Newsroom
Introducing Nemesis: Graph-Native Cyber Intelligence for the Interconnected Risk Era
RiskScan 2026, the annual cross-market research study published jointly by the Insurance Information Institute and Munich Re US, surveyed more than 1,700 insurance professionals and consumers across the US and UK. Its headline finding is unambiguous: cyber incidents are the dominant concern across every segment of the insurance market, losses are rising, severity is increasing, and the threat remains stubbornly difficult to predict.
Newsroom 9 June 2026
RiskScan 2026, the annual cross-market research study published jointly by the Insurance Information Institute and Munich Re US, surveyed more than 1,700 insurance professionals and consumers across the US and UK. Its headline finding is unambiguous: cyber incidents are the dominant concern across every segment of the insurance market, losses are rising, severity is increasing, and the threat remains stubbornly difficult to predict.
What RiskScan also identifies
What RiskScan also identifies, and what deserves more attention than it typically receives, is why cyber risk is so hard to price and contain. The answer is structural. Cyber does not behave like a conventional peril. It propagates. A single compromised node becomes a vector. A supplier breach becomes a portfolio event. A claim in one line triggers liability in three others. The threat is not isolated — it is relational, and the connections are what matter.
Conventional cyber risk tools were not built to see relationships. They were built to score entities: this company, this policy, this exposure. They produce snapshots. What they cannot produce is the map of how risk moves between nodes, amplifies across supply chains, and concentrates in ways that only become visible when you trace the connections rather than assess the components.
That is the problem Nemesis was built to solve.
Nemesis is Panamorphix's graph-native cyber intelligence product, built on CongDB and designed to model cyber risk as what it actually is: a network phenomenon. Where conventional tools see individual exposures, Nemesis sees propagation paths. Where others produce risk scores, Nemesis produces risk topology, the shape of exposure across an interconnected portfolio, with the relationships between entities carrying as much analytical weight as the entities themselves.
The architecture draws on a lineage of graph-based threat intelligence thinking, including the frameworks developed for persistent threat analysis in high-consequence environments. The core premise is the same: adversarial activity leaves a graph signature, and the most important intelligence is not what happened at a given node but how the event connects to everything around it.
Nemesis is currently in build. We are working with a small number of partners in the cyber underwriting and reinsurance intelligence space as we develop the product. If you are working on cyber portfolio exposure, accumulation modelling, or threat propagation analysis and want to understand what graph-native intelligence looks like in practice, we would like to hear from you.