Prevalent AI
by Prevalent AI
A sovereign enterprise knowledge graph that gives security teams and AI agents context
Prevalent AI builds a data fabric that ingests hundreds of fragmented security, DevOps and cloud sources and normalises them into a continuously updated sovereign knowledge graph of entities, relationships, dependencies, vulnerabilities and identities. It is aimed at CISOs and heads of data at banks, telcos and critical national infrastructure operators whose AI agents are failing for lack of trustworthy enterprise context rather than lack of models.
Prevalent AI, founded in London in 2017 by former GCHQ and Darktrace leaders, sells an AI-powered data fabric that turns fragmented enterprise data into a continuously updated knowledge graph — what the company markets as "enterprise context, sovereign by design." The platform ingests and normalises data from hundreds of security, DevOps and cloud sources, then models entities, relationships, dependencies, vulnerabilities and identities as a graph so that security teams and AI agents work from one trusted operational view instead of reconciling tool outputs by hand. It is packaged in three modules: Security Data Fabric for ingestion and contextualisation, Exposure Management for identifying, prioritising and remediating risk, and AI Solutions for deploying AI grounded in that context. The deployment posture is the differentiator — data stays inside the customer's own environment, independent of shared infrastructure or model-provider lock-in, which is what makes it saleable to central banks, telcos and critical national infrastructure. On 19 August 2026 the company announced a $22 million growth investment from Los Angeles-based Integrity Growth Partners, the first primary capital in its nine-year history after bootstrapping to profitability from its first customer; Istari, a Temasek subsidiary, had earlier taken a minority stake via a 2021 secondary. Leadership includes co-founders Paul Stokes (CEO) and Arun Raj (COO), former GCHQ Director Sir Iain Lobban, former GCHQ Deputy Director Andrew France, and CTO Nitin Maini, appointed September 2026. The company says annual recurring revenue more than doubled in the preceding twelve months, and cites an anonymised global insurer that cut executive security report generation time by 95% and an international banking group that improved incident detection by over 80%.
A CISO or head of security data at a bank, telco or critical national infrastructure operator whose agentic AI projects are stalling because no single system holds trustworthy, connected context about assets, identities and exposures.
One sovereign knowledge graph that both human analysts and AI agents query — built inside your own environment, with no dependency on a shared platform or a model provider.
At a Glance
- Category
- Data & Analytics
- Pricing
- Contact for pricing, Subscription
- Target Market
- CISOs, CIOs, Heads of Security Operations, Data Architects, Risk and Compliance Leaders
- Deployment
- Self-hosted, Hybrid, Cloud-first
- Founded
- 2017
- Headquarters
- London, United Kingdom
Key Features
- ✓Sovereign knowledge graph
Models entities, relationships, dependencies, vulnerabilities and identities as a continuously updated graph inside the customer's own environment.
- ✓Automated multi-source ingestion
Ingests and normalises data from hundreds of security, DevOps and cloud sources without analysts reconciling tool outputs manually.
- ✓Security Data Fabric module
Cleans, connects and contextualises fragmented security data so downstream teams and tools work from one trusted operational view.
- ✓Exposure Management module
Identifies, prioritises and continuously remediates risk using the graph's relationship context rather than isolated scanner severity scores.
- ✓AI Solutions layer
Deploys AI and agents grounded in that trusted enterprise context, which is what the vendor argues prevents agentic projects from failing.
- ✓No model-provider lock-in
Data stays under customer control and independent of shared infrastructure or any single model provider's platform commitments.
Capabilities
Use Cases
- •Executive security reporting
A global insurance customer reduced the time taken to generate executive security reports by 95% using the contextualised graph.
- •Improving incident detection
An international banking group reported incident detection rates improving by more than 80% after connecting fragmented telemetry through the fabric.
- •Continuous exposure management
Security teams prioritise vulnerabilities by real asset relationships and dependencies rather than by raw scanner severity alone.
- •Grounding enterprise AI agents
Agents query a single trusted context layer instead of stitching answers together from disconnected tools with conflicting entity definitions.
- •Financial crime and compliance analysis
The same graph is applied beyond cybersecurity to financial crime analysis, operational intelligence and regulatory compliance workloads.
Ideal For
Best For
- ✓Consolidating fragmented security, DevOps and cloud telemetry into one normalised, queryable operational view
- ✓Continuous exposure management: identifying, prioritising and remediating risk across assets, identities and vulnerabilities
- ✓Grounding enterprise AI agents in trusted context so they act on connected data rather than isolated tool outputs
- ✓Regulated sectors — banking, telecoms and critical national infrastructure — that require sovereign, in-environment data handling
- ✓Executive and board-level security reporting that currently takes analysts days to assemble by hand
Not Ideal For
- ✗Mid-market or cloud-native teams wanting a quick SaaS deployment — this is a sovereign, in-environment platform sold through enterprise sales with no self-serve option
- ✗Buyers who need public pricing, a free tier or a trial before engaging: none exists, and no list price is published anywhere
- ✗Organisations that already run a mature security data lake and graph-based asset inventory, where the overlap with existing spend needs justifying before adding another layer
Deployment
Market Analysis
Pros
- ✓Nine years of operation, bootstrapped to profitability, with annual recurring revenue reported to have more than doubled in the last twelve months
- ✓Founding and leadership bench drawn from GCHQ and Darktrace, including former GCHQ Director Sir Iain Lobban and Deputy Director Andrew France
- ✓Sovereign, in-environment deployment removes the data-custody objection that blocks shared-SaaS tools at banks, telcos and critical national infrastructure
- ✓Addresses a measurable failure mode — Gartner projects over 40% of agentic AI projects will be scrapped by end of 2027 on cost, unclear value and weak risk controls
Cons
- ✗Go-to-market is thin and only now being funded — the $22M is explicitly earmarked for building sales, marketing, customer success and a US presence the company largely lacks
- ✗The headline outcomes (95% faster executive reporting, 80%+ better incident detection) are anonymised vendor case studies with no named customer, baseline or methodology
- ✗No independent review presence at all: no G2, Capterra or Gartner Peer Insights listing, and no Hacker News or Reddit practitioner discussion to cross-check claims against
- ✗No published pricing, no free tier and no trial, so evaluation requires a full enterprise sales engagement
- ✗Overlaps meaningfully with security data lake, CAASM and graph-database spend a mature enterprise may already carry, making the incremental case harder to build
Pricing
Enterprise
Contact for pricing
- ✓Security Data Fabric
- ✓Exposure Management
- ✓AI Solutions on trusted context
- ✓Sovereign deployment inside the customer environment
No pricing is published — there is no list price, no free tier and no trial, and the platform is sold through enterprise sales to banks, telcos and critical national infrastructure operators. Because it deploys inside the customer's own environment and ingests hundreds of sources, expect scope-based annual contracting driven by data volume, source count and modules selected; budget separately for the infrastructure the fabric runs on.
Security & Compliance
Sources
This page was written from 4 sources, 3 on domains other than prevalent.ai.
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