Op-ed
    by James Thomas, Global Head of Cyber and Priit Liivak, Chief Architect

    Using AI to take a manoeuvrist approach to defence legacy

    Southsea castle in United Kingdom

    Service

    Data and AI Cyber Resilience

    Industry

    Defence

    In defence, advantage comes from applying force where it matters most. AI is now making it possible to understand complex legacy systems well enough to focus effort where it delivers the greatest effect.

     

    UK defence’s digital supply chain is a deeply interconnected system of systems, spanning C4I platforms, communications infrastructure, digital services, and operational and administrative technologies across the MoD and its supplier ecosystem. These systems are often owned and operated by different stakeholders with varying priorities, adding further complexity to how they interact and evolve. Many are decades old, embedded within long procurement cycles, and tightly coupled through layers of integrations that are only partially understood. While they continue to function, their critical role means they cannot simply be replaced without significant risk.

    Legacy as a structural feature, not a failure

    This creates a structural challenge: obsolescence is not an anomaly, but an inherent feature of defence at scale. Efforts to modernise have historically struggled not due to lack of investment or intent, but because the problem itself is difficult to define. When systems are so tightly coupled that changing one affects many others with cascading effect, the act of understanding the complete digital supply chain becomes as complex as transforming it. This complexity is compounded by multiple system owners, making alignment and coordinated change difficult to achieve.  
     
    Similar challenges are seen in multinational environments such as NATO, where decades-long efforts to improve interoperability and standardisation highlight how difficult it is to evolve highly interconnected systems across organisational boundaries. The result is a persistent cycle of legacy, limited visibility, and risk aversion, where the safest decision is often to leave systems unchanged. 

    The cost of working around legacy

    Limited visibility into system dependencies and data flows makes it difficult to identify vulnerabilities, enforce consistent security standards, or respond quickly to incidents. Workarounds inevitably emerge – created by users seeking faster access to functionality or new capabilities, and by developers and maintainers adapting systems to meet requirements they were never originally designed for.

    While often effective in the short term, these workarounds tend to be poorly documented, difficult to support, and frequently operate outside formal control frameworks. Historically seen as shadow IT, and now increasingly as shadow AI, they can demonstrate what is possible but rarely scale into core environments due to security requirements, integration challenges, and the need to meet strict approval and governance standards. Instead, they introduce new, ungoverned dependencies into an already opaque ecosystem, adding to complexity and creating the conditions for the next generation of legacy.

    Legacy systems are not only a constraint on modernisation, they are also high-risk targets. Limited visibility, hidden dependencies, and fragmented integrations create vulnerabilities that are difficult to manage and increasingly easy to exploit with advancing AI capabilities (like Anthropic's Mythos model). Modernisation improves security by making these systems more transparent, controlled, and resilient.

    Overlaying this is a fundamental tension in defence investment: the perceived trade-off between investing in physical capability and digital capability. Digital investment often delivers efficiency and decision advantage, but lacks the immediate visibility of physical assets. As a result, modernisation of the digital supply chain can be deprioritised, even where it would significantly enhance operational effectiveness. 

    System understanding through AI

    This is where artificial intelligence presents a different kind of opportunity. A new class of agentic AI systems can now interpret and reason over entire software estates. This enables a level of system understanding that was previously out of reach. Rather than relying on incomplete documentation or institutional memory, AI can reconstruct an understanding of individual systems directly from their artefacts such as code, data structures, configurations, and runtime behaviour – a practice we describe as AI-driven legacy archaeology. This makes it possible to surface embedded business rules, workflows, and system interactions that are often no longer fully visible, including patterns and dependencies that would be extremely difficult to identify through manual analysis alone. This shifts modernisation from assumption to evidence, providing a more reliable basis for prioritisation and reducing the risk associated with change. 
     
    However, achieving this depends on a structured approach to how systems are analysed. Legacy environments need to be broken down into manageable components, with clear decisions about how to do that, what and how to examine, and what needs to be achieved. This requires both a well-defined process and the expertise to apply it in complex systems. 
      
    In practice, this is as much about process design as it is about technology – how the analysis is structured, how different AI capabilities are applied, and how results are interpreted. Without that structure, AI-driven legacy archaeology can quickly become inefficient, expensive or produce useless results. 

    Focus effort where it delivers the greatest impact

    In operational contexts, AI has already been used to bring together fragmented data and improve situational awareness and decision-making. In Ukraine, AI-enabled platforms have been used to integrate data from multiple sources, such as sensors, intelligence feeds, and operational systems, providing commanders with a more coherent, real-time picture of the battlespace and enabling faster, more informed decisions. Earlier examples can be seen in the way intelligence agencies began linking previously siloed databases, enabling analysts to query and analyse data across systems rather than searching each one in isolation. These examples demonstrate how improved understanding of complex environments can directly translate into more effective action. 

    Applied to legacy modernisation, this enables a more manoeuvrist approach. Instead of attempting large-scale, high-risk transformation programmes, organisations can use AI-driven insight to focus effort where it delivers the greatest impact, grounded in a clearer understanding of system interactions and their consequences. 

    This improved understanding helps manage the risks associated with integrating new systems, technologies, and approaches. It also creates greater tempo, enabling more rapid adoption of advantage technologies such as AI, autonomy, and advanced sensing capabilities.  
     
    In practice, this means focusing effort on targeted interventions. This might include improving interoperability between communications and command systems, reducing reliance on manual workarounds across legacy administrative platforms, or mitigating risk in tightly interconnected systems. Here, AI is not replacing systems, but enabling more precise and confident change within them. 
     
    In this way, AI acts as a force multiplier, reducing the trade-off between digital and physical investment by enabling more effective use of resources. The result is improved decision-making on operations, greater resilience of critical systems, and more efficient use of resources.

    Breaking the cycle of legacy

    Building on this foundation, AI-driven legacy modernisation approaches offer a path to not only make legacy systems visible, but to prioritise and sequence their evolution. By combining system understanding with risk and impact analysis, defence organisations can move towards incremental, controlled modernisation, reducing fragility, improving supply chain security, and avoiding the creation of new forms of legacy, including unmanaged AI adoption. 
      
    The opportunity, therefore, is not simply to modernise legacy systems, but to break the cycle in which complexity limits change, and limited change increases complexity. In a system-of-systems environment, progress can be cumulative, with improvements in one area sometimes creating the conditions for change in others. With the right application of AI, defence organisations can move from reacting to legacy constraints to actively shaping a more secure, adaptable, and resilient system. 

    Complex systems need more than tools. They need direction. Reach out!

    James Thomas Defence

    James Thomas

    Global Head of Cyber
    Priit Liivak Defence

    Priit Liivak

    Chief Architect

    Only then does procurement begin to make strategic sense.

    Because in modern defence, the question is no longer who owns the most sensors.

    It is who can connect them.

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