An illustrative case study of how NeuroFlux Systems safeguarded their AI systems with Hopr's Enterprise Ultra platform.
In 2024, AI innovators like NeuroFlux Systems face threats that steal credentials for workloads and services, and aren't able to verify workload identity trust across cloud boundaries or with third-parties.
Hopr's Enterprise Ultra enables NeuroFlux Systems to safeguard its AI systems, algorithms, and highly sensitive data.
NeuroFlux deploys its AI code and models across AWS, GCP, and on-premise GPU clusters. AI agents repeatedly modeling dynamic financial data communicate via APIs using mTLS .
But the end-to-end encryption breaks at ingress controllers and service mesh proxies, and data is exposed.
Third-party workloads provide financial data to NeuroFlux Systems API endpoints using static API keys to authenticate. Keys are rotated every 12–24 hours using cloud-native tools like AWS Secrets Manager. But injecting the rotated keys into the authenticating endpoint exposes them to theft.
The NeuroFlux Systems AI workloads receive the third-party financial data, process it, and provide forecasts to NeuroFlux System's financial trading customers.
But trust with third party workloads is implicit — based on cloud IAM roles and PKI certs.
NeuroFlux Systems is a fast-growing AI startup developing proprietary machine learning agents for real-time financial forecasting and autonomous trading. Their business relies on the integrity of real-time data feeds from third party commodity exchanges. They deployed Enterprise Ultra with their AI microservices in distributed environments. They also worked with their third party data providers to ensure Korvette-SE WoSPs were deployed with third party edge workloads.
Designed for the Zero Trust era, microsharded ephemeral attack surface, and secure and trusted internal and edge API transactions.
NeuroFlux Systems' proprietary AI workloads could not trust the identities of third party workloads providing data or receiving automated trading orders.