Enabling the MOD's defence data management strategy with intelligent data access

Since the release of the Defence Data Management Strategy in 2020,1 significant strides have been made toward the MOD's vision of treating data as a “strategic asset, second only to people.” Yet, as the 2025 target date approaches, work remains to achieve the strategy's four key outcomes:
Curating integrated, machine-ready data
Treating data as a strategic asset
Developing skilled personnel to leverage information advantages
Positioning Defence as data leaders alongside partners and allies
While the objectives, rules, and purposes are clear, siloed systems persist. And as AI increasingly accesses sensitive defence information, questions of accountability and visibility become crucial. Also important is avoiding traditional compliance approaches that can significantly increase storage costs while slowing access to critical information.
CISOs in defence are faced with the question: How do we put a security wrap around AI to understand how it is being used and what data it is accessing?
MOD rules for data management
The Defence Data Management Strategy includes six data rules 2 that establish criteria for managing information. Each rule includes critical visibility questions:
What data is being accessed?
Who or what is accessing it?
Why is it being used?
They work together to enable auditability and accountability throughout the data lifecycle. Below are the six data rules Defence is required to implement:
Exercise sovereignty over data, establishing clear accountability and ownership.
Standardise data across the defence landscape, enabling seamless integration.
Exploit data at the most effective point in the value chain, maximising operational benefit.
Secure digital data, protecting information throughout its lifecycle.
Curate data, ensuring it is assured, discoverable, and interoperable.
Treat data as an enduring asset — preserving long-term value beyond individual projects.
By creating a comprehensive foundation for holistic visibility and auditability, Elastic helps defence teams implement all six rules effectively. Our end-to-end security approach, encryption, role-based access controls, and comprehensive audit logging, ensure that data remains protected throughout its lifecycle, while allowing complete transparency through open standards. This approach enables visibility while providing protection of the data and also illuminates its use, giving you the ability to monitor, track, and verify access across complex operations.
6 defence data management rules decoded: How to meet them with ease
1. Data sovereignty and control
Maintaining data sovereignty is a key priority for Defence. The organisations must tackle a complex legacy environment, unlock data hidden in siloed systems, avoid being locked out by supplier contracts, and implement clear governance frameworks that assign accountability for data quality and security. Without these, Defence risks losing control over its most critical asset — its data.
One way to address these challenges is by adopting flexible, secure data platforms that unify siloed data sources, support cross-domain collaboration, and maintain control over where and how data is stored and accessed. Data sovereignty demands tool consolidation, vendor agnostic approach, and systems that scale securely across legacy and modern environments.
2. Breaking down data silos
Right now, too much of Defence’s data is stuck in silos and exists in different formats, making it hard to trust or reuse. Without a shared approach to standards, Defence risks wasting time and resources on duplicated or incomplete data.
To achieve compliance with this second rule, Defence needs to eliminate data silos and apply consistent organisation structures across all data. This ensures information can be accessed, analysed, and acted on effectively and holistically, regardless of its format or where it is stored. Data mesh principles, with capabilities like cross-cluster search, make integration and cross-domain insights both possible and practical. It enables defence-wide visibility, enforces schemas, and supports governance aligned with Defence and cross-government requirements.
3. Accelerating data exploitation and insight
Exploitable data is key to Defence’s ability to act fast, share intelligence, and adapt to evolving threats. Despite the volume and value of data available, Defence often struggles to make it usable, spending too much time finding and cleaning data and not enough on using it. Teams are often stuck manually correlating disparate data from different systems, leading to delays when critical decisions need to be made.
To support large-scale data exploitation and mission-ready intelligence, Defence must bring data together into a single, searchable platform. This approach enables fast access, automation, and insight across systems. By supporting a wide range of data ingestion methods, such as lightweight agents, OpenTelemetry, and edge collection, data can be analysed and exploited at the most effective point in its lifecycle, providing commanders with decision advantage through timely, accurate, and context-rich information.
4. Built-in security for sensitive data
Defence operates under constant cyber threats and needs secure-by-design data. Fast, controlled access to classified information, swift breach detection, and strict compliance with legal and ethical rules are critical but difficult to achieve at scale.
To safeguard sensitive data and maintain operational integrity, Defence needs to implement strong, built-in role-based security controls, such as role-based access controls (RBAC) and attribute-based access control (ABAC), across multiple layers. These controls should be supported by unified identity management and detailed activity logging. And when combined with advanced monitoring capabilities, including machine learning to detect insider threats and unusual behaviour, these measures enable real-time policy enforcement and continuous oversight across systems.
5. Data curation for clearer decision-making
Defence systems produce vast amounts of data, but without curation, critical insights get lost in noise and duplication, delaying operational decisions.
To eliminate blind spots and enable timely insights, Defence must unify its data and apply AI-driven search to surface the right information from different sources through a single, holistic query. Machine learning can further support data curation by revealing hidden patterns, while advanced techniques like retrieval augmented generation (RAG) enhance context-aware analysis for faster, mission-critical decisions.
6. Optimising data retention and access
Data is often locked to its original purpose, losing relevance over time or its immediate use. Defence lacks consistent ways to maintain, govern, and extract long-term value.
Managing data as a persistent asset requires implementing lifecycle policies that automatically migrate data to economical, long-term storage, maintaining queryability with minimal performance impact. Elasticsearch logsdb index mode exemplifies this approach by efficiently storing critical logs and reducing storage costs by up to 65%, enabling defence to optimize data retention expenses.
Elastic meets the strategic outcomes of the MOD data strategy
In the Defence Data Strategy, the rules and outcomes play complementary roles. The Defence Data Rules set the principles and standards required to build a unified data environment, while the strategic outcomes define the goals Defence aims to achieve through their application. Together, the rules provide the foundation, and the outcomes set the direction — driving the operational transformation of Defence’s data landscape.
Elastic helps Defence overcome data silos and complexity, transforming strategic goals into mission success. Here’s how Elastic enables each Defence data outcome in practice:
Data is curated, integrated, and human/machine-ready: Data can be searched and understood in its original format without needing to be converted first. This means different types of information, like intelligence feeds, sensor readings, and operation records, can be analysed and accessed holistically, no matter where they come from.
Data is treated as a strategic asset: RBAC and ABAC enforce data sovereignty by granting authorised personnel granular access to accurate, up-to-date data for real-time decisions. RBAC simplifies privilege management by tying permissions to roles, removing access automatically when roles change — especially when integrated with systems like Active Directory.
Skilled personnel exploiting data for advantage: User-friendly dashboards let analysts identify trends and anomalies without specialised expertise. Generative AI integration summarises complex unstructured data, while prebuilt templates and workflows accelerate training. This addresses skills gaps and maximises the effectiveness of existing personnel, which can significantly increase security teams' efficiency.
Defence as data leaders with partners and allies: An open, flexible ecosystem with multi-vendor support allows Defence to unify data and drive innovation collaboratively with partners and industry, ensuring agile and secure operations.
The result is an operational advantage built on secure, accessible, and comprehensive information. Beyond this, the ROI for such a data foundation is remarkable, a critical advantage for defence teams managing complex compliance requirements and tight budgets.
Join our webinar series, Mission advantage: Strategic conversations with defence leaders to explore how defence organisations are embedding governance, auditability, and assurance into every phase of AI deployment — while building a unified data layer to drive mission success and accelerate Defence Data Strategy outcomes.
Explore additional resources:
Sources:
Computer Weekly, “Government announces data strategy for defence,” 2021.
Ministry of Defence, “Data Strategy for Defence - GOV.UK,” 2021.
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