The Marriage That Will Never Be
Outlining the potential for public-private intelligence cooperation between the AIVD and citizen collective Bellingcat
Lotte Nietzman s1517848 08 / 07 / 2020 Words: 21.885
Supervisor: dr. G.G. De Valk Second reader: dr. G.M. Van Buuren
Author’s note
Lotte Nietzman, MSc student at Leiden University (s1517848).
Master Thesis as part of the Master’s program Crisis and Security Management at the Faculty of Governance and Global Affairs.
Table of Contents
1. Introduction 4
1.1 Reading guide 8
2. Theoretical framework 10
2.1 Body of knowledge 10
2.2 Terms and definitions 13
2.3 Central concepts 16
2.4 Analysis of intelligence cooperation 19
2.4.1 Interests 19 2.4.2 Drivers 21 3. Methodology 25 3.1 Research design 25 3.2 Research methodology 26 3.3 Operationalization 28
4. Context of the case 33
4.1 Public intelligence agencies in The Netherlands 33
4.2 MH17 35
4.3 Bellingcat 36
5. Analysis of the case 38
5.1 Perspectives 38
5.1.1 Bellingcat 38
5.1.2 Dutch public intelligence services 42
5.1.3 Interim conclusion 43
5.2 Fagertsen’s 2012 model applied 45
5.2.1 Interests 45
5.2.2 Drivers of internal demand 48
5.2.3 Drivers of external pressure 51
5.2.4 Interim conclusion 54 5.3 Conceptual conclusion 56 6. Conclusion 60 6.1 Discussion 61 Bibliography 64 2
Acknowledgements
Within a year my interest in intelligence has grown considerably. I am grateful to have had the opportunity to dedicate my thesis to a topic within the field of intelligence. During the writing of this thesis, I truly enriched myself with interesting trips to and conversations with persons from the intelligence community from which I learned things you do not learn from books. I would like to thank all of these people for their efforts and for thinking along with me. In addition, I would like to offer some words of gratitude to my thesis supervisor dr. Giliam de Valk with whom I had inspiring conversations. Thank you for your enthusiasm and for your helpful suggestions. I would like to thank my second reader, dr. Jelle van Buuren for his comments on the penultimate version of my thesis. This master thesis forms the concluding part of the master Crisis and Security Management at Leiden University. With its completion I hope to have obtained my second MSc degree in a year.
Lotte Nietzman Amsterdam, july 2020
1. Introduction
The nature of war is changing (Kaldor, 2006:210; Keegan, 2004:228; Haynes, Hough, Malik, & Pettiford, 2017:229). Warfare used to be a matter between states or nations whereas now, it is about conflicts between states and non-state actors. In parallel with that, central governments used to be the only actors in the intelligence community. But after 9/11 the world changed, especially in the field of security. The terrorist attacks had shown the world the necessity of intelligence cooperation, domestically and internationally (Lefebvre, 2003:527). Nowadays several types of intelligence are being practiced: national security intelligence, military intelligence, law enforcement intelligence, business intelligence and private intelligence (Prunckun, 2013:5). As regards the latter, we see functions previously performed by public intelligence agencies being subcontracted to private intelligence corporations, which is known as the outsourcing of intelligence (Verkuil, 2007:2). Private intelligence can be defined as “the principles, processes, practices, techniques, and materials used by non-government entities to gather information and analyze these data” (Prunckun, 2013:6). Due to a rise in private intelligence (Palmer, 2013:3) literature has been focussing on outsourcing for some years now. The debate on outsourcing we saw earlier when intelligence tasks were subcontracted to private military companies (PMCs). A PMC is a company that carries out tasks for the army, typically logistics and security (Singer, 2001:186). Governments have been criticized for ceding too much power to such companies (Nowinski, & Kohler, 2006:1). It is not clear how PMCs should be held accountable for possible past mistakes as these companies - contrary to the army - are not accountable to, or are not obliged to answer to, politics (Leander, 2010:476 as in Buzan, Wæver, & de Wilde, 1998:92). The absence of legislation and oversight made the use of these companies controversial. But apart from private companies gradually gaining ground in the intelligence industry, citizens are becoming actors in the intelligence community as well.
There are two types of private intelligence. The first one being the outsourcing of intelligence generated by public organisations who subcontract their tasks to private companies. The second type of private intelligence is of a different order and concerns active citizenship: individuals taking the initiative to take on intelligence tasks based on common, democratic values of the rule of law. Active citizenship in this regard should be understood as
the realization of the duties of being a citizen as well as taking the initiative to bear a shared responsibility. This so-called ‘civic spirit’ is the driver for the second type of private intelligence and will be regarded as active citizenship in the remainder of this paper.
Bellingcat, an investigative journalism collective that is increasingly well-known for its open source investigation, is an interesting example of citizen intelligence. Bellingcat consists of citizens, not trained reporters, and gives high priority to human and civil rights and supporting the democratic order. It attaches great value to the quest for truth and hence, in situations in which the truth is not ensured, takes the initiative to truth finding itself. It does so with a sense of journalistic and social responsibility. The platform gained notoriety for, amongst others, investigating the disaster of MH17 using open source analysis. With their work, Bellingcat upholds democratic values that are transboundary in character. These are values which are not solely state-based but are rather shared by and apply to every single individual anywhere in the world. These democratic values are laid down in international covenants. Article 6 of the European Convention on Human Rights (ECHR) protects the right to a fair trial. In criminal law cases or in cases to determine civil rights, it protects the right to a fair and public hearing, within a reasonable time, by an independent and impartial tribunal. It stipulates the presumption of innocence and a few, minimum rights for everyone charged with a criminal offence. Article 10 of the ECHR provides the right to freedom of expression and the freedom to impart information without interference by public authority and regardless of frontiers. This article links with journalism. The second paragraph of the article prescribes the following:
The exercise of these freedoms, since it carries with it duties and responsibilities, may be subject to such formalities, conditions, restrictions or penalties, as are prescribed by law and are necessary in a democratic society, in the interests of national security, territorial integrity or public safety, for the prevention of disorder or crime, for the protection of health or morals, for the protection of the reputation or rights of others, for preventing the disclosure of information received in confidence, or for maintaining the authority and impartiality of the judiciary. (Art. 10(2) ECHR, 1950).
From the perspective of public intelligence agencies, there is a need for insight in the potential of cooperation with private intelligence collecting and processing parties such as
Bellingcat. Intelligence cooperation is commonly understood as the sharing or exchange of politically useful secret information between states (Crawford, 2018:3784) or agencies, who may also work together in producing or procuring intelligence. Cooperation between public intelligence agencies and citizen intelligence platforms might be beneficial for both parties involved. It might be favourable for public intelligence agencies to be educated in the use of open source investigations - and for citizen intelligence platforms cooperation might be advantageous when already existing open source reports are made available for analysis by public intelligence agencies in an organized way. As cooperation might be beneficial for both parties involved, it is relevant to examine the potential of it. This study will try doing so based on the Bellingcat case including the MH17 files. MH17 was a Malaysia Airlines flight which was shot down in july, 2014 when flying over Ukraine. All 298 passengers were killed. During the investigation into the downing of the plane help came from an unexpected quarter: research collective Bellingcat joined in. Bellingcat published information bringing forward new evidence in the case which, at the time, had not yet been published by others. With the evidence, “Bellingcat identified that the Russian military was involved years before it was confirmed by European officials” (Bellingcat, n.d.a, par. 3). In the context of active citizenship two or more parties are commonly involved, one of them actively standing up for democratic values. In the case of this research, among the actors involved are the MH17 Joint Investigation Team (JIT) that was established to conduct the criminal investigation, the government of the Russian Federation and research collective Bellingcat - the authoritarian regime of Russia undermining the investigations into the downing of flight MH17, and Bellingcat being the guard of our democratic order, the protector of our right to a fair trial in searching for the truth (see 5.1.1). This case accordingly forms part of a worldwide trend of parties standing up for democratic orders that are discredited or suppressed by authoritarian regimes. From this narrative crisis of democracy it follows the societal relevance of the research.
The potential of an intelligence cooperation between public and private, citizen intelligence agencies will be framed under citizenship as the driving force for truth finding, shared values of the legal democratic order and its supporting of law and institutions, and will be investigated in the light of a known framework for intelligence cooperation. Hence, the following research question has been drawn up: To what extent is it possible to outline conditions for intelligence cooperation with the help of the model of Fagersten, illustrated by
the case of the AIVD and Bellingcat on the MH17 files? This research investigates the
potential for public-private intelligence cooperation in the Netherlands only. It is possible however for other investigators to conduct the same research for another country, or for other agencies, based on the model of Fagersten. The above question will be answered by answering the following sub-questions:
1. How is intelligence cooperation commonly assessed in the body of knowledge? 2. What are the central terms and definitions used in the case at hand?
3. What are the interests and drivers for intelligence cooperation as described by Fagertsen?
4. What does the Dutch public intelligence landscape look like? 5. What is the MH17 case?
6. From which different perspectives can the nature of Bellingcat be defined?
7. Which interests and drivers of intelligence cooperation can be distinguished in the case of Bellingcat and the MH17 files?
8. What are triggers for intensifying public-private intelligence cooperation between the AIVD and Bellingcat?
The academic relevance of this study includes the fact that the phenomenon of public-private citizen intelligence cooperation is unexplored. In historical context, intelligence sharing between public and private interests for the purpose of national security was not unusual (Delaforce, 2013:32). Notwithstanding this, the conditions under which the now emerging phenomenon of intelligence cooperation between a public intelligence agency and a private citizen intelligence platform works effectively, have hitherto been unexplored. The goals of this study are therefore to expose the circumstances under which such a cooperation might be beneficial and to develop recommendations based on which triggers for intelligence cooperation can be intensified. With citizen intelligence initiatives rising, it is important to fill this academic vacuum.
The current research maps a new social phenomenon as well. The societal relevance of this study comprises three points. Firstly, there is the importance for citizens to gain knowledge and understanding in public-private liaison. Secondly, the study responds to governments’ need to receive directives on how to establish citizen participation to society. And finally, a common framework that helps create an intelligence collaboration would aid
the Dutch public intelligence services. As regards the first point, in the context of citizenship, citizen intelligence initiatives are activities carried out in public interest. And in order to preserve individuals’ rights, the establishment of trust within society is of utmost importance (Tayfun, 2010:45). From this point of view, for citizens it is important to enhance their understanding and similarly acquire confidence in the interaction with public institutions such as intelligence agencies. In addition, governmental bodies ought to foster initiative and participation to one’s own social environment as well as to the representative democracy. For these bodies it is important to be provided with guidelines for facilitating cooperation with initiatives that do so. Lastly, it may well be that Bellingcat is of great value to the Dutch public intelligence services. Answering the research question could improve current intelligence practices and inform professional decision-making with regard to intelligence cooperation. In the quest towards answering that question lies the societal relevance of this study.
1.1 Reading guide
This thesis begins with an introduction in Chapter 1. Chapter 2 describes the positioning of the research in the body of knowledge. That description is followed by an overview of used definitions and conceptualized terms. Afterwards, the multilateral intelligence cooperation framework as proposed by Fagersten (2012) is presented. Along with an analysis of the perspectives from which Bellingcat works, this will be used as analytical framework for the study. Chapter 3 entails the methodological justification in which the research design and research methodology are justified. The case selection of Bellingcat and the MH17 files is included here as well. In addition, limitations of the study in terms of reliability and validity are addressed. Chapter 4 functions as an introduction into the case. It describes the Dutch intelligence domain and provides explanation on the MH17 case and the role played by Bellingcat. In chapter 5 the results of the two-sided analysis are reported, being a reflection on the phenomenon Bellingcat and the application of Fagersten’s intelligence cooperation framework to the case of Bellingcat. This is followed by an interim conclusion based on which recommendations on how to intensify interests or drivers for intelligence cooperation are made. Chapter 6 outlines the most important results in the conclusion. The conclusion is followed by a discussion on how the findings relate to the body of knowledge. Limitations of
the study and possible avenues for future research are addressed as well. Finally, a list of referenced literature is shown.
2. Theoretical framework
This chapter describes the positioning of research on public-private intelligence cooperation in the body of knowledge of the intelligence domain. The first section of the body of knowledge addresses the way intelligence relationships are commonly assessed in literature. Here, the multilateral intelligence cooperation framework as outlined by Fagersten (2012) is presented. The second section of the body of knowledge outlines structures for public-private cooperation, which will be used for shaping the practical recommendations at the end of this study. In combining the two, a framework for outlining the potential of public-private intelligence cooperation is proposed. This description is complemented by an overview of definitions and concepts.
2.1 Body of knowledge
Though intelligence is one of the world’s earliest and oldest business (Tayfun, 2010:45) intelligence studies still is a very young discipline (Hijzen, 2015:10). Within the field of intelligence, intelligence cooperation is one of the least sufficiently studied aspects (Westerfield, 1996:523 as in Munton, 2009:122). The body of knowledge on intelligence cooperation, as a result, is limited. Most academic research is factual and reflects on historical partnerships or treaties rather than taking an analytical perspective. All contributions agree intelligence is shared when one state gives intelligence in its possession to another state. But why and when do states share intelligence?
The concept of an intelligence partnership is connected to traditional International Relations (IR) theories such as liberalism, realism and constructivism (Roseth, 2020:43). Liberalism encourages international cooperation and holds that complex interdependence is a condition under which cooperation prevails and conflicts will be reduced, which accentuates mutual benefits (Keohane & Nye, 2012 as in Roseth, 2020:53). The opposing school of thought of realism argues that world politics is a world of conflict in which states, not international organizations, are the central actors. Being vulnerable by entering into cooperation agreements might turn into costs on one or both parties should the relationship turn hostile (Keohane & Nye, 2012:12). As a result, states act in their self-interest. The constructivist view of IR claims that some significant aspects of international relations are socially constructed rather than only affected by power politics. Following this line of
thought, changes in inter- or innerstate social interaction will influence (inter)national security. Weighing a partnership on common norms, intelligence relations can develop into something more rather than merely a transaction (Wendt, 1992:417 as in Roseth, 2020:55). Though IR is not directly applicable to the case at hand, the concept of social constructions raises the question whether it is possible to categorise Bellingcat in more than one way only. This relates to different perspectives of how Bellingcat as an organisation can be seen. Bearing the constructivist view in mind, this question will be addressed in section 5.1.
Different scholars have explored intelligence cooperation relationships. Michael Herman examined government intelligence cooperation between the United States and the United Kingdom; John Nomikos explored cooperation in the Eastern Mediterranean region and Behram Sahukar looked at the intelligence cooperation relationships of India. Richard J. Aldrich examined intelligence liaison among NATO allies, Stéphane Lefebvre looked into U.S. relationships with close allies and Glen M. Segell wrote about intelligence agency relations between the European Union and the United States. Lastly, Bob De Graaff and Cees Wiebes investigated the relationship between American and Dutch intelligence communities from 1946-1994. The aforementioned authors did not use demarcated frameworks, nor did they set out specific interests or drivers based on which they analyzed the relationships. Based on their work and work of others it is possible however to identify specific elements of intelligence cooperation. In The International Politics of Intelligence Sharing, Walsh (2010:132) argues that the promise for potential gains is a necessary condition and an important motive for engaging in intelligence sharing. He secondly holds that states establish intelligence cooperation if they believe their partners are unlikely to defect, indicating that the reliability of potential allies plays a role. The lower the reliability of a partner, the higher the risks attached to intelligence sharing. When engaging into intelligence partnerships, countries are wary of penetration of its intelligence agencies (Herman, 1996:207). They may also be concerned that information is shared with a friendly power - something that might compromise intelligence sources. Herman (1996:381) argues furthermore that the general effect of intelligence cooperation is to optimize national strength and international influence in peacetime. He mentions the idea of shared responsibility on shared intelligence tasks and contends that entities within a close intelligence cooperation relationship believe in influencing the national view of the other. Strengthening common perceptions thus might be a general effect. According to Nomikos (2005:191) information sharing indeed offers
valuable knowledge for decision-makers who are therefore in a position to influence policy - directly or indirectly.
Rather than focussing on the risks or detriments of intelligence liaison, as most academic works do, a common framework that helps create an intelligence collaboration would aid. From the above paragraph it follows that states, when considering whether to share intelligence, weigh costs, risks and gains in terms of what they themselves might stand to win or lose in the process of intelligence operations among states (Sims, 2006:196). In combining most of these elements, Fagersten (2012) created a framework for multilateral intelligence cooperation in which he distinguishes between intelligence gains, policy gains, sovereignty costs and risks. I will elaborate on this framework in section 2.4.
The Dutch General Intelligence and Security Agency (AIVD) starts from the formula Threat = (intentions*capabilities*activities)/resilience (Valk, 2019). Its main objective is to mitigate threats, whereas Bellingcat focuses on promoting resilience as part of society and as part of active citizenship. This way, overlaps occur in both objectives and the concept of social constructivism pops up again. According to the constructivist view, one can assign social concepts to something and thereby give meaning to the world through social terminology. Following this line of thought, not only the different perspectives of Bellingcat can be framed in the context of constructivism but active citizenship can be as well. To date, resilience has predominantly been explained with citizens as passive actors. Citizens are little or not mentioned in notions on establishing or increasing resilience. But we are entering a new paradigm in which resilience is increased if citizens become actively involved with society (Bakker, 2012:10). Citizenship in this regard is framed different than what we are used to. It is given new meaning and brings up the possibility of citizens becoming actors in the intelligence process as well. A challenging way of addressing the reality of hybrid intelligence cooperation is by looking at it as ‘intelligence assemblages’ says Buuren (2014:84). “The concept of intelligence assemblages introduces a radical notion of multiplicity into phenomena which we traditionally approach as being discreetly bounded, structured and stable” (Buuren, 2014:84). Intelligence cooperation generally is recognized as something formal and contractual. But between the formally structured organizations that are powered by legal frameworks, a new movement arises: cooperation relations are no longer solely horizontal or vertical - they rather take the form of multilayered networks. The 21st century is characterized by complex cooperation ventures with informal discussion sessions.
Keeping this in mind when looking at intelligence cooperation, the assumption that it is sheer exchange between public intelligence agencies appears obsolete. Intelligence should be put into a broader context than only in the secret one. The essence of intelligence has long been associated with a clandestine process that produces the ultimate form of knowledge: truth. I would argue in favour of a new way of looking at intelligence in which attention is paid to the complexity of intelligence and the complexity of collaborative partnerships in the 21st century. Intelligence is no longer produced by public institutions only. It might as well be a coproduction of anyone who has something to do with it. In so doing, this idea has potential to become the new form of resilience: the state and its citizens, jointly engaging in intelligence. Because when different public and private actors are working together, they will produce a new reality, a new order, and given new meanings to this order (Buuren, 2014:84).
2.2 Terms and definitions
This section will go into the definitions of a) intelligence, b) intelligence cooperation, and c) citizenship.
Intelligence
Kahn (2001:79) defines intelligence in the broadest sense as information. Others hold that intelligence is a type of, but is not synonymous with, information - suggesting there is a difference between the two. Intelligence typically is used to describe a product, or a process, or both (Valk, 2005:8). According to Walsh (2010:6) intelligence is the process of obtaining information that someone prefers to be kept secret. Sherman Kent, pioneer in the methods of intelligence analysis, wrote already in 1949 “Intelligence is the knowledge which our highly placed civilians and military men must have to safeguard the national welfare” (Kent, 1949:49). With him, many others have tried to define intelligence, though there still is no universally accepted definition. In his article Wanted: A definition of intelligence Michael
Warner (2002:21) suggests a much needed new definition of intelligence: “Intelligence is secret state activity to understand or influence foreign entities.” Intelligence is, however, not confined to the realm of secrecy since it can be collected overtly too. Most or all agree that among the core functions of intelligence there is collection, analysis, dissemination and counterintelligence (Wheaton & Beerbower, 2018:324-328) and that it concerns both publicly available and secret information. Some simply assume that intelligence is what intelligence
agencies do (Stout & Warner, 2018:523). Following the same line of thought, the CIA considers intelligence “the information our nation’s leaders need to keep our country safe” (CIA, n.d.a). The latter two definitions however are overly broad and ignore the presence of intelligence activities outside the public domain. In this study, the concept of intelligence is used for the analysis of information processes within both public and private organizations. The definition that will be used in the current study originates from the 2013 JP 2-0, Joint Intelligence Publication of the Joint Chiefs of Staff of the Armed Forces of the United States. According to its definition, intelligence is:
1. The product resulting from the collection, processing, integration, evaluating, analysis and interpretation of available information concerning foreign nations, hostile or potentially hostile forces or elements, or areas of actual or potential operations. 2. The activities that result in the product. 3. The organizations engaged in such activities. (Joint Chiefs of Staff, 2013: GL-8).
Intelligence cooperation
In the literature the term intelligence cooperation is used interchangeably with the terms intelligence relations, intelligence liaison, intelligence sharing and intelligence exchange. In this study that is no different. The 9/11 attacks created a crisis of confidence in counter-terrorism (Tayfun, 2010:46). Countries are more eager to cooperate today than ever. There is a widespread belief that security threats should be taken care of by reciprocal collaboration, cooperation and coordination amidst intelligence institutions within and between countries. The partnership concept is taken from IR but has its origin in organizational science (Wilkins, 2008:362). “The foundation of intelligence cooperation has a transactional nature based on barter” says Roseth (2020:41). The value of a trustworthy intelligence liaison should not be underscored as it will lead to increased security for a state and its population (Omand, 2010 as in Roseth, 2020:42).
A distinction is made between international and domestic intelligence cooperation. International intelligence cooperation is relative to external threats and focuses on foreign intelligence (Roseth, 2020:42). Confusingly this sometimes is referred to as national intelligence cooperation, being a liaison ‘between nations’. Domestic security cooperation encompasses interstate policing and security cooperation through organizations such as
Interpol, though it may also refer to forms of cooperation between different agencies within a country. Herman (1996:210) reminds us that intelligence collaboration is not based on a coordinated national intelligence community toward one another. Rather, it is based on interagency cooperation.
Western security and intelligence agencies have long cooperated - either bilaterally or multilaterally (Lefebvre, 2003:527). Bilateral cooperation refers to cooperation between two countries or, in the abovementioned context, two agencies, for example the National Security Agency (NSA) and the Government Communications Headquarters (GCHQ). Multilateralism refers to an alliance of multiple countries or agencies seeking to achieve the same goal. A prime example thereof is the Five Eyes signals collection club among the United States, the United Kingdom, Australia, Canada and New Zealand (Roseth, 2020:43). Intelligence costs tend to decline and benefits decrease as the number of parties involved increases (Sims, 2006:202). The quality of the liaison will depend on the least trusted member of the group. Consequently, in general most parties will prefer a well-manageable bilateral agreement.
Citizenship
According to the Cambridge English Dictionary (n.d.) citizenship is defined as “the state of being a member of a particular country and having rights because of it”. Apart from being granted several rights, citizenship also entails carrying out duties and responsibilities. Some of them are legally required, such as respecting and obeying the law as well as the rights and beliefs of others. Other responsibilities are rather non-binding or optional, like participating in your local community. Citizenship thus encompasses two broad notions: the legally belonging to a particular nation on the one hand and active participation in society on the other. The latter is considered of having a more ethical dimension to it. The aforementioned concept of active citizenship is rather vague and means different things to different people. This concept will be outlined below, in section 2.3.
An important caveat is that citizenship often is defined and demarcated nationally, though in the case of Bellingcat we witness a phenomenon of citizenship which transcends national frameworks and is transboundary instead. That demands a little deeper investigation. The idea that one’s identity transcends geography and political borders and that responsibilities and rights are derived from membership in a broader class, ‘humanity’, is known as the idea of global citizenship. It generally is used to refer to the idea of world
citizens. The concept of global citizenship is mentioned frequently in opinion pieces and is acknowledged by several NGOs. On their website, Oxfam Novib (n.d.) for instance defines global citizenship as follows: “A global citizen is someone who is aware and understands the wider world and their place in it. They take an active role in their community, and work with others to make our world more equal, fair and sustainable.” Academic contributions on international citizenship however solely focus on asylum and states having responsibilities to protect refugees. The notion of international citizenship, in other words, has developed as a framework for assessing the ethics of states’ foreign policies (Souter, 2016:795). Academically, the concept of international or global citizenship has not yet been broadened or applied to the context of citizen participation in the democratic order, let alone in the context of potential cooperative ventures with intelligence services.
2.3 Central concepts
This section will outline the central concepts of a) active citizenship and b) the intelligence cycle.
Active citizenship
The concept of active citizenship means different things to different people. In general it can be seen as the balance between rights and responsibilities that are inherent to citizenship. If a country gives rights to its people, with those rights come expectations of behavior, certain responsibilities to uphold. Individuals might be taking these responsibilities based on shared democratic rights or principles, such as liberty, equality, truth, justice and the rule of law. Accordingly, active citizenship presupposes the initiative to bear a shared responsibility by referring to citizens actively participating in society. This is something you can learn and something that should be stimulated by the government, according to Jong (2015:12). Participation to one’s own world as well as to the representative democracy should be encouraged and the concept of the citizen as a consumer should be broadened to the concept of the citizen as an actor, whose knowledge might be of use for public institutions. Public authorities, in other words, should stimulate citizen engagement and foster education in civic spirit on the one hand, but should also have an open attitude towards collaboration with citizens. Citizens should be given the opportunity to take part, think along and contribute to
decision making. This is not solely about citizens having a say, it is about making good use of their knowledge. When promoting this idea, the government would act upon a higher principle of citizenship. Rather than acting like politicians, public actors should act like statesmen. Interests of opposing parties are not relevant nor important. It is about the higher goal instead: values being supported by representatives of the democratic order. One could argue we find ourselves in a narrative crisis of democracy when society is not shaped accordingly. In addition, the degree to which citizens actively participate in a society can be linked to the resilience of that society. For some years now, there is a growing awareness of the need for resilience to minimising the impact of incidents and accidents (Bakker, 2012:8). In light of this, the main goal must be creating awareness of the necessity of increased resilience through active citizenship and citizen participation. In the current study, Bellingcat will be framed in the context of citizens contributing meaningfully for solutions - thereby supporting the democratic order. So is understood from their mission statement (Bellingcat’s mission statement can be found in their Policy Plan 2019-2021, available on www.bellingcat.com). This will be further elaborated on in section 5.1. Accordingly, in their support for the democratic order lies the potential of cooperation with intelligence services.
The intelligence cycle
For understanding what intelligence processes do look like, several models can be used. The model most referred to in literature on intelligence is the so-called ‘intelligence cycle’ (Valk, 2005:12). The intelligence cycle or the intelligence process is a general process of the transformation of data into intelligence (Bruenisholz, Wilson-Wide, Ribaux, & Delémont, 2019:241). “It is the process by which information is acquired, converted into finished intelligence and made available to policymakers” (Richelson, 1995:3). The intelligence cycle follows different phases (see figure 1). Among scholars and practitioners there is disagreement on whether it consists of four, five or six steps. All agree the intelligence cycle includes collection, processing, analysis and dissemination. Some say however the process starts by giving direction (Wozniak, 2013:675). Direction is the phase in which intelligence requirements are determined, oftentimes by decision-makers. The requirements are used to initiate the intelligence cycle. The processing of information is consumer-driven in this regard. Collection is the gathering of information needed to fulfil the requirements (Wozniak, 2013:675). Information can be collected overtly (openly) and covertly (secretly) and includes
input from several intelligence collection disciplines, such as human intelligence (HUMINT), signals intelligence (SIGINT), imagery intelligence (IMINT) and open-source analysis (OSINT) (IOSS, 1996). During the next step which is called processing, the collected materials are converted into forms more suitable for the production of intelligence reports. Information is sorted, or, in other words, processed for exploitation. During analysis, the fourth phase of the process, all available and processed information is integrated and interpreted in order to create intelligence products (Joint Chiefs of Staff, 2013:I-16). It is the step in which information is converted into intelligence (IOSS, 1996) whilst focusing on answering the original task (CIA, n.d.b). In the next step, dissemination, finished intelligence products are provided to the consumer, the same policymaker who started the cycle. Sometimes evaluation is added to complete the circle (Bruenisholz e.a., 2019:241). According to the Joint Intelligence Publication of the U.S., evaluation and feedback occur simultaneously and should be considered as an assessment of the intelligence process as a whole (Joint Chiefs of Staff, 2013:I-21). It is the phase in which the decision-maker gives feedback and in which requirements are revised.
Figure 1 - Intelligence cycle
There has been criticism on the intelligence cycle. In his article What’s wrong with
the intelligence cycle? Arthur Hulnick (2006:960) argues the intelligence cycle is no realistic model, as in reality, decision-makers rarely give collection guidance. And, he adds, collection
and analysis, which are supposed to work in tandem, “in fact work more properly in parallel” (2006:961). He also argues the assumption that policy makers wait for the delivery of intelligence products before making policy decisions is likewise incorrect (Hulnick, 2006:964). Valk (2005:14) agrees that in practice, the research process is not as straightforward as the model indicates. The research process is often deviated from when passing through all the phases of the intelligence cycle, which happens, for example, in times of crises when policymakers are especially interested in raw intelligence (Valk, 2005:14). Notwithstanding this, the intelligence cycle still is commonly used to assess intelligence. In the current study it will be used as well to assess Bellingcat’s activities as the step-by-step process is very suitable for this.
2.4 Analysis of intelligence cooperation
In this study, the potential of intelligence cooperation between the Dutch public intelligence agency and private intelligence collecting and processing parties will be assessed, illustrated by the case of Bellingcat including the MH17 files. So will be done based on two analyses which can be found in Chapter 5. First, I will look at the different perspectives applicable to Bellingcat and verify whether this results in clues or starting points for liaison with a public institution. The second analysis is predominantly based on ‘Multilateral Intelligence Cooperation: A Theoretical Framework’ by Fagersten (2012), a framework for the analysis of multilateral intelligence cooperation which includes interests and drivers for cooperation and results in a cooperation outcome subsequently. The two analyses are not mutually exclusive but rather complementary.
Fagersten’s theory for multilateral intelligence cooperation addresses underlying motives for cooperation. De Vries (2019) operationalized this theory, making it suitable for case studies at both state- as well as agency-level. This operationalisation will be taken as a starting point and is worked out in section 3.3. Fagersten’s theory is explicated below.
2.4.1 Interests
According to Fagersten, scope and depth are the two vital dimensions of cooperation. Scope he defines as the number of tasks subject to cooperation. In the intelligence community this means whether functions such as tasking, collection, analysis and dissemination of intelligence are performed within joint structures (Fagertsen, 2012:4). By depth he refers to
the level of density of cooperation. Low depth implies voluntary coordination of states who occasionally share information about what they do on their own. High depth on the other side involves regular interaction between states, based on joint commitments within a chosen area of shared interest. When the frequency of cooperation increases and the range of cooperation widens, benefits rise (Walsh, 2007:161).
In determining why actors engage in intelligence sharing, the interests of these actors are a logic point of departure (Fagersten, 2012:8). This derives from the idea that arrangements are devised because they ought to fulfil important functions, which stems from both the rational choice branch as well as societal functionalism. Rather than presenting a cost/benefit-analysis or a threats/possibilities-table, Fagersten created a model which can be summarized as a simple trade-off: “reaching intelligence and policy gains without ceding sovereignty or compromising one’s sources and methods” (Fagersten, 2012:10). States generally prefer to enter intelligence cooperation when it enhances their intelligence capability. They search for intelligence gains in order to project power abroad or to secure the home territory. One way of achieving this is by cooperating with other states. Most forms of intelligence cooperation can be explained by the interest of intelligence gains, says Fagersten (2012:11). States however can also be motivated to enter intelligence cooperation as it is a way to strengthen, or repair, relations with other countries. This second interest looks into what a specific cooperation can mean for an institution. An arrangement that arises from this interest might have little to do with the actual exchange of intelligence. Instead, it is about “the urge to lend credibility to a specific actor or institute rather than provide for functional needs” (Fagersten, 2012:11). On the other side of the equation are the threats or costs. With regard to sovereignty costs, Koremenos, Lipson and Snidal (2001:771) point out that states traditionally reject centralized international authority. This means that, when accepting any development that restricts their authority or control over their territory, states pay the price of sovereignty costs. But more than anything, states are interested in avoiding risks. Risks are highest when there is a strong possibility for disclosure which would have considerable implications. Or, as Fagersten says, “the wider information is disseminated, the higher is the risk of leaks” (2012:13). Ensuring that shared information is not passed on to a third party is hard. And a high level of intelligence interdependence also means that partners will become aware of each others strengths and weaknesses, which could affect a country’s strategic situation in a negative way. In cases of risk, trust works mitigating. Actors are willing to take
more risk on behalf of each other when there is more trust between them. Notwithstanding this, some risks, like the ones based on technical failures, unexpected events or misunderstandings, can not be mitigated - even in a trusting relationship (Fagersten, 2012).
In sum, the relationship between key interests for states described above should be considered as a tradeoff in reaching intelligence and policy gains without ceding sovereignty or compromising one’s sources and methods (Fagersten, 2012:10). If (potential) benefits are higher than risks, states are expected to enter, or strengthen, intelligence cooperation and vice versa. The balance between these conflicting interests can change. How and why the trade-off changes can be explained by driving factors that sway toward cooperation.
2.4.2 Drivers
Though several scholars have pointed out driving forces - or drivers - that have negative impact, Fagersten distinguishes between three sets of drivers that positively influence interests in intelligence cooperation. The first set of drivers he calls ‘internal demand’, implying that reasons for intelligence cooperation are to be found within a state. Such demands may arise after a national intelligence failure or after an attack on home soil (Fagersten, 2012:16). Within this first set of drivers, Fagersten separates perceived national functional needs, perceived common functional needs and specialization. The first driver addresses and answers to the perception that states face problems they are unable to address alone. States generally seek intelligence gains if it perceives a national need thereof. If this need is put forth by the public or the media, the state may enter cooperation in pursuit of intelligence gains or policy gains. Put differently: perceived national functional needs drive cooperation through augmenting the intelligence or policy gains (Fagersten, 2012:16). The explanation for perceived common functional needs stems from the idea of functional spill over: political solutions will lead to demands for other solutions. Perceived functional needs drive cooperation because they raise the intelligence gains of cooperation. The third internal driver implies that states believe they can reach more cost-efficient solutions when cooperating with others. An example is that pooling intelligence resources might save costs at the national level. Analysis of situations from an economic perspective, in other words, may drive states towards seeking intelligence gains.
The second set of drivers is called ‘external pressure’, implying that changes in state interests could be explained by extraneous factors. In traditional realism, external pressure is
a common explanatory factor when analyzing state behavior (Fagersten, 2012:18). The first driver is called balancing allies. Changes in the power balance in international intelligence power may put pressure on a state, for instance when a partner-state increases its intelligence capabilities. In order to avoid a situation of intelligence dependency, other states might follow and increase their capabilities as well. Balancing the relationship thus influences state interests in two ways: first, by increasing the intelligence gains and secondly, by reducing sovereignty costs. The same holds for situations in which adversarial states increase their intelligence capabilities. In order to balance against the threat, other states might increase their capacities by inserting more resources or by seeking intelligence cooperation with other actors (Fagersten, 2012:19). This second driver is called balancing threats.
Apart from the idea that cooperation is triggered by factors either outside of or within the involved states, there is a third and last set of drivers which falls under the category of ‘cooperative dynamics’. It is based on the idea that cooperation originates from the cooperative process itself. When a cooperative relationship is established, there will be mechanisms reinforcing the cooperation automatically. This set separates between the drivers of trust building and the effects of institutional dynamics and the cooperative structure of cooperation (Fagersten, 2012:19-20). Keeping in mind a state’s interest of minimizing risk, trust building is considered a powerful driver in order to increase intelligence cooperation. Since trust building lowers the risks attached to cooperation, one way to establish trust is by taking risks, praying that the action will turn out successful. Risk-taking, after all, might also backfire if proven unsuccessful. Another way of trust building is becoming familiar with the interests of allies. According to Fagersten, uncertainty is an inexhaustible source of distrust (2012:20). These two trust building mechanisms drive intelligence cooperation by increasing the levels of risk that actors are willing to take in a specific cooperation. The second driver relates to the institutional design of the cooperation and looks into questions of centralization of a cooperation institution as well as the equivalence of all members involved. It focuses, in other words, on the mode of control and the mode of coordination. Hierarchy is proposed as a solution for coordination problems. Though an increase in sovereignty costs may restrain some actors to engage in cooperative relations, elements of hierarchy may also offer intelligence gains in cases where it empowers actors with high intelligence capabilities (Fagersten, 2012:21).
Figure 2 - Fagersten’s model of multilateral intelligence cooperation
In bringing the aspects of the four interests and three sets of drivers together, Fagersten compiled a model for the development of intelligence cooperation:
Three drivers, alone or in tandem, influence state interest. By way of various mechanisms, summarized in the figure below, these drivers render states more or less inclined to seek intelligence and policy gains, safeguard their sovereignty and accept risks in a specific context. (Fagersten, 2012:25)
Fagersten’s model for analysis of intelligence cooperation can be linked to the concept of active citizenship since the latter is the driving force for entitlement to shared values of the legal democratic order and value patterns are reflected in Fagersten’s model as well. Active citizenship is about supporting law and institutions, and shared values are the starting point of intelligence cooperation. Cooperation with citizen initiatives may be put into practice when there is agreement on shared values. Establishing partnerships across organizational borders however is not self-evident. Jong (2015:3) distinguishes three different opportunities for public-private cooperation which all consider the relationship between a public and private party in a different way. She calls these directive, collective and connective coalitions.
Figure 3 - Spectrum for coalition forming according to De Jong (2015:3)
The first type of cooperation - directive coalition - is based on the idea that in public-private partnerships, an organizing role is reserved for the public party. In this context several organizations have an explicit ambition which they aim to realize in alignment with others. This type of cooperation should be regarded as stakeholder management in which public authorities operate out of an orchestrating role to ensure all interests are inserted. One step further, in the second type of cooperation the interplay between input and output is key. Jong calls this the collective coalition. Organizations should be considered partners in a new arena of complementing parties each of which produce something and take something else. Crucial in this respect is a jointly formed ambition. The third type of cooperation is referred to as the connective coalition. With the aim to feed their own ambitions, public authorities play a facilitating role towards private initiators who, based on a personal drive, start a movement spontaneously. These three structures will be used to shape the practical recommendations for an intelligence partnership between the AIVD and Bellingcat at the end of section 5.
3. Methodology
In this chapter a justification for the research design and research methodology are to be found, followed by the case selection of Bellingcat and the MH17 files. Limitations of the study are addressed in terms of reliability and validity.
3.1 Research design
Intelligence cooperation is a highly complex phenomenon. In this study, the potential of a collaboration between a public intelligence agency and a citizen intelligence platform is analyzed, illustrated by the case of Bellingcat. A case study design is appropriate for conducting the abovementioned research as it analyzes complex situations as they are (Creswell, 2014:32). It is suitable for gaining a detailed insight of a phenomenon, better than for instance quantitative research (Flyvbjerg, 2011:301). Experiments do address the same type of research questions as case studies do, but require control over behavioral events (Teegavarapu, Mocko, & Summers, 2008:4). With case studies, that is not the case. Single case studies furthermore are ideal for studying a single case extensively in a short amount of time (Lijphart, 1971:691). In addition, single case studies are known for their contribution to theory building. Given the prescribed circumstances case studies are the suitable method for the current research.
The research at hand uses an exploratory research objective. The case of Bellingcat is used to explore the potential of intelligence cooperation with the AIVD. Fagersten’s model for multilateral intelligence cooperation provides guidelines for this exploration. Since their foundation in 2014, Bellingcat has addressed many issues. In order to interpret in depth, I will turn to one dossier only. That will be the MH17 files. The case of Bellingcat and MH17 provides for sufficient relevant information for the phenomenon under scrutiny in this research as both the AIVD and Bellingcat addressed themselves to the MH17 case. Accordingly, the MH17 case will help me expose circumstances under which a public-private cooperation might be beneficial. As furthermore, Bellingcat is an interesting example of private, or citizen, intelligence, the case of Bellingcat on the MH17 files is distinctive to the theoretical problem that is being considered. The case should be considered a revelatory case
as well: a case that reveals a phenomenon, public-private intelligence cooperation, that hitherto is unexplored.
The case of Bellingcat will be explored to look for patterns in convergence with the interests and drivers Fagersten (2012) distinguishes. Though it is a case study of its own unit it is part of a larger group of units as well (Gerring, 2004:352). In other words: the main interest lays on the case, however the findings may affect changes along the lines of the given theory, being public-private intelligence cooperation. The case is demarcated to one public intelligence agency and one citizen journalism platform that gathers open source intelligence. Within case studies, as they investigate a contemporary phenomenon in its context, the boundaries between the phenomenon and context may not be clearly evident (Van Horck, 2020). Since the combination AIVD-Bellingcat is not a ‘typical’ case, the outcomes of this study will only apply to a potential cooperation between AIVD and Bellingcat and not to other intelligence agencies or organisations. Hence, the external validity of this case study is low. Criticism of generalisability however is is of little relevance when the intention is one of particularisation, says Willis (2014:5). And, as stated previously, for other investigators it is possible to conduct the same research, based on the same interest and driving forces, for another country or illustrated by another case. Formulating theories for a single case study could satisfy the criterion of external validity. Using a logic model such as the one from Fagersten (2012) increases the internal validity of a case study (Teegavarapu e.a., 2008:7). Given that public-private intelligence cooperation in the context of citizenship is not yet found in existence, the outcomes of this research nevertheless should strictly be seen as initial practical recommendations which will need further elaboration.
3.2 Research methodology
Little academic contributions are known that analyze forms of intelligence cooperation and even fewer academic contributions are known that examine the potential for public-private intelligence cooperation. Hence, as this study aims to fill an academic gap, its character will be exploratory. The study will be a literature study into Bellingcat. The line of argument is developed in three layers. The top layer consists in the conceptual model of Fagersten and is the most abstract one of the three. The second layer includes the illustration of the case of Bellingcat and the MH17 files. Based on this data it is possible to come to a conclusion. The
third layer is based on logic. When both concepts and data are absent, in order to reach conclusions, logic fills the holes. Only if in the analysis the findings of the three layers point in the same direction, it is possible to make a fairly solid statement. Hence, on all three layers, the same elements should reappear. The conclusions of this study will be built on that three-stage basis. This enables me to draw conclusions from a N=1 design. It is an exploratory research into the potential of cooperation between agencies and organizations. This is done by providing a conceptual framework (‘Fagersten’) and a N=1 case study (Bellingcat/MH17) as an illustration.
Using different data sources will allow me to gain a better understanding and a more complete picture of the case. Consequently, I will use multiple sources of evidence: policy documents, state archives, online interviews, press statements and media reports. Using multiple sources of evidence is called triangulation of data sources (Shoaib, & Mujtaba, 2016:86; Yin, 2003 as in Olsen, 2014:2), which allows me to compare the construct validity of the different data sources as well. An obvious limitation of the sole use of literature as a source is the lack of triangulation of data collection methods, which has negative consequences for the construct validity in turn (Cook & Campbell, 1979 as in Gibbert, Ruigrok, & Wicki, 2008:16). With regard to the different sources of evidence, some are easier to get access to than others. I have to assume there is a possible bias in the reporting: the authors of the documents might be biased. This might skew my results, damaging the reliability that is concerned with the variation in case study findings (Gibbert e.a., 2008:7).
Bellingcat does a lot of open source in-depth investigation. This however is not to say that all they investigate has been published and is thus discoverable and accessible online. The study, in other words, has data accessibility limitations. Having access to all data collected and analyzed by Bellingcat would booster the validity of the outcomes of the study, however this was not possible. This applies to the feasibility of the study.
Due to the selection of the research design and methodology I am interested in analytical or theoretical generalization rather than statistical generalization. Hence, the main question is whether the case of Bellingcat is relevant for the phenomenon I aim to explore. Detailed examination of the case of Bellingcat in general and the MH17 files in particular is used to explore similarities with the theory of Fagersten in order to outline the potential for an intelligence partnership. Despite the exploratory character of the study, this research aims to
provide an insight in interests and drivers for public-private cooperation based on which practical recommendations can be made.
3.3 Operationalization
Fagersten created a model for the analysis of multilateral intelligence cooperation between state actors. In this study I work it out bilaterally between one state actor and one non-state actor. I am well aware of the fact that the model of Fagersten is not developed for such an analysis but chose to use it nonetheless because the model consists of elements that are suited to lend order and structure to my research. More importantly, there are no alternative or better suited models that can be used to outline conditions for public-private intelligence cooperation. As a second caveat I would like to add that Bellingcat is not into prognostic intelligence. It does not aim at answering questions like ‘how does the Russian Federation develop and what is the biggest threat we face the coming five years’. Notwithstanding this, the kind of research Bellingcat is doing is similar to the research being undertaken by intelligence services. There is something to be said for using Fagersten’s model in this sense. In chapter 2.4 Fagerstens’ framework for multilateral intelligence cooperation has been defined. The elements based on which the potential for intelligence cooperation can be outlined, have been listed. These elements are interests and drivers, as Fagersten calls them. It is relevant to operationalize these elements for analysis of the case of Bellingcat. Fagersten’s theory however is state-oriented: it is composed for the analysis of cooperation between states. Thankfully, De Vries (2019:21) operationalized the interests and drivers in order to conduct a case study at agency-level. His operationalisation will be adopted and is, slightly changed, outlined below. A schematic representation is given on page 31 and 32.
De Vries created a theoretical optimum for the analysis of intelligence cooperation and organizational intelligence consisting of eleven elements. He combined interests and drivers for intelligence cooperation as laid out by Fagersten with drivers of organizational intelligence as discussed by Wilensky (1967). His theoretical optimum is calibrated for the analysis of cooperation between both states and agencies, the latter making it possible to apply it to current study. Hence, in the current research the framework as put forward by De Vries (2019) will be used to analyze the potential for intelligence cooperation between the AIVD, public intelligence agency of the Netherlands, and private intelligence collecting and
processing parties such as Bellingcat. According to De Vries the interests and drivers described by Fagersten seem as valid for agencies as they are for states (De Vries, 2019:21). This implies that, in section 2.4 where Fagersten’s theory is described, it is possible to read ‘agency’ instead of ‘state’. The framework of De Vries involves both intelligence cooperation and organizational intelligence. Given that the current research examines the potential for intelligence cooperation, not organizational intelligence, the latter section of the framework will not be used due to lack of relevance. Using only the first part, the framework however still is applicable to the current study.
Fagersten’s framework distinguishes between four interests and three drivers. The interests can be summarized as follows: reaching intelligence and policy gains without ceding sovereignty or compromising one’s sources and methods (Fagersten, 2012:10). The three sets of drivers are listed as internal demand, external pressure and cooperative dynamics. The two vital dimensions of cooperation of scope and depth, as Fagersten calls it, are left out of the operationalisation. Assessing the scope and depth of an intelligence cooperation might be useful for the analysis of pre-existing structures of cooperation, they however are not relevant for analysis of the potential of yet non-existent cooperation structures. As regards the operationalization of the interests: when an agency seeks cooperation in order to enhance its intelligence capability, for example to gather information or to enhance its analysis capability, with the aim of doing their part in securing the home territory, this might be an indicator of the interest of intelligence gains (De Vries, 2019:21). An indicator for the interest of policy gains is when an agency seeks cooperation in order to strengthen its relationship with that agency. If an agency accepts interference from another agency and acts to it, this is indicated as the interest of sovereignty costs. The same applies when an agency asks for cooperation in what is regarded as ‘their’ case, or when an agency accepts being part in a structure where it is not completely independent. Lastly, an indicator for the interest of risks is when agencies disseminate information that would risk disclosing sources and methods (De Vries, 2019:22).
Apart from these state or agency interests, Fagersten distinguished several drivers that might influence the balance of the interests. The first set of these drivers points to reasons for cooperation that can be found within the state. More specifically, cooperation might be stimulated when problems are referred to as if they can impossibly be solved by agencies on their own or if agencies believe they can reach more cost-efficient solutions together. If these
indicators are present, they point to internal drivers. The second set of drivers of external pressure mentions drivers from outside the intelligence actor (De Vries, 2019:22). Cooperation might be stimulated by the public or by politicians, publicly raising attention for a specific threat or problem, as is the case with, for example, terrorism (De Vries, 2019:22). In his operationalization, De Vries placed this indicator for drivers for cooperation under internal demand. In the current study this indicator however is placed under external pressure because the stimulus of these groups is, in the case of agencies, not regarded as an internal stimulus. It is seen as external stimulus instead. Changes in the international system such as shifts in the intelligence power between states, or agencies within a state, are other indicators of external driving forces. The same applies for exogenous threats that are considered as if they should be tackled in a joint approach. The third set of drivers, cooperative dynamics, are drivers stemming from the process of cooperation itself. A cooperative structure functioning as underlying force, thereby fostering further intelligence cooperation, is an indicator of the presence of these drivers (De Vries, 2019:22). When agencies are willing to take risks, this is an indicator of the presence of trust building. And when a cooperative institution is being centralized or when there is effective authority over the cooperative setting, these are other indicators of changes in the extent of cooperation. Because a cooperation between the AIVD and Bellingcat does not exist yet, it is impossible to examine cooperative dynamics. For this reason, the cooperative dynamics as outlined by both Fagersten and De Vries will not be included in the analysis but will instead be used as starting points for the recommendations in chapter 5. In table 1 on the next page, all elements derived from both Fagersten’s model and De Vries’ operationalized framework are summarized. My decision to relocate the indicator of the public call for attention on a certain threat or problem by public or politicians (the indicator that originally was included in drivers of internal demand) to drivers of external pressure, is included in this table and is shown in blue.
4. Context of the case
The case at hand covers the activities of Bellingcat with respect to the downing of flight MH17. Analysis of the presence of interests and drivers of intelligence cooperation within this case relies predominantly on information originating from Bellingcat’s own website as well as newspaper articles on Bellingcat or interviews given by Elliot Higgins, founder of Bellingcat. Additionally, investigation reports into the downing of MH17 have been studied. The same holds for press statements from world leaders, addressing the disaster of the crash of flight MH17. The analysis can be found in chapter 5. Chapter 4 functions as an introduction into the case. It describes the Dutch intelligence domain and provides explanation on the MH17 case and the role played by Bellingcat. Additional information was gathered during a background interview with someone working at the AIVD who wishes to remain anonymous.
4.1 Public intelligence agencies in The Netherlands
In the Dutch context, one distinguishes the General Intelligence and Security Services (AIVD) and the Military Intelligence and Security Services (MIVD). The Dutch police as well is empowered with an intelligence task under article 91 of the Intelligence and Security Services Act (Wiv 2017) which is called the National Police Intelligence Service (DLIO). These three stakeholders together comprise a conglomerate with autonomous intelligence tools and resources. The National Coordinator for Security and Counterterrorism (NCTV) has no autonomous mandate or authorization for the collection or processing of intelligence.
The Wiv 2017 is based on conditions as set out in the European Convention on Human Rights (ECHR), on case-law from the European Court of Human Rights (ECtHR) and on the Dutch constitution. The Wiv 2017 states the rights and duties of the AIVD and MIVD and prescribes what exactly the Services can and cannot do to safeguard national security and protect the democratic order. The AIVD and the MIVD are restricted by law insofar that fundamental rights of citizens, like the right to privacy, are enshrined in the Dutch constitution. In order to guarantee no unnecessary intrusion on citizens’ fundamental rights, the ECHR requires independent review beforehand. In addition, the Wiv 2017 also states how oversight on the Services is regulated. Article 8 sub 2 of the Wiv 2017 entrusts the AIVD six
