Generative AI has the promise to significantly improve many business processes across enterprises. Nearly all enterprises have conducted proofs of concept with many of the models available today, but the primary concern implementing these solutions is security and reliability. Improving the security of generative AI solutions can be achieved by leveraging the power and flexibility of open-source AI models.
Open-source models allow enterprises to securely integrate their data on-premises with solutions. When data is kept on-premises, sensitive data can be secured without the risk of leaking to third-party vendors. This approach also offers enterprises the flexibility and customization they need in their production environment. The challenge with open-source models is that more customization is required to safeguard against traditional security threats, such as prompt injection, data manipulation, and adversarial exploits.
In collaboration with LatticeFlow AI, SambaNova has validated a better approach to avoid security challenges when deploying models — and benchmarked the results. Just announced, open models equipped with targeted guardrails achieve near-flawless security (up to 99.6%) while maintaining >98% service quality. No longer theoretical — it has been tested and proven across rigorous enterprise attack simulations.
What we uncovered:
LatticeFlow AI released complimentary risk reports for enterprises to use. You can find links to all of them in our supported models section of SambaNova Documentation. While regulated sectors, such as finance, must deploy secure solutions for compliance reasons, every enterprise should embrace security for brand trust, IP protection, and operational resilience.
At SambaNova, we believe strongly that performance and security go hand-in-hand. We are committed to delivering fast and efficient AI inference, powered by SambaRack, in the most secure manner and helping enterprises and governments succeed in deploying their solutions to production.