Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
Cloud & Infrastructure
The New Stack·Jun 18·5 min read
Neoclouds, sovereign AI and Postgres: The new operating model for regulated enterprises
TL;DR
Enterprises face challenges with data governance as sensitive information is often moved to external environments for AI inference, leading to security risks and inconsistencies. Neoclouds, a new operating model, aim to keep data within databases while enabling AI inference.
✦ Why It Matters
Engineers can implement neoclouds to enhance data security and streamline AI inference processes.
Key Takeaways
- Adopt a multi-cloud strategy to enhance flexibility and control.
- Utilize EDB Postgres AI for a unified operational and AI platform.
- Focus on reducing data movement to improve compliance and performance.
- Prioritize infrastructure strategy over model quality for AI success.
Full Summary
As enterprises increasingly adopt AI, the challenge of moving sensitive data for inference has become critical. Neoclouds, designed specifically for AI workloads, provide specialized GPU infrastructure that allows organizations to run AI models closer to their data, reducing latency and improving compliance.
EDB Postgres AI serves as a foundational platform, integrating operational data with AI capabilities, thus simplifying architecture and governance. Research indicates that 95% of organizations aim to become their own AI platforms, yet only 13% have succeeded, highlighting the importance of infrastructure strategy over model quality.
By consolidating AI and operational workloads on Postgres, organizations can achieve significant returns on investment and streamline their operations. This approach is particularly vital for regulated industries, where data sovereignty and compliance are paramount.