AI development.