Continuous network trust assurance for telecoms using distributed credentials and AI reasoning

The Challenge

The identity model within telecoms is antiquated: networks decide whether to trust a device on the basis of a single password or certificate, issued once and rarely revisited. As future networks become networks-of-networks — spanning operators, private networks and constrained rural infrastructure — operators have no way to continuously judge whether a connecting device, or the software and AI running on it, remains trustworthy. This blocks both security and the flexible business models future telecoms need.

The Solution

TrustNetZ is a suite of protocols to negotiate and interrogate the trustworthiness of network connections. Building on CAHN (Continuous Assurance for Heterogeneous Networks), NQM is developing new architectures that work across networks using advanced notions of identity and distributed credentials, combined with dynamic (AI) reasoning to continuously infer trustworthiness and assurance. Rather than relying on a single credential, TrustNetZ can use device-owner, device-manufacturer, software and device-testing evidence to inform a continuous assessment of security — and can even evaluate the trustworthiness of an AI system sitting within a device (drawing on TAIBOM).

Outcomes

The Southampton-based project contributes to the UK’s future-telecoms ambitions, including secure rural connectivity. A public demonstrator is available at cahn.nqminds.com, and the TrustNetZ device and network management protocol work has been published through NIST standards channels — a novel device and network management protocol published as a NIST standard. TrustNetZ also feeds NQM’s Synapse product, which builds on TAIBOM for AI assurance and TrustNetZ for device and network security.

TODO: record specific Phase 2 deliverables and demo outcomes.

In Detail

Negotiating and continuously assuring trust across the networks of the future.

TrustNetZ is a suite of protocols to negotiate and interrogate the trustworthiness of network connections. Rather than trusting a device on the basis of a single password or certificate issued once, TrustNetZ draws on device-owner, device-manufacturer, software and device-testing evidence — and can even evaluate the trustworthiness of an AI system sitting within a device — to continuously assess security across networks of networks.

Core benefits

• Dynamic Risk — multi-factor, 360-degree risk assessment using both device-type and device-instance data: the evidence split that underpins continuous assessment.
• Cognitive Security — reasoning under uncertainty; practical Zero Trust; controllable false positives.
• Collaborative — data shared between instances and organisations to better detect and respond.

Collaborative trust across operators

Trust negotiation across networks-of-networks requires evidence to flow between operators. TrustNetZ shares data between instances and organisations so that each network can better detect and respond, while distributed credentials keep every claim verifiable — supporting both stronger security and the flexible business models future telecoms need.