Generative AI Vendor Safety
Generative AI Vendor Selection: A Comprehensive Checklist
Part 4 of 6 in “Generative AI and Comprehensive Enterprise Safety”
© Deepam Mishra 2023. All Rights Reserved | www.tbicorp.com
Building Generative AI-based solutions is a multifaceted endeavor that often involves collaborating with various vendors. These vendors provide AI models, DevOps tools, data, services, specialized databases, cloud infrastructure etc.. Each vendor can significantly impact the overall safety and compliance of your AI system. To ensure a secure and compliant solution, it’s crucial to ask the right questions and conduct thorough due diligence when selecting vendors.
In this article, we provide a comprehensive checklist for vendor selection to help you make informed decisions.
1. Check Security Credentials
Certifications: Inquire about the vendor’s security certifications, such as ISO27001 and SOC2. These certifications demonstrate their commitment to data security and compliance. Verify how often these certifications are renewed to ensure ongoing adherence to security standards.
2. Check Incident Response Plans
Comprehensive Plan: Does the vendor have a well-defined and comprehensive incident response plan in place? In the event of a security breach or other incidents, a clear plan is essential for mitigating risks and minimizing potential damage.
3. Verify Data Compliance
Data Regulations: Ensure that the vendor complies with data privacy regulations such as GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act). Data compliance is crucial to protect the privacy and rights of individuals whose data is processed by your AI solution.
4. Vector Database Credentials
Database Location: Determine the location of the vector database. Is it hosted in the cloud provider’s instance or the vendor’s own instance? Verify all relevant credentials to ensure secure access and management of the database.
5. Cloud Provider Offerings
Architecture Choices: Cloud providers offer various architecture choices, each with its own set of trade-offs. It’s essential to understand which architecture aligns best with your AI solution’s requirements. Consider factors such as scalability, cost, and security when making this decision.
6. LLMops Tools
Tool Inventory: Take inventory of all the tools provided by vendors that are involved in your AI system. This includes tools like Langchain, LlamaIndex, and model monitoring solutions. Even seemingly minor tools can introduce vulnerabilities if not properly vetted for security and compliance.
7. Model Hosting Provider
Model Location: Determine where the AI model is hosted. Is it hosted in your own cloud instance, your cloud provider’s managed instance, or a multi-tenant environment? Understanding the model’s hosting environment is crucial for assessing security and performance.
To further enhance your vendor selection process, consider the following best practices:
- Vendor Reputation: Research the vendor’s reputation in the industry. Customer reviews and case studies can provide valuable insights into their track record.
- Contractual Agreements: Draft clear and comprehensive contractual agreements that outline the vendor’s responsibilities regarding security, compliance, and incident response.
- Regular Auditing: Implement a system of regular audits and monitoring to ensure that vendors continue to meet security and compliance standards throughout the partnership.
- Data Ownership: Clarify data ownership and access rights in your contractual agreements to protect your organization’s data.
By addressing these key considerations, you can make well-informed decisions when selecting vendors for your Generative AI-based solutions. Remember that the safety and compliance of your AI system depend not only on the quality of the AI model but also on the reliability and security of the tools and services provided by vendors.
In conclusion, vendor safety is a critical aspect of building secure and compliant Generative AI solutions. By diligently assessing vendor security credentials, incident response plans, data compliance, and other relevant factors, you can mitigate risks and create a robust AI system that not only delivers exceptional results but also safeguards the integrity and privacy of your data. Remember that vendor selection is not a one-time process; ongoing monitoring and collaboration are essential to maintaining the safety and compliance of your AI solution.
Previous Articles in Series
- Part 1: An Intro: Generative AI & Enterprise Safety
- Part 2: AI Model Safety
- Part 3: Data Safety in Generative AI
Next Up (coming soon): Part 5 — Generative AI Output Safety
