Big Data, Emerging Technologies and Intelligence: National Security Disrupted

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“Drawing on substantial and exclusive access to the Australian Intelligence
Community, this book provides a timely, detailed, and thorough analysis of
the many ways in which big data is transforming intelligence and broader
society. Dr Miah Hammond-Errey brings intelligence studies into the digital
era with this original contribution to the scholarly field on intelligence and
national security.”
Kira Vrist Rønn, Associate Professor, University of Southern Denmark
“With this book, Dr Hammond-Errey has produced a path-breaking
empirical analysis of how Big Data is transforming intelligence and the
challenges to which this transformation gives rise. Based on interviews with
around 50 people working in and around the Australian National Intelli-
gence Community, this book offers an invaluable guide to understanding the
impact of the Big Data landscape on intelligence practice in liberal democ-
racies and how this affects the intelligence-state-citizen relationship. It is
essential reading for students of intelligence and for all those working in the
field of intelligence, including its oversight.”
Mark Phythian, University of Leicester, UK
“This book is a timely account of the way big data and emerging technology
have been disrupting intelligence and society. Dr Hammond-Errey develops
an innovative framework of the landscape of big data that raises important
questions about legitimacy and public trust in democratic institutions, the
changing role of intelligence analysts, and the tendency to subject surveil-
lance capabilities to greater democratic accountability.”
Christian Leuprecht, Royal Military College of Canada and
Queen’s University, Canada

Big Data, Emerging Technologies and
Intelligence
This book sets out the big data landscape, comprising data abundance, digi-
tal connectivity and ubiquitous technology, and shows how the big data
landscape and the emerging technologies it fuels are impacting national
security.
This book illustrates that big data is transforming intelligence production
as well as changing the national security environment broadly, including
what is considered a part of national security as well as the relationships
agencies have with the public. The book highlights the impact of big data on
intelligence production and national security from the perspective of Aus-
tralian national security leaders and practitioners, and the research is based
on empirical data collection, with insights from nearly 50 participants from
within Australia’s National Intelligence Community. It argues that big data is
transforming intelligence and national security and shows that the impacts of
big data on the knowledge, activities and organisation of intelligence agencies
is challenging some foundational intelligence principles, including the dis-
tinction between foreign and domestic intelligence collection. Furthermore,
the book argues that big data has created emerging threats to national
security; for example, it enables invasive targeting and surveillance, drives
information warfare as well as social and political interference, and chal-
lenges the existing models of harm assessment used in national security. The
book maps broad areas of change for intelligence agencies in the national
security context and what they mean for intelligence communities, and
explores how intelligence agencies look out to the rest of society, considering
specific impacts relating to privacy, ethics and trust.
This book will be of much interest to students of intelligence studies,
technology studies, national security and International Relations.
Miah Hammond-Errey is the Director of the Emerging Technology Program
at the United States Studies Centre at the University of Sydney. She has a
PhD from Deakin University, Australia.

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Big Data, Emerging
Technologies and Intelligence
National Security Disrupted
Miah Hammond-Errey

First published 2024
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© 2024 Miah Hammond-Errey
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ISBN: 978-1-032-48558-4 (hbk)
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DOI: 10.4324/9781003389651
Typeset in Times New Roman
by Taylor & Francis Books

Contents
List of illustrations viii
Acknowledgements ix
List of Acronyms and Abbreviations x
Introduction 1
1 Big Data Landscape Fuels Emerging Technologies 22
2 Big Data Landscape Challenges Fundamental Intelligence
Principles and Practices 45
3 Big Data Landscape: New Social Harms and National Security
Threats 65
4 Big Data and Intelligence in Practice 83
5 Data and Privacy 114
6 Ethics and Bias 131
7 Trust, Transparency and Legitimacy 155
Conclusion 181
Appendices 192
Index 198

Illustrations
Figures
0.1 NIC Agencies (ONI 2017) 8
1.1 Features of the Big Data Landscape 24
Tables
0.1 Categories of Interviewees 11
Appendix Table A: Australian NIC Agencies 192
Appendix Table B: Types of Intelligence 195

Acknowledgements
This book is an adaptation of my PhD thesis. Thank you to my primary
supervisor Chad Whelan and secondary supervisor Diarmaid Harkin for all
your support, guidance and friendship throughout both processes. Thank
you, Tom Barrett, for your research and referencing assistance.
A huge thanks to colleagues, friends and family who have supported this
research. I can’t name many of you by name, however, am incredibly grateful
for your support–you made this research possible. I can’t thank you enough.
I’m grateful to the individuals and agencies who participated in this
research. I feel honoured to be privileged with your insights, experiences and
time. Thank you for sharing of these so willingly. Thank you to the agencies
for entrusting me with this research. This research draws on a variety of
perspectives from within the NIC. I have tried to reflect these (sometimes
contradictory!) views and common discourse faithfully. However, this book
contains only a small part of the discussions and is not a comprehensive
reflection of all the topics and issues covered.
The research was supported by a National Security Big Data Scholarship
from D2D CRC and a University Postgraduate Research Scholarship from
Deakin University. Additional support was also provided by D2DCRC in the
form of an Applied Research and Collaboration Award (2017) and an
Applied Research Grant (2019).
This book is a contribution towards greater understanding of national
security and big data as well as the ways the big data landscape fuels emer-
ging technologies. I’d like to continue this conversation with you. You can
also find out more by listening to my podcast, Technology and Security.

List of Acronyms and Abbreviations
ACIC Australian Criminal Intelligence Commission
AFP Australian Federal Police
AGO Australian Geospatial-Intelligence Organisation
AIC Australian Intelligence Community
ASIO Australian Security Intelligence Organisation
ASIS Australian Secret Intelligence Service
ASD Australian Signals Directorate
AUSTRAC Australian Transaction Reports and Analysis Centre
HA Department of Home Affairs
IGIS Inspector-General of Intelligence and Security
DIO Defence Intelligence Organisation
NIC National Intelligence Community
SDM Senior Decision-Maker
ONI Office of National Intelligence
ODM Operational Decision-Maker
TECH Technologist
ISME Independent Subject Matter Expert

Introduction
This book examines the impact of the technological phenomenon ‘big data’
on national security intelligence and decision-making. Data is all around us.
Big data has become a prevalent feature in commercial enterprise (Cukier
2010; Manyika et al. 2011; Reinsel, Gantz & Rydning 2017; Yiu 2012) from
shopping to socials, travel to transport and communications to finance. It is
also increasingly used in national security (Landon-Murray 2016; Van Puy-
velde, Coulthart & Hossain 2017). Big data and associated analytics are
presented as offering significant potential for a safer and more secure nation
(Akhgar et al. 2015; Manyika et al. 2011; Mayer-Schönberger & Cukier
2014) and are being adopted before their impacts are well understood.
Despite the significant impacts of big data on intelligence activities, empirical
research into its impacts is still in its infancy.
The ‘information age’ continues to provide an ever-expanding quantity and
variety of information (Degaut 2015, p. 511) that underpins many of the data-
driven technologies impacting national security. In 2014, it was forecast that by
2020 there will be as many bits in the digital universe as stars in the physical
universe (International Data Corporation 2014), and in 2019 this was revised to
forty times the number of bytes than stars in the observable universe (DOMO
2019). According to the International Data Corporation (International Data
Corporation 2022), by 2026 there will be more than 220 Zettabytes (220 billion
Terabytes) of data added annually to the global datasphere – the summation of
data we create, capture or replicate. This will be almost three times the 83 Zet-
tabytes produced in 2021 – growing at a rate of 21 per cent per year (Interna-
tional Data Corporation 2022). We are also more digitally connected than ever
before. The increasing interconnectedness of our systems and our infra-
structure – including our reliance on them – is transformative and unprece-
dented. In January 2023, more than 5.4 billion people out of the eight billion
global population (68 per cent) were using a mobile device, with the majority
being smartphones (Kemp 2023). Estimates of the number of devices connected
to the internet vary widely; however, there is consensus (Evans 2011; Gartner
2017) that this number has overtaken the global population – Ericsson (2022)
estimated the number of connected devices in 2022 to be 23.6 billion and predict
that by 2028 that number will reach 45.8 billion.
DOI: 10.4324/9781003389651-1

2 Introduction
Increasingly vast amounts of data are captured from and about humans,
machines and the natural environment, challenging political and economic
models (Mayer-Schönberger & Ramge 2018; Sadowski 2020; Schwab 2017;
Zuboff 2019). The abundance of data made possible by improvements in data
storage and computational capabilities, combined with digital connectedness
and ubiquity of technology, drive the big data phenomenon. The speed of
technological change has impacted how we store, interpret, analyse and
communicate information in society (boyd & Crawford 2012; Kitchin 2014a).
Intelligence activities are funded by the nation-state, with the express pur-
pose of protecting national interests and keeping citizens safe; however,
information about intelligence agencies and their activities is notoriously
sparse (Andrew 2018; Van Puyvelde 2018, pp. 380–381). Lundy et al. (2019,
p. 587) argue that ‘intelligence is essential to modern statecraft in times of
war and peace… [and] its vital role deserves – and requires – better general
comprehension’. Empirical research to date on intelligence activities, espe-
cially outside the United States, has been extremely limited (Hughes, Jackson
& Scott 2008; Van Puyvelde 2018; Zegart 2022). There have been ‘very few, if
any, reflections on how the Australian intelligence community works, its
contributions, or of its importance to policy and decision-makers across
government’ (Symon 2018). Whilst the scarcity of information is under-
standable, the growing role of intelligence in society presents a significant
need for understanding of the public value of intelligence agencies and
ensuring their accountability in liberal democracies. Gill and Phythian (2006)
argue that citizens have been excluded from knowledge of intelligence policies
and practices for too long.
The book shows that big data is transforming what intelligence is, how it is
practised, and the relationships intelligence organisations have with society.
This includes both the collection of information and secret intelligence as
well as the analytical processes used to create intelligence products and
advice to inform decision-making. The book details how big data is trans-
forming aspects of intelligence production specifically and the national
security environment more broadly. The book leverages semi-structured
interviews with almost fifty senior and operational intelligence officers and
decision-makers within the Australian National Intelligence Community
(NIC).
1
The NIC represents a unique group of interview participants, and
this research is the first to access them as a community. The focus of the
research is from the perspective of Australian national security professionals;
however, these perspectives are applicable and relevant internationally to all
states that invest significantly in intelligence collection technologies.
The introductory chapter examines and defines the key concepts of the
book, providing some background and context as well as offering insight into
how this research contributes to our understanding. First, it looks at big
data, followed by national security and intelligence. It is important to explain
these terms here as they are often used in different ways. The inconsistent use
of such concepts can lead to confusion and all three are essential to

Introduction 3
understanding the impact of big data on intelligence and national security.
Furthermore, the book argues that the advent of big data is shaping these
concepts, including what we see as intelligence and expanding the notion of
national security to include new social harms. The book shows how big data
is shaping the activities, knowledge and organisation of intelligence functions
that are intended to support policy makers in developing responses to these
new harms and vulnerabilities.
Big Data, National Security, and Intelligence
Big Data
Big data is an amorphous concept that is used to refer to large, diverse, growing
and changing datasets (Bennett Moses & Chan 2014; Chan & Bennett Moses
2016, 2017; Malomo & Sena 2016). Big data arose from technical advances in
storage capacity, speed and price points of data collection and analysis as well as
by the move towards understanding data as ‘continuously collected, almost-
infinitely networkable and highly flexible’ (Metcalf, Keller & boyd 2016, p. 2).
Prior to big data, databases were constrained and unable to simultaneously deal
with the original 3Vs of big data – volume, velocity and variety (Kitchin 2014b,
p. 68; Laney 2001). However, increased computational power, new database
design and distributed storage enabled the collection and analysis of big data
(Kitchin 2014b, p. 68). The unprecedented volume and size of data sets that
cannot be manually processed precipitated analytical solutions to analyse data
and derive insights, expanding the term big data from referring solely to the
storage of data (Mayer-Schönberger & Cukier 2014).
The term has evolved from the original 3Vs to include value derived from
understanding data sets as a whole and by drawing insights using new ana-
lytical techniques (boyd & Crawford 2012; Kitchin 2014a; Kitchin & Laur-
iault 2014). Kitchin (2014b) considers big data as fine-grained in resolution
and uniquely indexical in identification; relational in nature, containing
common fields that enable the conjoining of different data sets; and flexible,
holding the traits of extensionality (new fields can be added easily) and sca-
leability (can be expanded in size rapidly). Importantly, big data ‘is less about
data that is big than it is about a capacity to search, aggregate and cross-
reference large data sets’ (boyd & Crawford 2012, p. 663). It is this ability to
use the data for some type of decision or action that defines big data. As
others have aptly put, ‘big data are worthless in a vacuum. Its potential value
is unlocked only when leveraged to drive decision-making’ (Gandomi &
Haider 2015, p. 140). The requirement to consider the veracity of data and
value led to the expansion of the 3Vs definition of big data – volume, velo-
city and variety (Kitchin 2014b, p. 68; Laney 2001) – to a 5V definition that
includes veracity (certainty and consistency in data) and value (insights into
and from data) (Akhgar et al. 2015; van der Sloot, Broeders & Schrijvers
2016).

4 Introduction
A range of terms are used, sometimes interchangeably, to describe analysis
of big data. These include: big data analytics (Cloud Security Alliance 2013;
Beer 2018; Minelli, Chambers & Dhiraj 2013; Power 2014; Pramanik et al.
2017; Shu 2016), advanced analytics (Babuta 2017; Chawda & Thakur 2016;
Shahbazian 2016), big data computing (Chen, Mao & Liu 2014) and data
mining (Pramanik et al. 2017). Additionally, the terms artificial intelligence,
machine learning and algorithms are included in big data analytics for the
purpose of this study. In the book, big data is viewed broadly and refers to
all these components, including the technologies and analytics. Participants
in this research highlighted three key features of big data for national secur-
ity which, the book argues in Chapter 1, come together to form a big data
landscape.
National Security
National security – and our conceptualisations of it – evolves over time as it
is situationally, culturally and temporally contextual (Katzenstein 1996).
National security is a commonly used concept in international relations and
the analysis of policy decisions; however, its essential meaning is more widely
disputed than agreed upon (Baldwin 1997; Dupont 1990; Liotta 2002).
Maintaining national security is usually posited as the reason for the appli-
cation of intelligence resources. In a foundational text, Arnold Wolfers
characterised security as ‘the absence of threats to acquired values and sub-
jectively, the absence of fear that such values will be attacked’ (Wolfers 1962,
p. 485). Baldwin (1997, p. 13) subsequently refined ‘the absence of threats’ as
‘a low probability of damage to acquired values’. Wolfers (1962, p. 150) notes
that the demand for a policy of national security is primarily normative in
character and security points to some degree of protection of values pre-
viously obtained: ‘Security is a value, then, of which a nation can have more
or less and which it can aspire to have in greater or lesser measure’. Wolfers’
position has not gone unchallenged, as the field struggles to agree on ‘how
much security’ is desirable.
Zedner (2003, p. 155) posits that ‘security is both a state of being and a
means to that end’. Whelan (2014, p. 310) explains that we can understand
Zedner’s (2009) conceptualisation of security as an ‘objective state of being
more or less “secure” and as a subjective condition based on how secure we
“feel”’. Gyngell and Wesley (2007, p. 233) see security as a prudential value,
conceived as a condition which must be maintained against others’ potential
to degrade it. Buzan, Waever and de Wilde (1998) highlight that nation-state
security requires a referent object to make sense. The objective state of
security continues to imply a ‘referent object’ and an existential threat to that
object and the special nature of security threats justifies the use of extra-
ordinary measures to handle them (Buzan, Waever & de Wilde 1998).
Whelan (2014, p. 310) furthers this, noting the ‘referent objects and range of
potential threats have considerably broadened’, including the special nature

Introduction 5
of national security threats, among others. Thus, the political context of
national security is an important dimension (Dupont 1990). Wolfers (1952,
p. 500) highlights the challenges for those who bear the responsibility for
choices and decisions, that is, national security decision-makers:
Decision-makers are faced then, with the moral problem of choosing
first the values that deserve protection … the guarantee it may offer
to values like liberty, justice and peace … They must decide which
level of security to make their target … finally they must choose the
means and thus by scrupulous computation of values compare the
sacrifices.
The book argues that big data has created new social harms which are – or
need to be – considered by decision-makers as national security threats or
vulnerabilities. In the book, national security is considered a state of trust on
the part of the citizen that risks to everyday life, whether from threats with a
human origin or impersonal hazards, are being adequately managed to the
extent that there is confidence that normal life can continue (Omand 2010,
p. 9). Omand (2010) sets out three propositions underpinning the modern
approach to national security: psychological safety, citizen-centric view of
threats and hazards, and informed decision-making. This last point is crucial
in the use of big data: ‘the key to good risk management, maintaining that
delicate balance, is to have better informed decision-making by government
and thus place greater weight on the work of the intelligence community’
(Omand 2013, p. 21).
2
Symon & Tarapore (2015, p. 9) add that ‘making sense
of complex systems and phenomena – creating knowledge – is central to
sound national security decision making.’
Understanding national security, what it broadly encompasses and how
decisions are made to secure nations is critical to the way that big data
impacts on it and in understanding how intelligence resources are focused.
This research shows that participants see new technologies, like big data, as
expanding notions of national security to include, for example, information
warfare and aspects of how society functions online as infrastructure critical
to national security. Participants perceive that big data impacts on how
intelligence agencies can identify and respond to these increasing, diverse and
diffuse national security threats.
Intelligence
Intelligence here is understood through a combination of definitions. Intelli-
gence is ‘information [that] is gathered and analysed, sometimes secretly, and
then used to understand a particular situation and act with advantage in it’
(Rolington 2013, p. 17). Intelligence is ‘knowledge vital for national survival’
(Kent 1966, p. vii). It is information that has been collected, processed and
narrowed to meet the needs of policy and decision-makers in relation to

6 Introduction
defence, foreign policy, national state affairs (such as diplomacy, trade and
economics) and security (Lowenthal 2012).
Intelligence in practice can be thought of in three ways, sometimes simul-
taneously (Lowenthal 2012, p. 9), as knowledge, as an organisation and as
either an activity (Kent 1966) or product (Lowenthal 2012). Kent’s classic
characterisation covers the ‘the three separate and distinct things that intel-
ligence devotees usually mean when they use the word’: knowledge, the type
of organisation that produces that knowledge and the activities pursued by
that organisation (Scott & Jackson 2004, p. 141).
Omand (2020, p. 472) defines the purpose of intelligence to help ‘improve
the quality of decision-making by reducing ignorance, including reducing the
vulnerability of the decision-maker to uncertainty’. Intelligence production is
one of the primary mechanisms for framing information and analysis to
inform national security decision-making (George & Bruce 2014; Kent 1966;
Lowenthal 2012; Omand 2010). The purpose of the intelligence community is
to assist policy makers with national security issues (Gookins 2008).
The relationship between intelligence, policy production and senior
decision-makers is vital in the national security environment (Coyne 2014;
Lowenthal 2012) as intelligence is intended to reduce uncertainty for
decision-makers (Agrell 2012; Betts 2009; Davies, Gustafson & Rigden 2013;
Dupont 2003; Fingar 2011; Kent 1966; Lowenthal 2012; Marrin 2009; Heuer
& Pherson 2015; Spracher 2009). Without use by decision-makers –in order
to achieve national security –intelligence would be redundant.
The combination of these definitions acknowledges the changing informa-
tion environment, accounts for the impact of big data and open-source
information on intelligence activity, while acknowledging the extant role of
secret intelligence collection as well as decision-makers acting on the
intelligence. Furthermore, as Omand (2020) highlights, it is significant that
intelligence aims to reduce uncertainty and improve decision-making in
matters of nation-state security.
The relationship between national security and intelligence is noted by Agrell
and Treverton (2015, pp. 32–5): ‘the essence of intelligence is hardly any longer
the collection, analysis, and dissemination of secret information, but rather the
management of uncertainty in areas critical for security goals for societies.’
Additionally, the ‘use of the term in circles outside of government –“commer-
cial intelligence”, for example –can dilute its meaning, rendering intelligence a
synonym for information’(Richardson 2020a, p. 154). The term intelligence is
also used extensively in different government domains, such as law enforcement,
criminal, security, domestic, foreign and counterintelligence.
The book looks broadly across national security and intelligence activities
undertaken within the context of the National Intelligence Community,
rather than at a single academic discipline. It includes the intelligence appa-
ratus, but also the policy and political decision-making component essential
to national security. Big data, national security and intelligence are complex
concepts with a variety of meanings. Nevertheless, they can be loosely

Introduction 7
defined. The book argues that the relationship between intelligence producers
and users of intelligence – those that make political calculations about
national security – is critical and interconnected, especially in a big data era.
Furthermore, the book demonstrates the need to take a holistic view of
intelligence, defined by its purpose rather than field of application, and to
include policy and decision-makers.
Australian National Intelligence Community
This section provides an overview of the Australian national security archi-
tecture and background to the Australian National Intelligence Commu-
nity – including its composite agencies, oversight framework and legislative
foundations for an international readership. It also outlines the methodology
and analytical process of the research. It provides some context, especially
for international readers, to engage with the perspectives that participants
offered. Whilst this research is Australia specific, the themes surfaced here
are expected to apply in many democratic countries.
Ten agencies make up the Government’s intelligence enterprise – collec-
tively known as the National Intelligence Community (NIC) – working to
collect, analyse and disseminate intelligence information and advice in
accordance with Australia’s interests and national security priorities (ONI
2017). The NIC is a relatively new grouping of agencies, having expanded
from the six agencies known as the Australian Intelligence Community
(AIC): the Office of National Intelligence (ONI) – formerly the Office of
National Assessments (ONA), the Australian Signals Directorate (ASD), the
Australian Geospatial-Intelligence Organisation (AGO), the Australian
Secret Intelligence Service (ASIS), the Australian Security Intelligence
Organisation (ASIO) and the Defence Intelligence Organisation (DIO). To
these six have been added the Australian Criminal Intelligence Commission
(ACIC) and the intelligence functions of the Australian Federal Police (AFP),
the Australian Transaction Reports and Analysis Centre (AUSTRAC) and the
Department of Home Affairs (Home Affairs).
This expansion followed the 2017 Independent Intelligence Review (IIR),
which argued that the AIC’s collective tasks were growing more difficult,
given the increasing complexity of Australia’s geostrategic environment, the
rapid pace of technological change, and the broadening scope of security and
intelligence challenges (Department of the Prime Minister and Cabinet
2017). The IIR found that, while individual agencies were performing very
well, a higher level of collective performance could be achieved by strength-
ening integration across Australia’s national intelligence enterprise (Depart-
ment of the Prime Minister and Cabinet 2017). The IIR recommended
expansion from the six agencies of the AIC to the current ten agencies,
and the establishment of an Office of National Intelligence (ONI), incorpor-
ating the Office of National Assessments, to lead the community (Department
of the Prime Minister and Cabinet 2017).

8 Introduction
The creation of the NIC has been matched by a substantial growth in
budgets for Australian intelligence agencies. AIC budgets quadrupled from
2000 and 2010 to reach AUD$1.07 billion (Richardson 2020a, p. 100). In the
three years between 2018–19 and 2021–22 the combined publicly available
budget of NIC agencies has grown by AUD$1.5 billion to AUD$7.1 billion,
and staffing grew by 1,000 positions to 25,000 – noting this budget is for the
agencies as a whole not just their intelligence functions.
3
The budget for the
six AIC agencies alone (excluding the NIC additions) grew by AUD$400
million from 2018–19 to 2021–22, and 1,000 staff positions were added.
4
NIC
agencies also share a joint capability fund, which NIC member agencies pay
into and can apply for larger funding to improve overall NIC capability,
supporting gaps in technological innovation, training and other workforce
developments (Walsh 2021). Figure 0.1 shows the agencies and their primary
functions within the NIC.
Figure 0.1 NIC Agencies (ONI 2017)

Introduction 9
Intelligence Principles and Disciplines
In Australia, each NIC agency has a critical, distinct and enduring function
(Department of the Prime Minister and Cabinet 2017). More detail about
each of the agencies and their intelligence disciplines – as well as similar
agencies in the United Kingdom and United States of America – are listed in
Appendix A. How emerging technologies impact their activities is specificto
the legal framework, mission, purpose, and technological maturity of each
agency, as well as the kinds of intelligence work they do. Despite these dif-
ferent perspectives, they have a shared interest in improving their capability
to collect, analyse and disseminate information.
Australia has made several deliberate, principled choices to manage the
powers and activities of the NIC agencies (Richardson 2020a, p. 165). These
principles have been considered over time and include, among others, the
separations between security intelligence and law enforcement, and intelli-
gence collection and assessment; and the distinctions between foreign and
security intelligence, onshore and offshore operations, and Australians and
non-Australians (Richardson 2020a, p. 165). These distinctions have been
long discussed and, arguably, blurred – with some exceptions and assistance
between functions – but ultimately upheld.
The three most significant distinctions in the context of emerging technologies
are set out here. One of the most important distinctions concerns the jurisdiction
in which intelligence collection or action takes place. Outside of exception by
ministerial authorisation, the distinction between domestic and foreign intelligence
collection is clear in the AIC agencies. This distinction is not as straightforward
with the agencies added for the NIC, because a number have domestic and foreign
missions, they are not intelligence collectors and their activities not jurisdictionally
bound. The second distinction is how agencies are legislatively required to manage
privacy. Three NIC agencies – Home Affairs, AFP and AUSTRAC – are bound
to the Australian Privacy Principles of The Privacy Act 1988, which governs the
way each agency collects, stores, uses and discloses personal information
(Richardson 2020b, p. 22). The other seven agencies in the NIC are exempt from
The Privacy Act 1988 completely (Richardson 2020b, p. 22).
A third distinction is the ways information can be obtained and what it
contains. Appendix B outlines the various disciplines, ‘types’ or means of
intelligence collection (Lowenthal 2012). Collection can refer to collection
agencies, or the activity of intelligence collection (Lowenthal 2012).
5
Outside
of one agency – ASIO – intelligence gathering (collection) and intelligence
assessment functions take place in separate AIC agencies to compartmenta-
lise intelligence. For example, DIO relies on intelligence gathered by ASD
and others to inform its assessments (Hope Royal Commission on Intelli-
gence and Security 1974–77). However, the four NIC agencies do not fit into
this collection and assessment framework. As agencies are not directly
named in interview data, types of intelligence, in Appendix B, are an
important way to understand the activities of the NIC agencies.

10 Introduction
The Study
This research advances our understanding of the impacts of big data on
intelligence agencies and national security in Australia. The principal aim of
the book is to explore the impacts of big data for intelligence production and
decision-making in national security. In doing so it sets out the impacts of
big data for knowledge (the information and data needed for intelligence),
intelligence activities and the organisation of intelligence communities. It
demonstrates that big data has pronounced impacts on many aspects of
national security, and our conception of what it includes, but is especially
significant for the knowledge, activities and organisation of intelligence
agencies.
The overall aim of the book is to map broad themes relating to transfor-
mations in intelligence agencies and the national security environment. First,
it considers very broad impacts on the national security environment and the
national security threats posed by big data. Second, it moves to examine
more specific impacts for intelligence agencies and the production of intelli-
gence. Third, it explores large themes present in society but with specific
impacts for intelligence, including privacy, ethics and trust. A thread running
through the book is the change that big data brings and its potential to
transform the intelligence community and national security environment.
Interviews: Approach, Participant Selection and Considerations
For an emerging technology trend like big data, where research is limited,
semi-structured interviews provide the most appropriate data collection
method to access primary source data from national security agencies and
personnel. They are ideal when little is known about a phenomenon (Gray
2009; Saunders, Lewis & Thornhill 2007; Whelan & Molnar 2018; Yin 2013)
and act as a means of developing an understanding of social phenomena in
their natural setting. They have been successfully applied to the national
security, intelligence and policing fields where it can be difficult to access
primary source data.
6
Forty-seven participants from across all NIC agencies – as well as five
independent subject matter experts – participated in semi-structured inter-
views. Interview questions followed semi-structured interview protocols, such
as including a list of questions that were posed to all interviewees. All parti-
cipants were asked to briefly outline their background and then answer a
mixture of common questions, and additional questions that came up
organically.
7
Semi-structured interviews allow for a grounded theory approach which
‘aims to make patterns visible and understandable’ (Charmaz 2014, p. 89).
Grounded theory begins with inductive data; it involves ‘going back and
forth between data and analysis, uses comparative methods and keeps you
interacting and involved with your data and emerging analysis’ (Charmaz

Introduction 11
2014, p. 1). Through coding, the researcher defines what is happening in the
data and begins to grapple with what it means — developing an emergent
theory to explain the data (Charmaz 2014, p. 113).
8
Interviewee selection used a purposive sampling design, meaning the pri-
mary focus was to obtain a rich set of data rather than a representative
sample (De Vaus 2014). Participants were identified using snowball sampling,
where the researcher accesses interviewees suggested by other interviewees
and informal networks (Noy 2008). This process varied by agency. In some
cases, agency heads were interviewed first, and after approval, additional
participants were approached separately. In other cases, agency heads dele-
gated the process to a suitable point of contact and suggested suitable inter-
view participants. In other agencies, informal networks of the researcher, or
the D2DCRC
9
were used. In practice, it essentially became an availability
sample (De Vaus 2014) as subjects ‘self-selected’ or ‘opted-in’ to the research.
Interviews were conducted within all ten National Intelligence Community
agencies as well as the oversight body, IGIS. The research involved 47 inter-
viewees, comprising 40 individual interviews and two small groups (one of
four and one of three), identifying as either independent subject matter
experts (ISMEs) or within government agencies as senior decision-makers
(SDMs), operational decision-makers (ODM) or technologists (TECH). The
breakdown can be seen in Table 0.1.
Prior to all interviews, organisational consent was received, and the inter-
viewees were provided with a plain language statement (PLS) and individual
consent form to ensure involvement was voluntary. After the interviews, the
audio was transcribed and provided to participants or agencies for their
approval to ensure against the small possibility that classified or sensitive
material may have been inadvertently disclosed. Minor amendments were
Table 0.1 Categories of Interviewees
Category Number of interviewees
Senior decision-maker (SDM) 20
Heads, deputy heads of agency and agency head
delegates.
10
Operational decision-maker (ODM) 10
Typically, mid-management level employees responsible for
leading operational decision-making and activities with
small or large teams.
11
Technologist (TECH) 12
Those with a technology background.
Independent subject matter expert (ISME) 5
Those with decades of experience in intelligence, national
security fields and academia.
12

12 Introduction
made in many of the transcripts, predominantly to improve the overall flow
of the text, clarify ambiguous points or remove specific references to organi-
sational structure or proprietary technologies. These transcripts were then
entered into QSR NVivo 12 for analysis.
13
Ethics & limitations
This study received ethics approval from Deakin University’s Faculty of Arts
and Education Ethics Advisory Group and was assessed as ‘negligible risk’.
14
As a researcher with experience in the field, it is possible this impacted the
author’s access to participants. It is possible that being perceived as an insi-
der within the broader national security community contributed to this
access. It certainly affected the author’s approach to the research and their
perspective. However, author’s real or perceived ‘insider’ understanding and
status enables them to articulate the impact of big data for intelligence
agencies in a manner only possible with an emic understanding of a culture
(Given 2008; Pike 2015).
This research does face limitations. First, due to the purposive sampling
design, the views of participants are not necessarily representative of the NIC
community. Second, their understanding of key questions and terms, such as
‘big data’, could vary. However, the interview process mitigated this by
asking participants how they understood the term and then providing a clear
definition spelling out which technologies were included. Finally, the findings
of this research may also not be generalisable to other countries – although,
the key themes it explores are both relevant and present in other democratic
nations, and it is highly likely that aspects of this research will be relevant
and transferrable to similar democracies.
Book Outline
The book shows that big data fuels emerging technologies and is transform-
ing intelligence production specifically as well as changing the national
security environment broadly, including what is considered a part of national
security and the relationships intelligence agencies have with the Australian
people. The book highlights some of the current and future transformational
changes associated with big data in society writ large that have implications
for the intelligence community.
Chapter 1 establishes the big data landscape and shows how it fuels
emerging technologies. It shows that big data has created a new landscape
comprising data abundance, digital connectivity and ubiquitous technology.
This chapter argues that the features of big data need to be considered
together as a landscape to fully understand the impacts on intelligence pro-
duction and national security. In examining each of these features, this
chapter shows how they individually and collectively as a landscape impact
intelligence activity, operations and the community. It then shows how this

Introduction 13
new big data landscape is concentrating information, computation and eco-
nomic power and that this has the potential to challenge ideas of nation-state
security.
Chapter 2 shows how big data challenges some of the longstanding and
foundational principles and practices of intelligence. First, the changing practice
of secrecy in intelligence work and activities. Second, the way the big data
landscape impacts understandings of geographical jurisdiction, affecting the
distinction of between operations that occur onshore and offshore, as well as
what constitutes nationality in the context of data. Third, how emerging tech-
nologies are complicating intelligence as well as challenging the national security
approach to innovation and the way in which intelligence agencies adopt tech-
nologies. Fourth, big data challenges fundamental principles of intelligence sto-
rage and compartmentalisation which agencies rely on to reduce security risks.
This will be further challenged by new approaches to technology. Fifth, the big
data landscape has created national decision-makers outside of government.
Finally, it shows that the big data landscape has exponentially increased security
vulnerabilities and directly challenges existing methods of assessing social harms
and national security threats.
Chapter 3 outlines new social harms and national security threats created
by the big data landscape. First, this chapter charts the impacts of the rapid
growth in data and analytics and shows how it is making these capabilities
accessible to new actors. It shows that big data ‘democratises’ intelligence
capabilities, making intrusive digital surveillance, profiling, influence, mon-
itoring, tracking and targeting capabilities available to a wider range of
actors (state and non-state). Second, it shows how this is democratising sur-
veillance and creating new vulnerabilities for privacy intrusion. Third, it
highlights the capability for asymmetrical information dominance, enabling a
strategic advantage. It explores how disinformation and misinformation are
challenging intelligence. Fourth, it reveals how big data drives disinformation
and misinformation. Finally, it examines how the big data landscape enables
information warfare as well as social and political harm.
Chapter 4 examines the impact of the big data landscape on intelligence
production. It outlines the impacts on the knowledge, activities and organi-
sation of intelligence agencies. This chapter shows how big data is changing
intelligence as knowledge, including changes to the kinds of knowledge used
for intelligence and gaps in knowledge used for intelligence, requiring a
stronger focus on the purpose of intelligence. This section demonstrates how
big data is changing where the knowledge and data used for intelligence
come from and how knowledge for intelligence is received, digested and
understood. This chapter then demonstrates the impact of big data on intel-
ligence as an activity, showing changes to the intelligence cycle broadly and
specifically highlighting three areas that participants articulated as the most
pressing or of the highest priority (collection and analysis as well as data
sharing and communication of intelligence). Finally, it examines the impact

14 Introduction
of big data on intelligence as an organisation, including digital transformation
and a change to the traditional models of intelligence analysis.
Chapter 5 analyses the impacts of big data on data privacy. The big data
landscape has and continues to radically transform privacy across society.
First, it builds on the extensive literature evidencing that big data is changing
privacy norms globally and the perception that in Australia there is a need to
rethink the privacy principles underpinning privacy laws. It looks at the way
in which big data has changed social conceptions of privacy and challenges
the Australian legislative framework for privacy and why this is important
for intelligence agencies. This chapter argues the impact of big data on priv-
acy –and privacy regulation –in society at large has potential future impli-
cations for the intelligence community. Second, this chapter shows how
privacy is temporal and the impact of ‘anonymisation’and aggregation of
data. Chiefly that an abundance of data and the capacity to identify, link and
use data quickly have created the potential for privacy intrusion remote from
the individual, in less visible ways and at any point in the future. The vast-
ness of data collectors, sellers and users has led to complex and confusing
privacy landscape. Lastly, this chapter shows that intelligence agencies are
differently affected by shifts in privacy. However, this research suggests that
currently the direct impacts of big data on privacy in intelligence agencies are
limited and predominately dependent on an agency’s role and legislative
mandate, affecting some agencies more than others. Participants highlighted
that the impact of big data on privacy is characterised by one significant
distinction among the AIC collection agencies –that is, whether the agency
has a foreign or domestic mandate. Big data is changing how some agencies
collect, store and analyse data, particularly those subject to a legislative
requirement to determine whether the data relates to an individual who is
Australian.
Chapter 6 examines how the big data landscape impacts ethics in intelli-
gence. It reveals how the big data landscape is changing established ethical
boundaries of intelligence, including where big data will not improve intelli-
gence activities. According to participants, there are aspects of intelligence
where big data and automation will not ever be useful and other situations
where more testing and refinement is needed before such systems are intro-
duced. This chapter highlights ethical dilemmas of big data in intelligence
that have not previously been studied. First, ‘ethics at scale’–that some of
the decisions around ethics are being automated and applied at scale in social
contexts by private companies, which would represent a considerable ethical
dilemma if applied to intelligence activities. Second, ethics in intelligence
includes considering bias. This chapter indicates that intelligence practi-
tioners should be aware of the difference between cognitive bias and data
bias as well as the intelligence challenges of incomplete data sets and the bias
of intelligence collection itself.
Chapter 7 shows how the big data landscape is changing public perception
of trust, transparency, and the legitimacy of intelligence agency operations.

Introduction 15
Interviewees reflect on their relationships with the public and how big data
has and will impact that relationship. Emerging strongly from the data was a
sense that trust is significant in the role of national security agencies in
Australia. Participants indicated that they saw big data and the information
ecosystem it enables as changing the relationships between intelligence
agencies and the public. Furthermore, this chapter argues that big data
impacts trust in the entire system of government and public service agencies
as it is reliant on trust in the way data is collected and used across all
government agencies, not just the national security sector. This chapter
proposes that big data is changing the public’s perceptions of the intelligence
community around trust, transparency and the legitimacy of intelligence
agency operations. It unpacks how participants understand trust and the key
concepts of trust, legitimacy and the social contract, which each emerging
from the interview data. This chapter shows that participants perceive that
how trust is built and developed is impacted by big data, with participants
suggesting intelligence agencies need to align big data use with agency values
and purpose, transparency and public engagement.
The Conclusion reflects on the findings throughout the book and highlights
some of the implications for policy and limitations of as well as areas for future
research. The book reveals how the big data landscape is transforming what
intelligence is, how it is practised, and the relationships intelligence organisations
have with society and with each other. It shows that big data has impacts on
many aspects of national security, including our conception of what it constitutes.
The impact of big data is especially significant for the knowledge, activities and
organisation of intelligence agencies. The book highlights specificimpacts for
intelligence agencies and the production of intelligence, and then examines how
intelligence agencies interact with each other and look out to the rest of society.
The book details how big data is impacting the relationship between intelligence
agencies and citizens, specifically in the areas of privacy, ethics and trust.
Notes
1 The NIC is comprised of the original Australian Intelligence Community (AIC)
agencies plus four new ones. The agencies in the AIC are the Office of National
Intelligence, Australian Security Intelligence Organisation, Australian Secret
Intelligence Service, Defence Intelligence Organisation, Australian Signals Direc-
torate and Australian Geospatial-Intelligence Organisation. The Home Affairs
Portfolio brings together Australia’s national and transport security, criminal jus-
tice, emergency management, multicultural affairs, and immigration and border-
related functions and agencies. Agencies within the Department of Home Affairs
include the Australian Criminal Intelligence Commission (ACIC) and the Aus-
tralian Transaction Reports and Analysis Centre (AUSTRAC). ACIC is included
in the NIC in its entirety, whereas the other new agencies in Home Affairs
(AUSTRAC and the Department of Home Affairs itself and the AFP) have only
the intelligence functions of their organisations included.
2 While this is a UK-specificdefinition, a similar definition from the US defines
national security as ‘the ability of national institutions to prevent adversaries from

16 Introduction
using force to harm Americans or their national interests and the confidence of
Americans in this ability’, from both the physical and psychological dimensions
(Sarkesian, Williams & Cimbala 2008, p. 4).
3 Richardson 2020a, p. 267; ACIC 2022; AFP 2022; ASD 2022; ASIO 2022;
AUSTRAC 2022; Department of the Prime Minister and Cabinet 2020; 2022;
Department of Home Affairs 2019; 2022. Note: These the budget and staffing
figures exclude ASIS, DIO and AGO, as their details are ‘not for publication’.
4 Richardson 2020a, p. 267; ACIC 2022; AFP 2022; ASD 2022; ASIO 2022; AUS-
TRAC 2022; Department of the Prime Minister and Cabinet 2020; 2022;
Department of Home Affairs 2019; 2022.
5 For a thorough outline of collection and collection disciplines see Lowenthal
(2012, pp. 71–118).
6 Examples include examinations of data science use in the United States’ Defense
Intelligence Agency (Knopp et al. 2016), the UK police’s use of data (Babuta
2017), and the impact of big data on the production of security in Australia
(Chan & Bennett Moses 2017), which fell short of specifically exploring big data’s
impact on intelligence production. Additional studies in intelligence, analysis and
national security also utilised qualitative interview methods (Chan & Bennett
Moses 2017; Chen et al. 2017; Coyne 2014; Ratcliffe 2012; Treverton & Gabbard
2008; Walsh 2011; Whelan 2014).
7 Common questions included: (i) What is your understanding of big data? (ii)
How does big data impact on your organisation? (iii) How would you describe the
current and future challenges and opportunities of big data?
8 Coding was conducted line by line (Charmaz 2014, pp. 124–127), followed by
focused coding to draw out larger concepts (Glaser & Strauss 1967, pp. 101–117).
The final stage of the analysis involved the in-built search and frequency query
functionality of QSR NVivo 12 to ensure no categories or data were missed.
9 Data 2 Decisions Cooperative Research Centre provided a scholarship to partially
fund this research.
10 SDMs were SES2 and above in the Australian Public Service context.
11 ODMs were mainly EL1, EL2 and SES1 in the Australian Public Service context.
12 The five ISMEs were Stephen Merchant PSM, Dennis Richardson AC, Clive
Lines, Ian McKenzie PSM and Dr Lesley Seebeck. In the data their comments are
de-identified as ISME.
13 QSR NVivo is a software designed to help researchers to gain richer insights from
qualitative and mixed-methods data. It stores and organises data as well as
helping researchers to categorise, analyse and visualise their data.
14 This involved submitting a ‘low risk’ application form, the PLS and consent form
as well as sample interview questions. The two ethical considerations of this study
were ensuring participant anonymity, and the security of the interview data – as a
result, all participants are anonymised, and the recordings and transcripts are
only accessible to the author.
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