Our manifesto for graph-powered intelligence analysis

Introduction

The world is a web of connections buried under an ever-growing flood of fragmented information.

Even top analysts struggle to identify and track fast-moving threats in this environment when the tools they use operate as if the world were flat and static.

Intelligence teams are expected to explain how people, events, organisations, and assets connect. But they’re trying to understand complex, evolving networks using tools built for records, tables and linear workflows. Relationships are treated as secondary. Context is reconstructed manually. Mission-critical insight depends on individual, manual effort.

This gap is not sustainable. As threats become more interconnected and data volumes grow, approaches that fragment information and obscure relationships become a potentially fatal weakness, delaying timely intelligence.
Graph-powered intelligence analysis is the solution to the broken model. By modelling the world in terms of connections, graphs reflect reality as it is, not as legacy systems assume it to be.

This manifesto sets out our view on intelligence analysis today, why the prevailing approach is holding teams back, and why graph-powered intelligence analysis is the necessary shift forward.

Our beliefs

Principles shaped by real intelligence work

Intelligence work rarely starts with a complete picture. Teams work with partial information, evolving cases, and fragmented sources, building understanding over time as new signals emerge.

These beliefs reflect what supports that work in practice, from making sense of relationships to preserving context as investigations develop and complexity grows.

1. Connections matter

Every entity exists within a dynamic network of relationships, essential for understanding context, revealing patterns, and predicting outcomes. In intelligence analysis, connections are as vital as the entities themselves.

2. Conventional approaches have limits

Established data analysis techniques struggle to meet the complexity of modern intelligence challenges. Siloed systems, rigid data structures, and linear workflows obscure relationships and hinder the discovery of meaningful patterns, making analysis tedious and error-prone.

3. Speed and precision are critical

In intelligence analysis, speed, completeness, and accuracy often determine success. Explicit linking of related data preserves context, offering a real-time, holistic view for informed decision-making while reducing errors from fragmented and incomplete information.

4. Network visualisation is intuitive

A network visualisation is the most intuitive and natural representation of real-world interconnectedness. This approach aligns with how the human brain recognises patterns and understands complex systems.

5. Insights must be explainable

Machines should enhance, not replace, human judgment in intelligence analysis. Every insight must be explainable and verifiable. Analysts must stay in control, with technology clarifying, not obscuring, how it reaches conclusions.

Our vision for the future

What intelligence analysis looks like when it’s built for today’s reality

This vision isn’t speculative or aspirational. It describes what intelligence analysis must become to remain credible in the 21st century. The following shifts describe what must change for intelligence work to keep pace with reality, not fight against it.

1. Adoption of graph thinking

Organisations will recognise the fundamental importance of graph thinking in intelligence analysis. They will move beyond traditional, static data models and adopt a mindset that prioritises interconnected data. This shift will foster deeper contextual understanding and reveal hidden relationships, leading to better intelligence outcomes.

2. Creation of a graph foundation

Organisations will build their data foundations on graph data models to ensure that the interconnected nature of information drives every layer of intelligence analysis. Regardless of the user interface (dashboards, tables, charts, maps, or graphs), the underlying system will treat data as a network to take full advantage of graph technology’s benefits: speed, flexibility, deep relationship exploration, intuitiveness, explainability, and fine-grained security.

3. Investment in training

Organisations will fully leverage graph-aware intelligence by investing in training programs focused on graph data modelling, graph querying, graph algorithms, graph visualisation, graph data science, and graph-powered machine learning. These skills will become essential for analysts, data engineers, data scientists, and decision-makers alike.

4. Adoption of graph-powered tools

Organisations will incrementally adopt graph-powered tools into existing intelligence workflows. Initial use cases will deliver immediate value, demonstrating the power of graph-powered intelligence analysis. Over time, organisations will expand their capabilities by integrating additional data sources, automating processes, and leveraging graph data science, ML, and AI. Graphs will help create an institutional memory that ensures continuity, prevents knowledge loss, and allows analysts to build on prior insights.

5. Human oversight

Organisations will implement safeguards to ensure that human analysts remain in control of intelligence analysis results, thereby minimising bias and maximising adherence to ethical standards, legal frameworks, and principles of accountability. Automated insights, machine predictions, and algorithmic outputs will be transparent, interpretable, and subject to human validation. Intelligence findings will be explainable to stakeholders and, where necessary, the public, upholding trust in intelligence practices.

6. Collaboration and standardisation

Organisations that have already embraced graph-powered intelligence analysis will work together to establish best practices, frameworks, and standards for secure and seamless intelligence sharing. This collaboration will drive innovation, enable interoperability, and enhance global intelligence efforts while maintaining high standards of security and privacy.

The future of intelligence analysis lies in understanding the world as it actually is: complex, interconnected, and constantly evolving.

Graphs aren’t just another way to visualise data. They represent a fundamentally different way to model that reality, giving teams clearer ways to see, question, and explain what’s happening.

By embracing graph-powered systems, organisations can achieve faster, more accurate, and deeper insight into the threats, risks, and networks they’re responsible for understanding.

We believe in the power of graphs. Join us in reshaping the future of intelligence analysis