How defence is changing and why databases are critical to the shift into AI

The defence sector is experiencing a historic shift. Military advantage is no longer defined solely by physical platforms - tanks, jets, ships - but by how quickly forces can sense, decide, and act in dynamic environments. Artificial intelligence (AI) now sits at the centre of this transformation, enabling autonomous systems, predictive logistics, cyber resilience, and real-time intelligence analysis. Yet, one truth is clear: without the right data foundations, AI in defence cannot deliver on its promise.

What’s broken about defence data today?

Current defence data practices are fragmented, siloed, and slow. ISR (intelligence, surveillance, reconnaissance) feeds live in one system, logistics data in another, cyber threat telemetry in yet another. Analysts and operators stitch information together through brittle pipelines and manual workflows.

This fragmentation causes several problems:

• latency: by the time data is ingested, normalised, and delivered, it is already stale
• interoperability: proprietary or legacy systems resist integration across domains or coalition partners
• siloed AI development: training pipelines are often batch-oriented, meaning models learn from yesterday’s battle, not today’s

The result is that commanders cannot trust AI outputs for real-time decision-making, and autonomous systems risk acting on partial or outdated information.

Why legacy infrastructure falls short

Traditional relational databases and batch ETL pipelines were designed for back-office accounting, not live combat environments. Their shortcomings include:

• rigid schemas: fixed relational tables cannot handle the messy mix of structured, semi-structured, and unstructured defence data
• centralised architectures: a single data warehouse may work for finance, but it collapses under the needs of distributed, multi-domain operations
• no context for AI: legacy stores lack vector search, graph reasoning, and temporal memory - features that modern AI workloads demand

Simply put, yesterday’s infrastructure cannot keep pace with the tempo of cyber defence or autonomous drone swarms.

The defence shifts driving a new data paradigm

Three major trends make a new kind of database essential:

• agentic AI and autonomy - drones, UGVs, and cyber agents need persistent, context-aware memory to act independently
• multi-domain operations - land, sea, air, cyber, and space domains must be fused into a single operational picture
• ISR overload - satellites, UAVs, and sensors generate terabytes of raw data per hour, demanding real-time filtering and graph-based correlation

Without a unified, adaptive database, these trends overwhelm command structures and create operational blind spots.

Agentic AI demands a new defence data layer

Autonomous and agentic AI do not simply “query” data - they live in it. For them to operate safely and effectively, the data layer must provide:

• persistence: durable memory of prior actions, decisions, and observations
• contextual reasoning: graph traversal to link events, actors, and places
• real-time responsiveness: millisecond-fresh updates through live queries
• secure compartmentalisation: row- and field-level permissions for coalition or mixed-trust environments

This is not optional. Without these capabilities, autonomous systems risk becoming brittle, unsafe, or even exploitable.

How SurrealDB gives defence an edge

SurrealDB is an open-source, Rust-based, ACID-compliant, multi-model database engineered for AI-native workloads. Its architecture collapses the polyglot stacks defence currently struggles with - vector stores, graph engines, relational tables, and key-value caches - into one engine and one query language (SurrealQL).

Key advantages in combat and intelligence contexts:

• real-time decision-making - live queries push data directly to operators or agents, ensuring ISR feeds, cyber alerts, or logistics updates are always current
• multi-model reasoning - a single SurrealQL query can combine similarity search (vector), knowledge-graph reasoning (graph edges), and transactional updates (SQL) in one round-trip
• tactical edge deployment - a 3 MB build runs on any device with an operating system or a standard library without saturating its resources, making it possible to run close to the action
• agent memory - versioned records allow AI agents to “time-travel”, replaying prior states for audit, reproducibility, or situational awareness

In a cyber defence scenario, SurrealDB can power agents that detect anomalies, correlate them across networks, and autonomously isolate compromised nodes - without waiting for human-in-the-loop approvals.

Rust as a strategic advantage for defence security

SurrealDB is written in Rust, a systems language that prioritises memory safety, performance, and resilience. For defence, this matters deeply:

• exploit resistance: Rust eliminates common vulnerabilities such as buffer overflows and use-after-free, which adversaries routinely weaponise
• performance at the edge: lightweight binaries can run securely on constrained hardware without garbage-collection pauses
• resilience under attack: strong type-safety and concurrency guarantees make denial-of-service and race-condition exploits harder to achieve

In short, Rust makes SurrealDB a secure foundation for sensitive workloads where compromise is not an option.

Conclusion: the future of defence AI rests on the database layer

Defence is shifting from platform-centric to data-centric warfare. AI will only be as effective as the database beneath it. The fragmented, brittle systems of the past cannot support autonomous agents, ISR fusion, or multi-domain command.

SurrealDB offers a unifying data layer - real-time, multi-model, secure, and edge-ready - that equips defence organisations to act faster, more accurately, and with confidence in the age of AI. The message is clear: winning tomorrow’s conflicts will not only depend on superior weapons, but on superior data. And that begins with the database.

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