Man versus Machine at Combat Tactical Level Decision Making

The human ability to collect information, make sense of a situation, optimise action, and learn while executing has been challenged recently in games, simulators, diagnoses, and real-time analysis. How may this development reflect to future tactical combat level decision-making? Is the machine going to win the man in combat?

"This requires not only substantial investment in resources but also an open-minded and exploratory approach, in contrast to the common but sometimes exaggerated perception of military organisations as conservative entities." Meir Finkel (Finkel 2023)

"Fifth-generation warfare shifts the focus from kinetic force in physical dimension to the impact information dimension, where narratives and perceptions take centre stage, enabled by emerging technologies such as artificial intelligence, automation, and robotics." Daniel Abbott (Abbott 2010)

The article reviews some recent achievements in artificial intelligence, sets the situation for the combat technical level functions, digs deeper into decision-making under stressful conditions and illustrates a possible vision for the future state. The aim is to shake the historically conservative concepts of land battle to consider future possibilities.

Artificial Intelligence Improvements in Decision Making

A view to the evolution of machine learning improvements in various strategic-tactical games and competitions in Table 1 shows that machines are catching up and dominating men in table, card and video games and creativity competitions. Furthermore, fast-learning general-purpose algorithms are beating dedicated algorithms in those same games. 

Table 1: A sample of improvements in Machine learning applications in gaming and creativity

Year	Confrontation	Improvement
1997	Chess: DeepMind against Garry Kasparov	It took IBM 11 years to build and use customised chips to execute parallel searches. DeepMind was able to evaluate 200 million positions per second.
2016	Go: AlphaGo against Lee Sedol	A neural network-based algorithm first learned from game data, then played against itself, and finally, improved based on made mistakes. AlphaGo was able to create an unseen move during the game.
2017	Chess: AlphaZero against Stockfish (2016 top chess engine)	General purpose reinforcement learning algorithm that learned Chess after playing 4 hrs against itself. AlphaZero was able to assess 80 000 positions per second.
	Shogi: AlphaZero against Elmo (2017 world champion Shogi engine)	The algorithm learned the game after playing 2 hrs by itself. AlphaZero was able to assess 40 000 positions per second on a board that has more options than Chess.
	Go: AlphaZero against AlphaGo Lee (advanced Go engine)	Deep neural network with tabula rasa reinforcement learning algorithm. The algorithm learned the game within three days while playing itself.
	Poker: Liberatus against four champion poker players	The algorithm used a game theoretic approach for reasoning in an imperfect information environment while playing simultaneously against four human players with the following abilities: ·        Managing the whole poker competition in advance ·        Solving each game during the contest ·        Self-improvement after each day of the three-week competition
2019	Dota 2: Open AI Five against a Team of 5 esport players	The algorithm used proximal policy optimisation. The algorithm used 800 petaflops/s to gain about 45 000 years of experience within ten months. The short-term average decision time was 80ms.
2020	AlphaFold2 doubled the score of human competitors in Critical Assessment of Structure Prediction.[1]	The algorithm predicted 3D structures based on complicated rules faster and more holistic than a human.
2022	AI model that uses tens of terabytes of Earth system data and can predict the next two weeks of weather tens of thousands of times faster and more accurately than contemporary forecasting methods.[2]	With enormous amounts of data, ML algorithms can create forecasts of very complex phenomena.

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[1] https://www.technologyreview.com/2022/02/23/1045016/ai-deepmind-demis-hassabis-alphafold/

[2] https://www.technologyreview.com/2023/07/05/1075865/eric-schmidt-ai-will-transform-science

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In conclusion and, in theory, a machine combined with the above features could:

1. Starts from zero knowledge and trains within months to master given battle scenario's technical, tactical, and possibly operational level features for victory.
2. Anticipates adversary moves ahead, creates picture of potential scenarios, and predicts adversary manoeuvring in 3-D space better than humans.
3. Makes short-term decisions within 80 milliseconds and optimises decisions simultaneously at technical and tactical levels.
4. Identifies lessons from the events and gains 150 years of theoretical combat experience teaching itself overnight.

 

Technical Level of Ground Combat is a Complex Military Decision-Making Environment

Probability and chance are well-recognised (Clausewitz 1984) (Fuller 2012) (Oliviero 2021) factors of battle environment. Tactics-technical level combat capability is a sum of surprise, manoeuvre, mass, firepower, and tempo (to name some essential tenets) orchestrated in variety of combinations with Command and Control to disrupt the adversary's socio-technical military system and exhaust its fighting ability. (Friedman 2017) The tactical tenets are in transformation to address the foreseeable changes on the battlefield. First, let's review the most likely changes in land warfare and, second, see what they will require from tactical tenets.

RUSI Land Warfare Conference (RUSI 2023) promoted the following tendencies of change in land warfare, which will challenge the contemporary tactics:

1. Transparent battlefield

• Civilian and military LEO satellite-based sensors provide a continuous feed of information from above the battlefield. The data can be acquired from commercial sources and fused with algorithms trained to identify especially military action on the ground.
• Unattended ground sensors improve details and add reliability to real-time event pictures.
• Cover and concealment become harder since sensors can fuse detection data from different parts of spectrum.
• Adversary will know the location and movement of blue forces as quickly as the information flows in the blue battle management system.

2. The concentration of effects vs protection

• Standoff weapons, lethal autonomous weapon systems, and precision warheads make it challenging to survive with contemporary armour. Adding armour thickness slows tactical mobility.
• Concentrated armoured units create a lucrative target for conventional artillery, attack helicopters, or massing of anti-tank UASs.
• Platforms and actors need to become more expendable and distributed but able for coordinated manoeuvres and fires.

3. Sustainment

• Logistics enables the tempo of fighting and is essential for offensive operations. Supplying distributed units require new delivery methods.
• Movement and mass of material expose logistics for continuous, wide-spectrum surveillance, so protection and endurance of logistics become a challenge.

4. Situational awareness

• An increasing amount of data and information challenges sense-making as human cognition overburdens from large amounts of information, loses focus in the stimulus-rich environment, and makes a biased conclusion.
• The organisational culture may prevent the distribution of information (need-to-know vs need-to-share; air-gap security vs zero-trust security), so situational awareness does not meet the requirements of distributed tactics. (Mansoor and Murray 2019)

5. Boundless, urban battlespaces

• People reside primarily in urban environments, and military strategies aim to "capture the will of the people and their leaders, and thereby win the trial of strength." (Smith 2005)
• Participating actors in urban battlespace may include, for example, civilians, communal authorities, law and rescue institutes, local corporates, international corporates, non-governmental organisations, insurgents, commercial military companies, interest groups, militias, criminal organisations, adversary regular forces and adversary coalition units. (Waterman 2019)
• The urban environment is more complex as these actors do not carry clear signs for identification, their intentions may transfer from day to night, and they do not follow agreements on war crimes.

In conclusion, the following Table 2 reflects the above tendencies to classical tenets of tactics and illustrates the possible impact in battle techniques and tactics and, therefore, change of tactical sense- and decision making.

Table 2: How do visible tendencies of change in land warfare affect tactical tenets of ground combat?

Tenet / Tendency	Surprise	Manoeuvre	Massing of force	Firepower	Tempo
Transparency	Surprise in land domain may be gained through other domains and dimensions.	Swarming manoeuvre of smaller, less detectable platforms.	Concentration becomes lethal, but dispersion rules.	Target acquisition is more lethal if situational awareness is achieved.	The advantage is harder to gain in a transparent battlefield.
Effect	Systems effect creates surprise and disrupts force cohesion.	A large, moving, hot, and radiating platform is an easy target.	Calls for a mass of nimble, small, and mobile warheads	The 4IR produces software-defined effectors.	Dispersed effectors will increase friction and entropy.
Sustainment	N/A	Higher mobility and wider distribution obscures logistics.	Dispersed troops increase the logistical challenge.	Smart warheads require software maintenance.	Besides live supplies, the force needs technical maintenance.
Situational awareness	Digitalised C2 creates more cognitive bottlenecks.	Becomes a core enabler and vulnerability for the swarming of distributed effectors.	Becomes a core enabler and vulnerability.	Becomes a core enabler and vulnerability.	Becomes a core enabler and vulnerability .
Urbanisation	Provides concealment in the physical dimension.	Slows manoeuvre and promotes smaller, autonomous, and agile platforms.	Constraints massing of units, but prefers small, swarming effectors.	Favours defence but constraints offence.	Slows down units and increases their entropy.

Art of Military Sense- and Decision-making

A Concept for Sense- and Decision-making

The classical military decision-making framework defined by John Boyd is simplified as Observe, Orient, Decision, and Action (OODA) (Osinga 2007). Based on this framework, Figure 2 illustrates a concept for sense- and decision-making. In this context, sense-making consisting of observation and orientation, which interprets the equivocal data. (Mattila 2016) Furthermore, decision-making is searching and selecting alternatives optimising between projected results, capabilities, and constraints. (Mattila 2016) The concept has three different situational pictures: real-time events per domain, composed operational picture, and forecasted possible/intended situations, which are referred to existing information and, finally, shared and agreed upon at the socio-cognitive level.

Figure 2: Concept for Observe, Orient and Decide at the Military tactical level

The above Command and Control (C2) concept may be established with an emphasis on creative leadership or policy compliance. These emphases are founded in the culture from which armed forces are generated. For example, German culture from 1871 – 1945 promoted officers' autonomous and aggressive action on the battlefield. (Mansoor and Murray 2019) Conversely, after the forceful manipulation of Bolshevik government, Russian culture produced obedient younger officers and relied on experienced and resourceful commanders at the operational level. (Freedman 2022)

A Team of Military Officers in Decision-making

A successful military command should be a mixture of compliance with institutional management culture and creative operational art. (Kuronen 2015) German culture before WW II reflected the war as "an art, a free and creative activity founded on scientific principles." (Condell and Zabecki 2008) The US FM 5-0 requires adaptive leaders"…who do not think linearly, but  who instead seek to understand the complexity of problems before seeking to solve them…" (Cojocar 2011) On the other hand, NATO assesses military success with five measures of merit and only one of them, measures of performance (MoP), includes some personal leadership features. (CCRP 2002) The other four enforce doctrinal and process compliance. (NATO RTO 2002) The 1/5 ratio in expectations does not indicate innovative tactical decision-making from NATO officers.

At the tactical commitment level, all efforts should focus on gaining the initiative and, eventually, victory over the adversary (reduction of adversary combat power by more than 30%). (Oliviero 2021, 51) In reality, this is not necessarily evident for all officers: 

• Training enforces drills and tactical forms, so officers prefer to use familiar concepts to solve battlefield challenges in decision-making. 
• Viewpoints may be constrained by their basic training and arms. An infantry officer aims to gain ground, an armoured forces officer aims to gain distance, or an artillery officer assesses ranges, amount of ammunition and supplies to impose a particular effect. 
• The Red Force doctrine, officers are training against, remains linear, predictable, and unimaginative adversary. 
• Since live exercises are expensive, officers train their tactical decision-making in war games, which often neglect friction, fog, chaos, and cognitive stress present on the battlefield.

Studies (Henaker 2022) (Scott and Bruce 1995) (Loo 2000) have concluded that there are five different decision-making styles categorising individuals when making important decisions: Rational, Intuitive, Dependent, Avoidant and Spontaneous.

1. Rational seeks information systematically and prefers logical assessment. However, rational has challenges in creativity and implementation of decided intent.
2. Intuitive recognises details from the information flow and matches patterns that feel right. Intuitive relates positively to creativity and difficulty-solving. 
3. Dependent seeks social conformance from others before decision-making. The decision-making process may be distracted and in need of social support.
4. Avoidant tries to postpone decision-making because of their low self-esteem. Still, avoidant is compliant with policies, doctrines, and orders. Avoidant is not suitable for creativity and tends to have high stress levels.
5. Spontaneous tries to accomplish decision-making as soon as possible. Spontaneous does not like conflict situations but perform well in rash decision and high-risk situations.

Human vs Machine Decision-making in Future Battlefield

The section fuses the tenets of tactical combat with visible transformations and tries to reflect these new situations in human-centric and machine-centric decision-making as featured in previous sections. Table 3 illustrates the outcome of the fusion from the view of two champions:

• Human is assumed as an average decision-making officer with 3-4 years of military education and about five years of professional experience with, possibly, one year of experience gained in live tactical action. 
• Machine is assumed to be a high-performance computer running a combination of continuously learning algorithms, expert algorithms, and pre-trained algorithms with real-life or synthetic data. Digital connectivity is supposed to be at combat cloud level . 

Table 3: Human vs Machine decision-making in transforming tactical combat environment

Transforming tenets of tactical combat feature decision-making challenges	Human	Machine
Transparency increases information and requires more computing power to make sense of collected data. Tactics prefer smaller, profoundly dispersed, manoeuvrable effectors, which swarm for effect, and retreat quickly.	Available data and information may overburden the cognitive ability to comprehend the situation.	A machine can recognise images, find patterns from large data mass, and forecast complicated, interdependent behaviour.
Effect calls systems understanding for system-wide impact. Dispersed effectors are harder to control and coordinate. Software-defined precision requires better target acquisition and configuration.	The adversary must be understood as multi-dimensional actor-network (Inglis and Thorpe 2019). Dispersed effectors require coordination of larger volume of details.	A machine can map the COA spectrum, model complicated, interdependent systems, and optimise the action of small effectors.
Sustainment of distributed, cyber-physical platforms requires more flexible and expert maintenance.	Rising complexity of critical paths on availability or sustainment may overwhelm cognitive capacity under stress.	With a digital-twin model and scenario-based simulation, a machine creates an overall logistics picture and can optimise sustainment.
Awareness is achieved by delegating sense-making to lower cooperative level or improving the information management ability of a steeper, hierarchical command structure.	Socio-cultural structures and beliefs handicap the application of the optimum C2 method.	Socio-cultural structures do not constrain a machine, and it can act even with partial information environment.
Urbanisation increases entropy, slows the tactical pace, increases casualties, raises the need for sustainment, and makes the environment and situation harder to understand.	The urban environment increases entropy and requires more innovative decision-making.	A machine makes sense of complicated situation even with partial information, recognises faster volatile behaviour, and optimises effort and sustainment.

A Company Commander Meets an Ex-Machina Battle Captain

When a Human Commander meets an Ex-Machina Captain within a tactical scenario on a future battlefield, the parties of combat may have the different abilities for decision-making. In situation with equal forces, linear doctrines, and a reasonably stable battlefield, the company commander does not have a chance against Ex-Machina. A creative human commander may gain an advantage in more chaotic conditions and with innovative tactics. Are our military institutes educating agile officers? Still, higher man-machine teaming performance indications are positive in Dota 2 strategic game, but it remains to be studied in future articles.

 

Figure 3: Man vs machine in tactical decision making

The Promise and Peril of Generative Artificial Intelligence (especially ChatGPT4.0) from a Military Viewpoint

 

Figure 1: Will ChatGPT provide world dominance? (Composed using Canva)

There is an Ongoing Global Competition for Disruption and Gaining a Strategic Advantage

There is an ongoing competition to use Artificial Intelligence and related technologies to gain a strategic advantage between the three military superpowers or wannabes.

• "Whoever becomes the leader in this sphere [of Artificial Intelligence] will become the ruler of the world." Putin 2017 
• "Chinese official documents and their enunciation of military doctrine indicate that the country's leaders see massive promise in AI's utility and are working to leverage this emerging technology into their force posture." (Bommakanti, 2020)
• "Emerging technologies are transforming warfare. The technological innovations expected to play increasingly important roles on future battlefields include artificial intelligence, sensors, unmanned air and ground systems, and cyber capabilities." (Weissmann & Nilsson, 2023)

Did we see one of these disruptions happening before our eyes at the beginning of 2023? The OpenAI product Chat GPT conquered the Internet with the speed of one million users in 5 days (Gartner, 2023) and 100 million monthly active users within two months after launch , and Sam Altman, the CEO of OpenAI, testimonies to US Congress that "AI could be as big as "the printing press" but acknowledged its potential dangers." 

Possibly – but not the way you may first think!

Generative AI Meets the Lower Levels of Bureaucratic Creativity

Since 2015, OpenAI has been delivering breakthroughs in AI algorithms, competing successfully in human games and creating human-like content. Their release of ChatGPT 4.0 has impressed the world with abilities for conversation and logical text generation. For some of the users, ChatGPT 4.0 has appeared as an artificial general intelligence (AGI)  with human-like consciousness to some of the users. Fortunately, that is not the case.

Figure 2: An extract from a discussion with ChatGPT

Generative AI can create content from given data. This content can be delivered in multiple modalities, like text (articles or answers to questions), images (photos or paintings), videos, and 3-D representations (scenes and landscapes for video games). Generated content has been winning digital-art awards and scoring among or close to the top 10 per cent of test takers in numerous tests, including the US exam for lawyers and the math, reading, and writing portions of the SATs, a college entrance exam used in the United States. 

The ChatGPT is a combination of three functions:

1. The user interface in the application defines Chatbot. So easy to use it can create an illusion of chatting with a human counterpart. 
2. Fine-tuned and continuously learning discussion engine. Fine-tuning adjusts the weights of the neural network or adds layers to help the model better understand the nuances of the task. 
3. The GPT model has a complex machine learning algorithm of a deep neural network with 96 layers managing around one trillion parameters . The statistic large language model (LLM) has been taught with more than 45 terabytes of human-produced text acquired from the Internet (over 300 km of bookshelf space, beyond any human to read through).  The LLM has identified test patterns from this vast data and chooses the following word using the learned values and probabilities. 

In summary, people use ChatGPT because it is convenient, replies fast and mostly rationally, and is available 24/7, unlike most human counterparts. (Based on ChatGPT answer through Bearly.ai interface 03. June 2023) Besides the convenient and funny private discussions, organisations are seeking several ways to benefit from the NLP and LLM. Financial services giant Morgan Stanley is testing the technology to help its financial advisers better leverage insights from the firm's more than 100,000 research reports. The government of Iceland has partnered with OpenAI in its efforts to preserve the endangered Icelandic language. Salesforce has integrated the technology into its popular customer-relationship-management (CRM) platform. 

Military opportunities with generative AI

Can the ChatGPT provide an advantage in the assessment of the situation? 

"The general who wins the battle makes many calculations in his temple before the battle is fought. The general who loses makes but few calculations beforehand." Sun Tzu 

No, but if military culture enforces rules, then the GPT model may be fine-tuned with digitised military rules, policies, doctrines, tactics, techniques and procedures, and an Officers Companion application can warn if an officer is going to deviate from the authorities when deciding.

Can the ChatGPT master adversary at the strategic or operational level?

"For to win one hundred victories in one hundred battles is not the acme of skill. To subdue the enemy without fighting is the acme of skill." Sun Tzu 

No, statistical algorithms of large language models do not understand war at its different levels. Even though, if one trains a model with a large variety of tactical plans, there may be possible to generate combinations of these plans as graphs. Other AI algorithms are more proficient in strategy, gaming, and tactical confrontation than LLM. 

Where may the military use the ChatGPT like generative artificial intelligence, then?

Suppose we use the Figure 3 military impact model which illustrated the evolution of the cyber environment. In that case, there are several apparent points where the military may benefit from generative AI:

• Man-Machine Interface (MMI) may be improved using Chatbot and LLM for translation of text and speech, free soldiers' hands from the keyboard of the battle management system, provide a dutiful companion for a lonely soldier in a trench to ease the anxiety or trauma, or act as a virtual instructor/trainer in the military metaverse.

• Generating content and establishing virtual relationships for information operations. For example, 

"Russia has operationalised the concept of perpetual adversarial competition in the information environment by encouraging the development of a disinformation and propaganda ecosystem."  

Generative AI provides affordable means to generate disinformation, and misinformation, and manipulate people through social media as the furthest-reaching weapon after intercontinental missiles.   Furthermore, Cyber attackers may adopt new means to create more believable phishing emails, generate cyber-attacks, and craft new malware. 

• Writing computer program code.  Since computer application programming uses very abstract languages, Generative AI may translate applications from one programming language to another, create programs to solve coding problems, simplify code, write documentation, or test code to find failures. 

"For many developers, generative AI will become the most valuable coding partner they will ever know." 

As the military is becoming more software-defined, the generative AI may provide an edge for Armed Forces to establish their code factories. 

• Generative AI can generate synthetic data based on patterns and relationships learned from actual data.  Synthetic data may accelerate the learning of other AI algorithms, for example, to counter swarming drones, see faster the adversary behavioural pattern from clutter, or provide optimisation advice  from a smaller amount of data points.

• Generative AI may enable the military to see a wider variety of options in the tactical situation through "machine hallucinations".  Moreover, as generative models can use statistics from large amounts of data points, they may illustrate historical battleground schemas on the current tactical situation and expand these with other variations  providing a broader foresight for tactical planners.

• Generative AI may be used for 3D object generation  to accelerate military metaverse development, wargaming and simulation. The acceleration may create the next wave of revolution in military education (Chatman, 2009) and training, making the force generation able to execute continuous training more affordable and evolve the training content faster than currently. Furthermore, the ChatGPT may finally move the 2nd industrial teaching methods forward and have instructors focusing on critical-­thinking and problem-solving skills rather than copying textbooks for answers. 

Figure 3: Levels of Military Impact and Evolution of Cyber Environment (Mattila, 2022)

The above, possibly surprising, impacts are not easy to achieve, however!

Military Challenges with Generative AI

The adoption of generative AI proliferates in commercial and open-source segments ; organisations must address several critical challenges to ensure the success of their Generative AI initiatives. These challenges may include:

1. Data Management: Effective data management is critical to the success of generative AI, as models rely on high-quality, well-labelled data. Organisations must ensure that their data is accurate, consistent, correctly labelled, managed securely, and in compliance with relevant regulations.
2. Model Complexity: Generative AI models can be complex and resource-intensive, requiring significant computing power and technical expertise. Creators must ensure they have the resources and skills to develop and deploy these models effectively. In addition, the acquisition may need to verify the function of algorithms before deployment. 
3. Like any analytical model, Generative AI has proven vulnerable to deliberate manipulation by sophisticated adversaries.  For example, data poisoning - the data used to train the models may be manipulated, and adversarial attacks — feeding algorithms malicious inputs, may be used to counter AI-enhanced features.
4. Ethical Considerations: As mentioned earlier, ethical considerations, such as bias and privacy, are becoming increasingly important in the development and deployment of generative AI.  Creators must ensure their models are fair and transparent and respect user privacy in peacetime use. 

Nevertheless, US DoD states that the U.S. public and private sectors cannot afford to pause their artificial intelligence pursuits amid an international race for technological supremacy.  

Fear is relative!

Credit: Colin Anderson Getty Images and The Conversation

A human composed this article using AI enhanced Google and Bing searches to find sources, Bearly to summarize found articles, provide different wording and have discussions along the writing process, Word writing assistant to guess the next word, Canvas to create graphics, and Grammarly to proof-read the text.

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 Zero Trust Security Architecture in Military Cyber Environment

Summary

• Zero Trust Architecture (ZTA) is rooted in the principle of “never trust, always verify.” Zero Trust design aims to protect modern cyber environments and enable digital transformation by using strong authentication methods, leveraging network segmentation, preventing lateral movement, providing Layer 7 threat prevention, and simplifying granular, “least access” policies. 
• ZTA is replacing the previous trust on domain ownership and airgap isolation in access management as part of information security.
• The military has adopted or is adopting the new foundation for security trust as they proceed with digital transformation.

What is a Zero Trust Security Architecture?

Information security architecture is about trust. The military has a long tradition of trusting an entity if it is a part of the owned domain (SIPRNET), physically separated from others (AIR GAP), situated in a know location (Camp), the user represents a trusted organization or uses authorized terminal (Workstation in a Command Post). 

Unfortunately, the digital transformation of military enterprises is not possible based on these old trusts (Snowden , Teixeira , data breaches doubled in 2022 in DoD ) but require access from mobile terminals (no place), Adhoc networks (no domain), quickly changing roles (no organization) and via a variety of terminal (no workstation). Therefore, it is hard to establish the foundation for trust when everything can change. Hence, A zero-trust architecture (ZTA) is an enterprise cybersecurity architecture based on no-trust principles designed to prevent data breaches and limit internal lateral movement. 

The NIST SP800-207  and the CISA ZT Maturity Model v2  are the most used references for the ZTA. They also provide examples of migration roadmaps from perimeter trust towards zero trust. The following principles define the zero-trust approach:

1. Every access request starts from a position of zero trust (applies to all entities - humans, devices, services).
2. Authorization is granted based on dynamic context (risk-based), ideally per request.
3. Assume a breach - of user ID (including machine or application service ID), access device, or transport network. 

Naturally, the above level of untrust requires 24/7 monitoring and a thorough understanding of one’s information and computing assets. Therefore, a consolidated cloud computing architecture usually enables Zero Trust and helps build Digital Trust. 

The NIST SP800-207defines seven tenets for ZTA as follows:

1. All data sources and computing services are considered resources.
2. All communication is secured regardless of network location.
3. Access to individual enterprise resources is granted on a per-session basis.
4. Access to resources is determined by dynamic policy.
5. The enterprise monitors and measures the integrity and security posture of all owned and associated assets. 
6. All resource authentication and authorization are dynamic and strictly enforced before access. 
7. The enterprise collects as much information as possible about the current state of assets, network infrastructure, and communications and uses it to improve its security posture.

How are Military Organizations Proceeding with ZTA?

Typically, military organizations are found somewhere along the evolutionary path of information security. Depending on their position, they can proceed with small steps or take a revolutionary leap to enable the full features of digital transformation.  For example, Table 1 provides a view of what is going on in military information security.

Armed Force	Areas of ZTA Application	Plans for the Future
FIN	2008 secured Internet service within a Confidential domain[1] 2009 Secret session over untrusted networks with trusted terminal 2015 Any confidentiality level session over any access network on any available terminal[2]	N/A
US	2021 Executive order to USG to move to Zero Trust Architecture[3] 2022 US DoD Path to Zero Trust Architecture (ZTA)[4]	FOC 2027 for cloud-based services JADC2 will be based on ZTA[5]
5 Eyes	2023 Aligning the 5 Eye Nations ZTA approaches[6]	N/A
EUMS	2022 Regulations for a high common level of cyber security, digital operational resilience, and resilience of critical entities  [7]	N/A

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[1] https://www.is.fi/digitoday/art-2000001436589.html

[2] https://www.defmin.fi/files/1834/tietojohtaminen.pdf

[3] https://www.strongdm.com/blog/zero-trust-executive-order-14028

[4] https://www.defense.gov/News/News-Stories/Article/Article/3229211/dod-releases-path-to-cyber-security-through-zero-trust-architecture

[5] https://defensescoop.com/2023/04/12/army-at-the-crawl-phase-in-journey-to-zero-trust

[6] https://www.cybersecurityconnect.com.au/defence/8574-five-eyes-alliance-discusses-zero-trust-cybersecurity

[7] https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/

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The 4th industrial revolution will provide disrupting opportunities for the defence industry

 What is 4IR?

The Fourth Industrial Revolution or 4IR—is the next phase in the digitisation of the manufacturing sector, driven by disruptive trends, including the rise of data and connectivity, analytics, human-machine interaction, and improvements in robotics.  Some will extend the industrial digital transformation to include also advances in biotechnology.  As a result of this perfect storm of technologies, the Fourth Industrial Revolution is paving the way for transformative changes in how we live and radically disrupting almost every business sector.  

Academics are already observing transformation both in the defence industry and gradually also in military affairs.  For example, connectivity and data collection enable automation, artificial cognition, autonomy, and enhanced awareness through military operations. So naturally, the U.S.A. aims to sustain their technological advantage by rearranging its defence industry and investing in research and development, such as artificial intelligence.  China and Russia also have high expectations for the 4IR enabling capabilities and dual use of emerging commercial technology.  

What are the areas of opportunity for 4IR enablement in the defence industry?

Defence capabilities are becoming more socio-technical in nature. Previously independent platforms got connected, and functions on each platform create more value as part of a grid or swarm rather than separately. Soldiers are not working with each other but cooperating with machines through advanced human-machine interfaces. The physical, cyber, and biological realms become interchangeable without clear boundaries. The convergence of the three realms enables the emergency of cyber-physical systems , i.e., software-defined everything or bio-cyber-physical systems, i.e., bio-mimicking systems  and cybernetics-driven synthetic biology .

Industrial engineering, design engineering and systems engineering  provide a framework to illustrate the systems life cycle, as illustrated in Figure 1. The life-cycle view can recognise some opportunities for 4IR enhancements or even disruptions within the cycle compared to the current situation. 

Figure 1: Systems life-cycle view 

The first way may disrupt the capability analysis and system requirements establishment of defence systems. In all-domain joint operations, symmetric platform combat is not a valid argument when the military is seeking systems impact and effect. Businesses and offer development may use modelling and simulation to illustrate their differentiation in the market and show how their proposal meets the evolving adversary capabilities.

Secondly, the digital twin concept enables designers to approach the solution with model-driven methods. As a result, possible solutions can be created and proven with real-life data before physical manufacturing or software development. 

Thirdly, the 4th industrial revolution will make it possible to produce prototypes faster than ever since one of the most significant barriers to prototyping is time. With 3D printing, the defence industry can create prototypes, experiment, and improve the design faster and cheaper than with 3rd industrial methods. 

Fourthly, the 4th industrial revolution will also change how information is shared within the defence industry. The new technologies will enable the real-time exchange of data across multiple sectors. The enhanced data exchange will improve collaboration between specialised industries and support their shared efforts in research and development within a defence ecosystem without working through large system integration consortiums. As a result, opportunities for faster innovation may emerge for clusters of smaller but more effectively cooperating companies. They may work around the defence giants if they can overcome the domestic political and public acquisition barriers.

Fifthly, the new technologies will enable the military to maintain their armament more efficiently. The remote operation requires online connectivity but allows a collection of run-time data from a platform, use of data to simulate availability trajectory, optimise availability, and call platforms individually for physical maintenance. Furthermore, the inspection with computer vision will provide faster and better information on a visible tear and wear than any human technician. Combining data from built-in sensors and visual observation will create a foundation for proactive maintenance. Furthermore, the software-defined features will provide a longer life cycle to military platforms as their features and abilities can be defined and changed by configuring software differently.

The 4th industrial revolution may also affect the acquisition of high-level competencies. The new technologies will enable the establishment of teams of people with tailored competencies for the job. Even further, with digital means of sourcing knowledge workers, the defence industry can use short-term freelance force or award open innovation societies to innovate their offer developments.

What is keeping the defence industry from benefitting from 4IR?

Unfortunately, the military, political interests, and public acquisition behaviour are slowing down the disruption in the defence industry. For example, the volatile globalisation and power competency are shaking the supply chain reliability. Government acquisition regulations, green transfer and national politics are changing the terms and conditions of defence contracts. The competition in edge products and achieved monopolies in other products are creating asymmetric but volatile markets. The core of 4IR is access to data. Therefore, the defence security requirements constrain the sharing and use of data to the fullest.  Furthermore, the revolution requires advanced semiconductor circuits, integrated circuits, data engineering and science competencies, intellectual properties, and integration capabilities. The U.S.A., with other advanced manufacturers and designers, are trying to constrain China and Russia from gaining these assets. 

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