Multi-operator and 6G workloads on a confidential computing substrate — validated through EU Horizon Europe research and built with global telecom leaders.
Telecommunications involves multiple operators who need to collaborate on shared infrastructure without exposing proprietary network data to each other or to cloud providers. Conventional AI APIs cannot support this trust model.
Private AI for telecom: network intelligence, anomaly detection, and operational AI running inside your infrastructure — never exposing network data to external parties.
The TEE substrate validated through CONFIDENTIAL6G and ELASTIC — purpose-built for telecommunications security.
Explore Cocos AIConfidential AI for telecommunications is the deployment of AI workloads — network anomaly detection, traffic optimization, predictive maintenance, and multi-operator analytics — inside Trusted Execution Environments, so that proprietary network data is never exposed to cloud providers, other operators, or infrastructure administrators. The hardware enforces the boundary; no data leaves the operator's perimeter in readable form.
Each operator runs its portion of the workload inside a TEE. Inputs from each operator are sealed in hardware — the other operators and the platform operator cannot read them. Only the agreed result — a joint anomaly score, a shared model weight update, or an aggregated metric — is released. Remote attestation proves which code produced the result, satisfying cross-operator trust requirements without contractual data-sharing agreements.
CONFIDENTIAL6G is a €5 million EU Horizon Europe research project validating confidential computing infrastructure for 6G networks. Ultraviolet's Cocos AI is the confidential computing substrate in this project, working alongside global telecom leaders. The project demonstrates that TEE-based AI can meet the security and latency requirements of next-generation mobile network infrastructure.
Key AI use cases in telecommunications include: network anomaly and intrusion detection (requires processing raw traffic data), predictive maintenance on network hardware, dynamic spectrum and traffic optimization, multi-operator roaming analytics, customer churn prediction, and 6G slice management. All of these involve sensitive network intelligence that operators cannot safely send to a third-party cloud.
Yes. Cube AI supports edge deployment for distributed network sites where latency requirements make centralized processing impractical. TEEs are available on edge-class hardware including NVIDIA platforms with confidential GPU support. This enables anomaly detection and traffic optimization to run locally at a base station or edge data centre, with results aggregated centrally.
Network intelligence — traffic patterns, subscriber behavior, optimization algorithms — is core competitive IP for any operator. Sending this data to a shared cloud AI platform exposes it to the platform provider and, potentially, to other tenants. TEE-based deployment seals the data in hardware: even the infrastructure vendor and the platform operator cannot read it. Competitive intelligence stays inside the operator.
Talk to the team about telecommunications AI deployments, 6G security requirements, and multi-operator collaboration.
