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Reclaiming analyst time: Smarter investigations with AI in defence

How the MOD can reduce investigation fatigue and boost operational efficiency

By

Crossley McEwen

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Security analysts at the UK Ministry of Defence (MOD) — and everywhere — face an overwhelming challenge: They can receive thousands of alerts daily, and distinguishing genuine threats from false positives in a timely fashion has become nearly impossible without technological intervention. The human cost is significant — over 70% of SOC analysts (across sectors)1 report burnout, even while the MOD saw a 400% increase2 in data breaches over the past five years. Organisations often respond by adding more tools, personnel, and (unnecessary) costs rather than addressing fundamental inefficiencies.

Meeting the MOD's AI productivity goals

The UK Defence Artificial Intelligence Strategy (2022)3 recognises this challenge, acknowledging that “in an environment where data is ubiquitous it is increasingly important that intelligence analysts can automate the collation and analysis of large datasets.” The MOD also recognises the potential of AI to improve productivity and reduce workload for security analysts,4 with plans to create a productivity portfolio to identify and track potential uses of emerging technologies, like AI that:

  • Automates and accelerates routine business operations and policy work

  • Enhances the speed of decision-making

  • Optimises logistics

  • Increases the availability of military capabilities

Elastic’s agility and scalability enables it to serve the MOD’s strategy, not as just another tool adding to the noise and complexities, but as a force multiplier that changes how analysts work. By integrating AI directly into the workflow, tools like AI Assistant and Attack Discovery process multiple data streams simultaneously while distilling hundreds of alerts into actionable intelligence. It can elevate tier-one analysts’ capabilities and turn hours of tedious investigation and repetitive tasks into minutes of focused, high-value analysis and action.

Seeing the complete picture, immediately

The MOD seeks to automate routine operations while enhancing decision-making speed across its security operations. In practice, this means finding ways to quickly separate genuine threats from the background noise that consumes valuable analyst time.

This can be achieved through AI-powered systems that connect seemingly unrelated alerts into comprehensive attack narratives. By analysing hundreds of alerts simultaneously and evaluating them against asset criticality, risk scores, and behavioural patterns, security teams can identify the most critical attacks that require immediate attention. When an adversary attempts to establish persistence across multiple systems, tools like Attack Discovery can recognise the pattern and present it as one coherent attack story.

Natural-language interfaces complement this capability by allowing analysts to investigate further without technical barriers. Analysts can quickly ask follow-up questions, like “Show me all similar activity across our network in the past week,” and receive contextual insights that would normally require complex query construction. AI Assistant represents one approach to enabling this more intuitive investigation process.

Security teams can identify, understand, and act upon threats in minutes rather than hours, reclaiming up to 74% of full-time employee (FTE) hours previously spent on routine tasks — directly supporting the Defence AI Strategy's focus on speed and efficacy as determinants in future conflicts.

Simplifying security operations

This unified data model brings together endpoint, network, and cloud telemetry in one searchable data view. Analysts can quickly pivot from alerts to detailed investigation without switching contexts. By eliminating the need for separate tools and their associated licensing costs, total security tooling costs can be reduced by approximately 25% while actually improving capabilities and reducing complexity. Investigation guides and prebuilt playbooks standardise response procedures while ML-powered detection rules identify threats that might otherwise be missed. 

For remediation, security teams can execute actions across distributed endpoints simultaneously — isolating compromised machines, killing malicious processes, or deploying patches without leaving the platform. This end-to-end workflow automation transforms what was once a multi-hour, multi-tool process into a streamlined operation.

Building a Zero Trust foundation

The MOD faces a 2026 deadline for Zero Trust Architecture implementation —‚ not a simple task. A practical approach to this challenge centres on data integration rather than adding more security tools. Collecting authentication logs, network traffic, and application telemetry in one place creates visibility across traditionally separate domains. This matters for Zero Trust. When a user accesses sensitive data, the system needs to verify not just their identity, but their device health, network path, and even the time and location of access. Without unified data, these checks become cumbersome or impossible, and it’s possible for threats to go undetected or stuck in siloed systems.

A data foundation makes Zero Trust implementation practical rather than theoretical, ensuring the MOD meets its 2026 target.

Explore how defence leaders are enabling secure, real-time collaboration across domains with AI and unified data visibility. Watch our webinar series.

Explore additional resources:

Sources:

  1. Tines, “Voice of the SOC Analyst,” 2021.

  2. Intersec, “MOD Fights Back,” 2024.

  3. Ministry of Defence, “Defence Artificial⁣ Intelligence Strategy,” 2022.

  4. Civil Service World, “MoD to investigate potential for AI to improve productivity,” 2024.

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