Future Scenarios for Defence Systems Approach to Training (DSAT) in Individual and Collective Training

This is a “personal opinion” response to RUSI’s paper on the need for prioritizing training in the Strategic Defence Review. I have used AI to do some background research.

Paul O’Neill’s paper "The Strategic Defence Review Must Put Training at its Heart" is excellent and if you haven’t read it, then you should do so before? reading this.

My take on this is one of being involved in DSAT and military training since 1974 (when I joined the Army) right up to this day, when I am looking at the influence of technology on the military of today and indeed through the lens of Generation Z and even further for Generation A who will be the military of tomorrow.

So, given the current trends and potential disruptors in defence training, I see three (and there are probably hundreds I could have used) distinct future scenarios that can be explored for both individual and collective training within the Defence Systems Approach to Training (DSAT) framework.

These 3 scenarios integrate advanced technologies, global collaboration, and emerging threats, reflecting the insights provided by RUSI’s paper on the need for prioritizing training in the Strategic Defence Review. Each scenario is broken down into 4 parts, these include:

Key Drivers

These are the main factors or trends that influence or cause change in a situation. They are the forces that push developments in a particular direction.

Implications

These are the consequences or effects that result from the key drivers. They describe what might happen as a result of changes in the drivers.

Strategic Responses

These are the actions or plans that need to be taken to address the changes or challenges brought about by the key drivers and their implications.

Early Warning Indicators

These are the signals or signs that suggest a change is about to happen. They help to identify when it is time to take action or adjust strategies.

Scenario 1: AI and Data Driven Adaptive Training Environments

Key Drivers:

·???? AI and Machine Learning: Increasing use of AI to analyse performance data and adapt training in real-time.

·???? Data Driven Insights: Big data will allow training environments to personalize instruction and predict learning needs, enhancing individual and collective skills.

·???? Simulation Technology Advancements: Significant improvements in VR, AR, and synthetic training systems allow for highly immersive and scalable training environments.

?Implications:

·???? Individual Training: AI-driven platforms will personalize individual training based on real-time performance feedback, reducing the time needed to master skills.

·???? Collective Training: AI enhanced simulations will enable entire units to train in real-time against evolving scenarios, providing dynamic feedback and adjustments.

·???? Reduced Live Training: A shift towards virtual simulations may reduce reliance on expensive and logistically complex live exercises, though live training will remain necessary for certain contexts.

Strategic Responses:

·???? Investment in AI-Driven Platforms: Develop adaptive learning environments that integrate AI, enabling training scenarios to adjust dynamically to learner performance.

·???? Integration of Real-time Data: Utilize data analytics to improve individual and collective performance assessments.

·???? Blended Training Programs: Incorporate both synthetic and live training to ensure skills developed in virtual environments translate effectively to real-world scenarios.

Early Warning Indicators:

·???? ?Increased military investment in AI and machine learning for training.

·???? ?Rising demand for data scientists and AI specialists within defence training programs.

·???? ?Growing reliance on virtual training for individual and collective exercises.

Scenario 2: Distributed, Remote, and Modular Training

Key Drivers:

·???? Remote Technologies and Connectivity: Advances in digital platforms and secure communication networks enable distributed training across multiple locations.

·???? Cost and Resource Pressures: Reduced availability of resources, such as personnel and equipment, encourages a move towards more flexible, scalable training models.

·???? Global Threat Environment: Complex global threats require multinational collaboration, driving demand for interoperable, distributed training solutions.

Implications:

·???? Individual Training: Remote learning modules will allow individuals to complete training asynchronously, reducing logistical challenges.

·???? Collective Training: Distributed simulations will enable units across different geographic locations to train together in real-time, reducing the need for physical colocation.

·???? Security Concerns: Ensuring the security and resilience of communication networks will be critical, especially as reliance on distributed systems increases.

Strategic Responses:

·???? Development of Modular Training Systems: Create flexible training systems that can be accessed remotely and scaled according to specific operational needs.

·???? Hybrid Exercises: Combine physical and virtual exercises to maintain cohesion while leveraging the flexibility of distributed training.

·???? Secure Communication Infrastructure: Strengthen the security of networks to prevent disruption or espionage in distributed training exercises.

Early Warning Indicators:

·???? Rising adoption of remote collaboration tools for military training.

·???? Shift towards more decentralized military operations with greater reliance on remote training.

·???? Increased vulnerability of communication networks to cyber threats during training exercises.

Scenario 3: Hybrid Warfare and Multi-Domain Integration Training

Key Drivers:

·???? Hybrid Warfare Tactics: Growing prevalence of hybrid warfare tactics, blending conventional, cyber, and information warfare.

·???? Multi-Domain Operations: Increased need for coordination across land, sea, air, cyber, and space, reflecting the complexity of modern conflicts.

·???? ?Cybersecurity and Information Warfare: Growing importance of defending against cyber threats and disinformation campaigns, which require specialized training.

Implications:

·???? Individual Training: Focus on developing cyber and information warfare skills alongside traditional military competencies.

·???? Collective Training: Multidomain exercises will require units to collaborate across different domains in real-time, with advanced simulation environments integrating cyber, space, and physical warfare.

·???? Complex Operational Scenarios: Training must simulate hybrid warfare environments, preparing forces for asymmetric threats, including disinformation and unconventional tactics.

Strategic Responses:

·???? Multidomain Integration Platforms: Invest in platforms that allow for real-time, integrated training across land, sea, air, cyber, and space domains.

·???? Cybersecurity Training: Prioritize cybersecurity and information warfare training, ensuring personnel are equipped to handle modern hybrid threats.

·???? ?Cross Domain Scenario Development: Develop complex, hybrid training scenarios that combine conventional combat with cyber, information, and space operations.

Early Warning Indicators:

·???? ?Increased incidents of hybrid warfare tactics being employed in real-world conflicts.

·???? ?Emergence of new multidomain simulation platforms that integrate cyber and physical elements.

·???? ?Growing emphasis on cybersecurity and information warfare in military training programs.

Conclusion

As outlined in Paul’s paper, the evolving nature of conflict, technological advancements, and multi-domain operational requirements necessitate a comprehensive rethinking of the Defence Systems Approach to Training (DSAT).

The integration of AI, data-driven analytics, immersive technologies, and cyber capabilities into both individual and collective training represents not just an enhancement of existing frameworks but a necessary evolution for modern defence preparedness.

These advancements must be carefully integrated into DSAT, ensuring flexibility, scalability, and adaptability to the dynamic global threat landscape.

Integration of Advanced Technologies

The rise of AI and immersive technologies such as Virtual and Augmented Reality (VR/AR) creates a paradigm shift in how military personnel are trained.

Real-time data collection and machine learning algorithms can be used to personalize individual training while adapting collective training scenarios dynamically.

This allows for increased efficiency, shorter learning curves, and real-time responsiveness to trainee performance.

The use of simulation environments like the Gladiator system, which blends live and virtual elements, is a cornerstone in optimizing resources while ensuring preparedness.

However, successful deployment requires robust integration protocols across the services, ensuring consistency and interoperability among NATO allies and other strategic partners.

Adapting to Evolving Threats and Hybrid Warfare

The modern battlefield is increasingly influenced by hybrid warfare, where conventional military engagement is complemented by cyberattacks, disinformation campaigns, and unconventional tactics.

Future training scenarios must simulate these hybrid environments to effectively prepare personnel for asymmetric threats, such as cyber-physical attacks, swarming drones, and information warfare.

Training for cyber and information warfare should be embedded in both individual and collective exercises, focusing on multi-domain integration.

Cybersecurity and resilience training must become a core component of DSAT, particularly in light of the increasing digitalization of warfare.

Fostering Interoperability and Multi-Domain Operations

Training programs must be designed for interoperability, ensuring that forces from different branches and allied nations can operate cohesively in joint missions.

The advent of multi-domain operations which span land, sea, air, cyber, and space demands that DSAT supports cross-functional collaboration at all levels of training.

The need for interconnected platforms that can simulate multi-domain warfare in real-time, while incorporating AI-driven decision-making tools, is critical to maintaining operational readiness.

By investing in distributed training systems that allow for geographically dispersed units to train collectively through advanced networked simulations, the Ministry of Defence can prepare for large-scale joint operations without the logistical constraints of physical co-location.

Optimizing Resource Allocation through Synthetic Training

Given the cost pressures and capacity constraints highlighted in recent RUSI commentary, synthetic training environments offer a solution to scalability and resource optimization.

By blending live and virtual elements, as seen in modern synthetic training systems, defence forces can maximize their training capacity without the extensive use of expensive physical assets.

However, this transition to synthetic environments must be carefully balanced with the need for live exercises to ensure that personnel remain capable of responding to real-world frictions, such as battlefield unpredictability and physical fatigue.

Strategic Defence Posture

The Ministry of Defence must ensure that its strategic defence posture integrates training as a core operational capability. This includes:

·???? Adopting a true Agile approach to all aspects of DSAT. DSAT and its forerunners was originally designed for Defence Procurement (pre-internet), and whilst JSP 822 has evolved as a process it is still being used as a one-size-fits-all approach for ALL military training and the phrase “DSAT Compliant” has long been an uninformed commercial crutch.

·???? Strategic investments in AI, simulation technologies, and cybersecurity training systems that enhance both individual and collective preparedness.

·???? A focus on multi-domain integration, ensuring interoperability across allied forces and enabling seamless coordination in joint missions.

·???? Flexible, scalable, and modular training programs that allow for rapid adaptation to emerging threats, particularly in hybrid warfare environments.

·???? Resilience in distributed training infrastructure, ensuring secure, uninterrupted access to networked training platforms, even in contested environments.

Early Warning Indicators

·???? Increased defence budgets directed towards synthetic training and AI integration.

·???? Growing incidents of hybrid warfare that emphasize the need for comprehensive multi-domain training.

·???? NATO-driven initiatives to standardize training frameworks and ensure interoperability across allied forces.

·???? Rising demand for cybersecurity expertise within defence training programs, driven by the increasing digitalization of warfare.

The Ministry of Defence must transition towards a DSAT model that is not only truly agile, but also technology-driven, and multi-domain capable, ensuring that training remains at the heart of defence capability in an era of rapid technological advancement and evolving threats.

By doing so, the UK will not only enhance its operational readiness but also strengthen its role in global defence collaborations.