Risk Ledger Raises £24M Series B for AI and US Expansion

Third-party risk management has quietly become one of the more expensive workflows inside a large enterprise, and Risk Ledger's Series B is a bet that the fix is to turn it into a shared network rather than another questionnaire tool. SecurityWeek reports that the London-based company raised £24 million (roughly $32.3 million) in a round led by Axiom Equity, with previous investor Mercia Ventures also participating. That brings the total raised to £33.8 million (roughly $45 million) since the company was founded in 2018.

The pitch is that instead of every enterprise sending its own bespoke supplier questionnaire, Risk Ledger runs what it calls a network-first platform, where more than 16,000 organizations across financial services, critical national infrastructure, government and insurance already share standardized supplier profiles that are maintained in real time. Axiom Equity founding partner Jonathan Organ told SecurityWeek that "Risk Ledger is creating a category rather than competing in an old one," and that the network it has built is "hard to replicate and grows more valuable with every organisation that joins." It is the classic network-effects argument applied to a workflow most CISOs would rather delete than manage.

The proceeds are earmarked for growing that network, deepening the intelligence shared inside it, and building more AI tools to "automate reviews and surface risk signals," alongside a first push into the US. That US move is the interesting part for anyone watching third-party risk. The market already has entrenched incumbents there, and a UK-heavy network of suppliers does not automatically translate into a useful signal for a US bank or federal contractor. Whether Risk Ledger's shared assessment format gets accepted by US procurement teams is a live commercial question, not a settled one.

The honest caveat is that the announcement is light on the things a serious buyer would want to see. There is no disclosed valuation, no revenue figure, no named US launch customer, and no detail on which AI techniques the automated reviews will actually use. Take the specifics as reported, not as a full picture. What is worth watching is whether shared, network-based supplier assessments start eroding the questionnaire-plus-consultancy model that still dominates enterprise procurement, and whether AI-automated reviews really do compress a process that today can take weeks per supplier.