The Influence of Big Data in the Intelligence Cycle

Big Data entails innovative technological progress to the intelligence cycle as it strengthens the collection stage, introduces the correlational analysis method, and facilitates the dissemination of data to the final consumers. However, Big Data also presents some challenges and risks as human consciousness and expert participation remains essential to ensure the intelligence cycle’s effectiveness.

by Alejandra Bringas Colmenarejo

The inclusion of Big Data (BD) in the intelligence cycle has entailed a great advance since it introduced objective and quantitative methods in a discipline highly characterised by its subjectivity. In this sense, BD attempts to reduce intelligence uncertainty through the collection of a huge volume of data and the identification of hidden correlations unobservable in smaller samples. However, while BD is a beneficial technological advance of the intelligence cycle, it also leads to deep controversy given that policymakers may be tempted to replace the expert knowledge and the intelligence analysis with raw BD assets and correlations [1].

BD “represents the Information assets characterized by such a High Volume, Velocity and Variety to require specific Technology and Analytical Methods for its transformation into value” [2]. Consequently, BD is defined by the extremely large quantity of information collected in real-time and in continuous flows. Such information includes structured and unstructured data, traditional processed numeric and text databases, as well as unprocessed formats like images, audios, videos, tweets, emails and more [3]. Furthermore, BD also entails the necessary technologies to collect, manipulate, compare and analyse the collected bulk data and transform it into a reasoned intelligence assessment [4].

The inclusion of BD in the intelligence cycle has several challenges since it surpassed information, knowledge, casualty and context to centre the focus of attention on correlations [5]. Once its veracity and validity have been determined, the data collected from different sources is analysed to predict, determine or even prevent future scenarios, actions and behaviours [6]. Consequently, BD intelligence analysis is “the process of examining and interrogating Big Data assets to derive insights of value for decision making in a quasi-immediate response” [7]. However, this intelligence progress entails some risks and challenges since the increasing dependence on gathering technologies, as well as the enormous quantity of data collected, could result in a sense of overconfidence in technologies and a refusal of human capabilities.

Regarding intelligence collection, BD improves the inductive approach that attempts to recognize long-term trends, patterns and anomalies [8]. Different algorithms and informatics tools enable the automatization of collection, storage, management and transmission of data. This automatization decreases the dependence from manual processes and facilitates the continuous flows of data, [9] which strengthens the analysts’ capabilities to discover intelligence gaps or unusual behaviours. However, to avoid a paralysation of the intelligence process it is essential that the algorithms used are effective in selecting valid and useful data from the vast raw data collected [10].

BD also allows intelligence analysts to generate and refute hypotheses. BD analysis appears to be quite inductive since it refers to past events and historical patterns to causally respond to the question of ‘what is happening’. However, the value of BD lies in the correlation and the identification of hidden events and circumstances so that realities which may not be evident or observable become available to the intelligence analyst. Consequently, filtering valid information from the massive quantity of data allows analysts to support their speculations with facts or to deny a previously confirmed hypothesis [11]. The quick and real-time collection, as well as the long-term storage of data, provides analysts with the necessary evidence to develop informed and predictive intelligence hypotheses. In spite of that, the BD correlation process could also result in the identification of patterns and realities that extrapolated from their specific context are completely useless or coincidental. Consequently, intelligence agents should carefully use BD correlations as without the appropriate expertise analysis they could lead to irrelevant events or unconnected behaviours [12].

Despite the massive volume of data gathered by the intelligence actors, some information remains unknown and excluded from the correlation process because of its secrecy or its restricted access. In this context, non-state data collectors, such as social media platforms, marketing agencies or companies collect and store information that can be bought by the intelligence actors to fulfil the information gap. Nevertheless, the veracity and accuracy of this information remains dependent on the initial collectors [13]. As a result, data provided by private actors could involuntarily impact the effectiveness of the intelligence process or maliciously corrupt, manipulate and counterfeit the reality to deliberately influence the final intelligence assessment [14].

In this manner, BD remains dependent on human capabilities because it still lacks creativity, consciousness and judgement to contextualize new correlations within a broader analytical framework [15]. The limitations of BD should be understood completely in order to avoid misinterpretations and misunderstandings of reality. BD needs expert analysts who are able to identify mere coincidences and consider the unpredictable behaviour of human beings.

Concerning the relation between intelligence analysts and consumers, BD could play different roles. It could help disseminate relevant intelligent assessments to their effective consumers facilitating well-informed analysis and decision-making. Despite this progress in the dissemination stage, intelligence consumers may be sceptical about the veracity and validity of BD’s correlations. Consequently, they could ask for in-depth pattern’ explanations or even become reluctant to authorise action or enact policies supported by BD’s analysis [16]. Otherwise, consumers may be tempted to use raw data without the necessary subsequent analysis to support their own interest and purposes, contrary to the effectiveness of the intelligence cycle [17].

The challenges introduced by Big Data in the intelligence cycle are part of the existential debate between humans and technology and a logical consequence of the very speed of technological advances. Nevertheless, an even greater intelligence revolution could result from the next technological progress – the autonomy of artificial intelligence (AI). AI would collect BD in real-time, develop the consequent intelligence analysis and finally disseminate a reasoned assessment. Future BD analysis and AI would be able to reduce uncertainty and solve intelligence puzzles. However, the challenges and risks associated with this kind of technology are also undeniable since the human element in the intelligence cycle is reduced to the mere intelligence consumer. In the present time, BD does not possess human consciousness, however, full autonomy could be a reality in the near future [18].

Sources:

[1] Van Puyvelde, Damien, Stephen Coulthart, and M. Shahriar Hossain. “Beyond the buzzword: big data and national security decision-making.” International Affairs, 2017: 1397-1416.

[2] De Mauro, Andrea, Michele Grimaldi, and Marco Greco. (2014) “What is Big Data? A Consensual Definition and a Review of Key Research Topics.” 4th International Conference on Integrated Information. AIP Proceedings, pp. 1-11.

[3] Normandeau, K. (2013, September 12). Beyond Volume, Variety and Velocity is the Issue of Big Data Veracity. Available at https://insidebigdata.com/2013/09/12/beyond-volume-variety-velocity-issue-big-data-veracity/

[4] Boyd D. & Crawford. K. Critical Questions for Big Data. (Information, Communication and Society, 2012), p. 662-678

[5] Landon-Murray, M. (2016). Big Data and Intelligence: Applications, Human Capital, and Education. Journal of Strategic Security, 9(2), p.92-121.

[6] Lyon, D. (2014, July-December). Surveillance, Snowden, and Big Data: Capacities, consequences, critique. Big Data & Society, p.1-13. doi: 10.1177/2053951714541861

[7] Couch, N., & Robins, B. (2013). Big Data for Defence and Security. Royal United Services Institute for Defence and Security Studies, p.6.

[8] Lim, K. (2015). Big Data and Strategic Studies. Intelligence and National Security, p.619-635.

[9] Symon, P. B., & Tarapore, A. (2015). Defense Intelligence Analysis in the Age of Big Data. Joint Force Quarterly 79, p. 4-12

[10] Couch & Robins, p.9

[11] Lim, p. 636

[12] Landon-Murray, p.94

[13] Zwitter, A. (2015) Big Data and International Relations. Ethics & International Affairs, 29, no 4, pp. 377-389.

[14] Symon & Tarapore, p. 9.

[15] Dyndal, G. L., Berntsen, T. A., & Redse-Johansen, S. (2017, 28 July). Autonomous military drones: no longer science fiction. Available at NATO Review Magazine: https://www.nato.int/docu/review/2017/also-in-2017/autonomous-military-drones-no-longer-science-fiction/en/index.htm

[16] Landon-Murray, p.101.

[17] Jani, K. (2016). The Promise and Prejudice of Big Data in Intelligence Community. Sam Nunn School of International Affairs, p.14.

[18] Dyndal; Berntsen & Redse-Johansen.

The Chinese Swarming Programme – Part Three of Three

The People Liberation Army has recognised the potential of swarm technology to disrupt the current order; the low cost of swarming technology means that it could be used for saturation assaults on a high-value target by simply overwhelming the current defensive systems.

By Caitlin Irvine

When discussing the People’s Liberation Army’s (PLA) developments in swarm technology it is first important to lay out their strategy. The PLA has recognised the potential of swarm technology to disrupt the current order; the low cost of swarming technology means that it could be used for saturation assaults on a high-value target by simply overwhelming the current defensive systems [1]. The Chinese military, therefore, intends to use this technological advancement as a force multiplier. According to the PLA, unmanned weapons systems are central to future operations in all domains of warfare [2]. Political commentators have speculated that swarming technology could be deployed by China in contentious areas such as the South China Sea [3]. The unmanned nature leaves the party coming into contact with the swarm having to decide whether or not a flyover is an act of aggression, simply reconnaissance, or human error. The secrecy surrounding Chinese military operations resulted in only two clear examples of swarming being discovered; the demonstration at Guangzhou Air Show in 2017 and a simulated reconnaissance mission.

The simulated reconnaissance mission tested an entire group of drones – incorporated with swarming technology – carrying out a variety of missions [4]. Unspecified portions of the flight were performed autonomously whilst still acting as a swarm. Feng and Clover highlight that Beijing therefore thinks ‘swarms of drones will become a weapon of the future’ [5]. It is clear that this technology – and autonomous weapons systems more generally – are an area of debate with severe implications for future warfare.

The PLA aims to harness ‘military-civil fusion to enable future military applications’ by integrating military and civilian developments [6]. A success in the civilian arena rapidly transfers over to the military dimension [7]. In 2017, at the Guangzhou Air Show, a swarm of 1,108 quadcopters displayed the results of Chinese civil-military cooperation [8]. Not only did these drones illustrate synchronised flight but they also showed ‘independent thought’ [9]. During the performance at least three drones fell out of the swarm for an unpublished technical reason. However, when they failed to complete their delegated tasks each drone executed their  individual landings. Drone swarms have previously been compared to an American football team – the swarm runs set plays and the operator oversees the network [10]. But in this demonstration the drones have also shown independent self-repair capabilities; the communication connection from the drones to the hive-like mind was re-established during flight [11]. This self-repairing function therefore demonstrates the potential for these systems to have decision-making capabilities outside of the operator’s direct control. This is a developing technology, still in the early stages, but the PLA is committed to investing in drone swarms for the long-term future.

Drone swarms represent a disruption in the strategic status quo of warfare. In this three-part mini-series, three main points about the consequences of swarming technology have been made. The low entry cost relative to conventional munitions could make these systems commonplace. As a weapon, drone swarms place the onus of differentiation on those being attacked. The advantages for unconventional theatres, such as urban terrain, make these systems attractive to militaries around the world. In both the American and Chinese examples, investment in swarming technology has been seen from both civil and military entities. Within the narrative surrounding drone swarms, it appears that the main use of such systems will be reconnaissance. But, it is their ability to also host attack capabilities is what makes them particularly terrifying. It appears that drone swarms have less political opposition in comparison to Lethal Autonomous Weapons Systems, or ‘Killer Robots’ as they are more popularly known, yet mark a clear point in the path towards such autonomous technologies [12]. 

Sources:

[1] Kania, E (2017) ‘Swarms at war: Chinese advances in Swarm Intelligence’, The Jamestown Foundation: China Brief, Vol 17, Issue 9, p 13

[2] Ibid.

[3] Wise, D (2017) ‘Chinese Drone Swarms Could Overwhelm US at Sea’, The Cipher Brief [online] available at: https://www.thecipherbrief.com/chinese-drone-swarms-overwhelm-u-s-sea accessed on 16th April 2017

[4] Trevthick, J (2018) ‘China Is Hard At Work Developing Swarms Of Small Drones With Big Military Applications’, The Warzone [online] available at: http://www.thedrive.com/the-war-zone/17698/chinas-is-hard-at-work-developing-swarms-of-small-drones-on-multiple-levels accessed on 19th April 2018

[5] Feng, E and Clover, C (2017) ‘Drone swarms vs conventional arms: China’s military debate’, The Financial Times [online] available at: https://www.ft.com/content/302fc14a-66ef-11e7-8526-7b38dcaef614 accessed on 16th April 2017

[6] Kania, E (2017) ‘Swarms at war: Chinese advances in Swarm Intelligence’, The Jamestown Foundation: China Brief, Vol 17, Issue 9, p15

[7] Laskai, L (2018) ‘Civil-Military Fusion and the PLA’s Pursuit of Dominance in Emerging Technologies’ [online], The Jamestown Foundation: The China Brief, Vol 18, Issue 6, availible at: https://jamestown.org/program/civil-military-fusion-and-the-plas-pursuit-of-dominance-in-emerging-technologies/

[8] Romaniuk, SN and Burgers, T (2018) ‘China’s Swarms of Smart Drones Have Enormous Military Potential’, The Diplomat [online] available at: https://thediplomat.com/2018/02/chinas-swarms-of-smart-drones-have-enormous-military-potential/ Accessed on 16th April 2018

[9] Ibid.

[10] Department of Defence (2017) ‘Department of Defence Announces Successful Micro-Drone Demonstration’, Department of Defence, Press release number NR-008-17, 9th January [online] available at: https://www.defense.gov/News/News-Releases/News-Release-View/Article/1044811/department-of-defense-announces-successful-micro-drone-demonstration/ accessed on 19th April 2018

[11] Romaniuk, SN and Burgers, T (2018) ‘China’s Swarms of Smart Drones Have Enormous Military Potential’, The Diplomat [online] available at: https://thediplomat.com/2018/02/chinas-swarms-of-smart-drones-have-enormous-military-potential/ Accessed on 16th April 2018

[12] Docherty, B (2012) ‘Losing Humanity: The Case against Killer Robots’, Human Rights Watch Report

The American Swarming Programme – Part Two of Three

In an attempt to stay ahead of the curve, the US has been heavily investing in the research and development of drone swarms. Three developments in the US swarming programme are particularly interesting: the Perdix drone swarm, the Low-Cost UAV Swarming Technology (LOCUST) and the Control Architecture for Robotic Agent Command and Sensing, or CARACaS systems.

By Caitlin Irvine

In an attempt to stay ahead of the curve, the US has been heavily investing in the research and development of drone swarms. A swarm consists of multiple unmanned aerial vehicles (UAVs) with a certain amount of autonomy to navigate and sense the surrounding area [1]. In comparison to Predators or Reapers they ‘are smarter and more autonomous, designed to take off and land on their own, fly mission sets on their own, refuel in the air on their own, and penetrate enemy air defences on their own’ [2].

Three developments in the US swarming programme are particularly interesting. Both the Perdix drone swarm and the Low-Cost UAV Swarming Technology (LOCUST) programmes demonstrate the future trend towards more autonomous warfare. Finally, the Control Architecture for Robotic Agent Command and Sensing, or CARACaS system, demonstrates that swarm technology can be applied to multiple theatres of operation.

In October 2016, the US military ‘released a 103-strong swarm of Perdix drones’ in California [3]. The Perdix drone is a micro-UAV as its wingspan is less than 30 centimetres – making it ideal for operating in urban environments. The swarm demonstrated advanced behaviours ‘such as collective decision-making, adaptive formation flying, and self-healing’ [4]. The UAVs were launched from three F/A-18 Super Hornet fighter jets showing the ability of the US Air Force to use the developments in swarm technology in combination with their advanced air superiority. The Department of Defence’s press release stated that ‘Perdix is a collective organism, sharing one distributed brain for decision-making and adapting to each other like swarms in nature’ [5]. The DoD’s optimism concerning swarm technology, might indicate  that it will play a role in future conflicts.

Whereas the Perdix drones indicate a move towards autonomously functioning hardware, the LOCUST programme refers to the software used. LOCUST is currently being used in Coyote UAVs that are tube-launched from a platform – not dissimilar from the anti-ship missile launchers currently on board US naval vessels. Seen as a cheaper way of gaining attack capabilities the LOCUST programme could potentially substitute for a single, expensive, anti-ship missile [6]. LOCUST systems fire a minimum of 30 Coyote UAVs in 40 seconds and they are then synchronised mid-flight to create the swarm [7]. At around $500,000 for a 30-drone swarm and just $15,000 for a single unit, the cost of LOCUST is less than half the price of the currently deployed million-dollar Harpoon anti-ship missile [8]. The LOCUST is specifically intended to take advantage of the low-cost UAVs such as the Coyote – the drones are expendable so that if one is destroyed ‘the others autonomously change their behaviour to complete the mission’ –  into an offensive dimension [9].

Finally, the third development in the US swarming programme can be found in the CARACaS programme. CARACaS developed both software and hardware that can be fitted in any vessel in the US Navy illustrating that the move towards autonomous systems is happening across multiple theatres. CARACaS is currently used in small, unmanned boats – but can be used in any vessel – and operates using swarm technology that allows the boats to communicate with one another [10]. The idea behind this project is that expensive but important routine tasks such as harbour patrols could be delegated to an unmanned supervised system. The Navy’s CARACaS system is removing the ‘dull, dirty, and dangerous tasks from sailors lives’ [11]. But the phrase ‘dull, dirty, and dangerous’ covers almost every duty and responsibility given to a standing military.

The majority of swarming software is being designed by civilian firms, for both offensive and defensive uses. Defensive systems have been relatively untouched by the current debate on lethal autonomous weapons systems. This is simply because it is difficult to campaign against a system with defensive purposes. By creating a system that has offensive capabilities – but is primarily used defensively – the issue of whether or not such a system is acceptable becomes blurred. Within the narrative surrounding drone swarms, it appears that the main use of such systems will be reconnaissance. However, their ability to also host attack capabilities is what makes them particularly terrifying.  

Sources:

[1] Bürkle, A, Segor, F, and Kollman, M (2011) ‘Towards Autonomous Micro UAV

Swarms’, Journal of Intelligent And Robotic Systems, Vol 61(1-4), p342

[2] Singer, PW (2013) ‘The Global Swarm’, Foreign Policy [online] available at:

http://foreignpolicy.com/2013/03/11/the-global-swarm/

accessed on 18th April 2018

[3] Feng, E and Clover, C (2017) ‘Drone swarms vs conventional arms: China’s

military debate’, The Financial Times [online] available at:

https://www.ft.com/content/302fc14a-66ef-11e7-8526-7b38dcaef614

accessed on 16th April 2017

[4] Adhikari, R (2017) ‘Pentagon Battle-Tests Micro Drone Swarm’,

TechNewsWorld [online] available at:

https://www.technewsworld.com/story/84217.html

accessed on 18th April 2018

[5] Department of Defence (2017) ‘Department of Defence Announces Successful

Micro-Drone Demonstration’, Department of Defence, Press release number

NR-008-17, 9th January [online] available at:

https://www.defense.gov/News/News-Releases/News-Release-View/Article/1044811/department-of-defense-announces-successful-micro-drone-demonstration/

accessed on 19th April 2018

[6] Richardson, J (2017) ‘Swarming UAVs demonstrate enormous attack potential’,

Defence Procurement International [online] available at:

https://www.defenceprocurementinternational.com/features/air/drone-swarms

accessed on 19th April 2018

[7] Hambling, D (2016) ‘Drone Swarms will change the face of modern warfare’,

Wired [online] available at:

http://www.wired.co.uk/article/drone-swarms-change-warfare

accessed 10th April 2018

[8] Lachow, I (2017) ‘The upside and downside of swarming drones’,

Bulletin of the Atomic Scientists, Vol 73:2, p97

[9] Hambling, D (2016) ‘Drone Swarms will change the face of modern warfare’,

Wired [online] available at:

http://www.wired.co.uk/article/drone-swarms-change-warfare

accessed 10th April 2018

[10] WarLeaks (2017) ‘US Navy Drone Swarm Boats: Autonomous Boats Short

Documentary’, WarLeaks - Daily Military Defence Videos and Combat Footage [online] available at:

https://www.youtube.com/watch?v=NN3A7z9diT4

accessed on 16th April 2018

[11] Ibid.

BIG WORLD, BIG DATA

The number of potential applications for the use of big data is immense. Initially intended as a private sector tool, big data is now finding its place within the realm of politics. Cambridge Analytica’s involvement in the Trump and Brexit campaigns has demonstrated the onset of a new era where big data may be used not only for population analysis, but also to influence the political views and preferences of the population as well.

By Yuliia Kondrushenko

The evolution of technology and the use of big data has forcefully shifted the balance of power relations within society. It is no longer the person who watches the algorithm, but rather the algorithm watching the person [2]. The main features of big data – volume, velocity, and variety – create a very appealing tool as it allows for the discernment of patterns and relationships that are not readily evident from the input data itself.

Big data is increasing “situational awareness” by recording trends that are taking place. This is often used by major supermarket chains such as Wal-Mart, which handles more than a million customer transactions every hour [4]. For example, customer buying behaviour records can demonstrate if the person is conservative, or if they are prone to shifting preferences based on prices, branding, and other factors. Nevertheless, one must be aware that big data can only show event correlation and cannot concretely explain causation.

Due to the corporate-centric nature of big data collection, this sector is where it will be deployed. Big data is an essential tool for detecting bank fraud; should a transaction deviate from the customer’s normal buying patterns, the bank is able to block the activity immediately [5]. But contrary to commercial application, deployment of big data analysis “for the public good” has not been widespread. One place big data could have been useful was the 2007 mortgage crisis in the United States, which began the world financial crisis of 2008. Had big data analysis been performed in relation to debt securities, the bubble may have been halted at its inception.

This is where the limitations of big data analysis become obvious though. The first issue is the amount of data available for algorithmic consumption. The predictive power of big data can only be strengthened by a “significant number of known instances of a particular behaviour” [6]. This means that while bank fraud is a common and well-researched problem with a distinguished pattern, unprecedented crises like the mortgage bubble are not easily predictable.

Another limitation comes from the creation of the algorithm itself. Consumption of an “example data” set creates the operation with the task of finding correlations in the data [7]. Data, which is separate from the example set, is then used to test the effectiveness of the resulting algorithm. This can sometimes create an algorithm that is efficient at forecasting based on the sample used to create it, but is still inadequate for classification of new test data.

While there is a significant risk of result politicization – where the data expert will find scenarios they were initially hoping to find – the fast expansion of available data sets and their dynamic nature makes big data analysis a very powerful tool for business and research.

Sources:

[1]Cárdenas, A., Manadhata, P. and Rajan, S. (2013). Big Data Analytics for Security Intelligence.

[ebook] Cloud Security Alliance, pp.1-22. Available at: https://cloudsecurityalliance.org/download/big-data-analytics-for-security-intelligence/

[2]Jani, K. (2016). The Promise and Prejudice of Big Data in Intelligence Community.

[ebook] Ithaca: The Computing Research Repository Journal, pp.1-19.

https://arxiv.org/abs/1610.08629

[3]Seifert, J. (2007). Data Mining and Homeland Security: An Overview.

Washington D.C.: Congressional Research Service, pp.1-29.

[4]Troester, M. (2012). Big Data Meets Big Data Analytics. [ebook] SAS Institute Inc., pp.1-11.

https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/big-data-meets-big-data-analytics-105777.pdf

[5]Cárdenas, A., Manadhata, P. and Rajan, S. (2013). Big Data Analytics for Security Intelligence.

[ebook] Cloud Security Alliance, pp.1-22.

https://cloudsecurityalliance.org/download/big-data-analytics-for-security-intelligence/

[6]Seifert, J. (2007). Data Mining and Homeland Security: An Overview.

Washington D.C.: Congressional Research Service, pp.1-29.

[7]Jani, K. (2016). The Promise and Prejudice of Big Data in Intelligence Community.

[ebook] Ithaca: The Computing Research Repository Journal, pp.1-19. Available at: https://arxiv.org/abs/1610.08629

3D PRINTING AND NUCLEAR PROLIFERATION

The combination of innovation and digitalisation poses a threat to the Non-Proliferation Treaty (NPT) as the current institutional framework is targeted at objects, not information. The spread of technology does not fall under the jurisdiction of the NPT and is, due to its digital nature, hard to regulate.

By Caitlin Irvine

The political implications of the recent technological tsunami have yet to be fully explored. Additive Manufacturing (AM), the broader term for 3D printing is one such area as it is displaying the potential to alter the global nuclear balance. Although AM technology has been in use since the 1980s, investment in 3D printing has increased in the 21st century as the initial intellectual property rights expired [1].

After a non-profit organisation called Defence Distributed produced the Computer Aided Design files for a 3D printed handgun, the plans were downloaded over 100,000 times around the world before the cease and desist order came into effect [2]. Although currently it is not possible to use this technology to manufacture nuclear weapons due to the export controls on the maraging steel required for use in centrifuges, 3D printing represents a potential proliferation pathway [3]. The combination of innovation and digitalisation poses a threat to the Non-Proliferation Treaty (NPT) as the current institutional framework is targeted at objects, not information. The spread of technology does not fall under the jurisdiction of the NPT and is, due to its digital nature, hard to regulate.

The need for a regulatory framework, however, is urgent. In 2015, General Electric used a AM process called Direct Metal Laser Melting to produce a jet engine capable of 33,000 rotations per minute, similar to the requirements of a uranium-enriching centrifuge [4]. As 3D printing technology, expands in the aerospace industry it will develop a reputation for quality manufacturing; an example of this emerging trend is Raytheon, the U.S. defence contractor, who is attempting to use 3D printing technology to create components of a guided missile system that can be used for a nuclear warhead [5].

Policy must keep comfortable pace with technological advances. Even though AM is still an evolving technology, policy is seriously lagging behind. With no export controls or centralised manufacturing base for the AM industry, the technology is decentralised and open source – to such a degree that my flatmate built two 3D printers in his bedroom for his undergraduate dissertation. Presently, it is possible to almost completely build handguns, grenade launchers, drones, and even guided missiles [6]. Developments in AM technology are therefore likely to impact the system of global governance and non-proliferation because of the variety of products that can be produced. Especially since there is no way of knowing in what hands this knowledge will end up.

Sources:

[1] Kruth, JP, Leu, MC, and Nakagawa, T (1998) ‘Progress in Additive Manufacturing and Rapid Prototyping’,

CIRP Annals, Vol 47: 2, pp 52. https://doi.org/10.1016/S0007-8506(07)63240-5

[2] Morelle, R (2013) ‘US government orders removal of Defcad 3D-gun designs’ [online] BBC News

http://www.bbc.com/news/technology-22478310

[3] Christopher, G 2015, '3D Printing: A Challenge to Nuclear Export Controls'

Strategic Trade Review, vol 1, no. 1, 2, pp. 22.http://www.str.ulg.ac/3D_Printing_A_Challenge

[4] GEreports (2015) ‘The 3D Printed Jet Engine’, YouTube

https://www.youtube.com/watch?v=W6A4-AKICQU

[5] Raytheon (2017) ‘To Print a Missile: Raytheon research points to 3-D printing for tomorrow's technology’

[online] https://www.raytheon.com/news/feature/print-missile

[6] Fey, M (2017) ‘The Increasing Salience of 3D Printing for Nuclear Non-Proliferation’ [online],

Peace Research Institute Frankfurt Blog, https://blog.prif.org/