Today is an important milestone for Prevalent AI, as it marks the first time in our nine-year history that we have raised primary capital. My co-founders and I are excited to work with Integrity Growth Partners (IGP) on this round, and we look forward to a close partnership as we continue to build our business globally, but it is a good moment to reflect on our journey to this point.
When we started Prevalent AI in 2017, we were thinking differently about some of the security challenges that large, complex organisations faced. At the heart of many of these problems was the same origin, an inability to make effective use of the fragmented, contradictory data generated by the hundreds of different security, networking and IT management tools that these companies typically have.
To help customers to get value from this data, we needed to integrate with all the different tools and systems they had deployed across the cloud, SaaS applications and legacy on-premises systems, and deliver them a unified, context rich view of their environment that understood their business criticality, assets, users, applications, security controls and the relationships between them.
The importance of the Data Fabric
Our approach to this was our AI-powered Data Fabric, which automatically ingests data from hundreds of sources, normalizes it against a common ontology, resolves duplicate records into single unified entities, and organizes everything into a live Knowledge Graph. The output is one clean, connected, continuously updated picture of our customer’s entire environment. The Knowledge Graph allows customers to query the data, uncovering security coverage gaps, control drift, unmanaged systems and other security problems that had previously been undiscovered.
What is different about Prevalent AI’s progress to this point is that we have been completely customer driven. By choosing to bootstrap the business, we have built our revenue and value on customer growth, working with some of the largest, most complex organisations globally, achieving consistent profitability based on successfully delivering meaningful customer outcomes. This is due to the support & partnership of our customers, a long-term commitment for which we are very thankful, and to the hard work and expertise of the Prevalent AI team in both the UK and India.
We made two important early decisions that have helped us meet those customer expectations;
First, we understood the value of the intelligence we were generating. Prevalent AI was founded by alumni of GCHQ, practitioners who spent their careers solving some of the hardest data and intelligence problems in the world so it was critical that we respected our customers ownership of their data.
Data Sovereignty has been at the heart of our solution from the very beginning, none of our customers want these critical insights in the public cloud, so we support them by delivering the solution either through private cloud or on-premises deployments, this flexibility has been critical for our adoption by large, heavily regulated companies in Financial Services, Healthcare and Critical National Infrastructure.
We are now seeing a huge increase in awareness of the need for data sovereignty because of the growth in regulation and control around AI models, as technology leaders are recognising the importance of remaining in control of their own data.
The second key decision was that customers would need a deeply integrated partnership with Prevalent AI to ensure successful outcomes when we thought about the complex requirements of large, global enterprises, so we pioneered a ‘build with’ model, where our solutions architects, managed services and engineering teams have been deeply embedded with our customers, designing, deploying, and continuously developing the platform around their operational needs.
These decisions, together with the capability of our data fabric and knowledge graph, have allowed us to develop deep, multi-year relationships with our customers, and to grow strategically and profitably, so why take external funding now?
The new market reality
The reality is that the market in 2026 has changed. The volume of security data generated has never been larger, due to technology sprawl, but the biggest factor has been the growth of AI. The same challenges that Prevalent AI has been focusing on for the past nine years have been exacerbated by the rise of AI in many ways.
The impact of AI on attacker behaviour and velocity has been huge, with a dramatic increase in both the volume and sophistication of attacks, and a corresponding decrease in the time to exploit. Security teams need the trusted enterprise context and clarity provided by the knowledge graph to keep them in the race.
Equally, for AI projects to be successful, they need high-quality data on which to base their reasoning and decision making. In most organisations, that is not possible today. In fact, Gartner predicts that more than 40% of agentic AI projects will be cancelled by the end of 2027, in part due to these uncertain foundations.
The same AI and data science led approach to clean, connect and contextualise enterprise data that we have been delivering for organizations for the past nine years has now become a key foundation for technology leaders who want to both protect their organizations, and also to take advantage of the advances in agentic and frontier AI. It’s fair to say that in many ways, the market is now ready to take advantage of the trusted context layer that Prevalent AI has always provided.
As our customer’s requirements expand and evolve, then so do we. This external investment will support our continued investment in our team, the technology and the services to expand our capabilities to meet the demand. When we look at our existing customers, we see that while they start with cybersecurity use cases such as exposure and vulnerability management, controls monitoring and insider threat, the same data models that build the trusted context here are equally applicable in other domains.
In today and tomorrow’s world, technology leaders know that to be successful they will need to support different AI models in different jurisdictions, that they will require enterprise context and semantic understanding to allow them to take advantage of the structured and unstructured data they have today. These are all problems that Prevalent AI has solved for customers, first in cybersecurity, but increasingly in non-cyber use cases across IT, financial services and business operations. This funding allows us to build on the success we have had so far, and to ensure that we can continue to support our customer and the wider market.
You can read the full details of our new funding round here, and I would encourage you to sign up to hear more about what’s coming from Prevalent AI over the upcoming months.