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Trends & issues in crime and criminal jusce
ISSN 0817-8542
Understanding law
enforcement information
sharing for criminal
intelligence purposes
Rick Brown
Criminal intelligence has become a central tool to support
modern crime control (Ratclie 2016; Rickards 2016). Law
enforcement eorts have focused on improving criminal
intelligence to understand the nature and extent of criminality
and ulmately to prevent, detect and disrupt crime. Such
criminal intelligence relies on the building blocks of raw data and
informaon generated internally by a law enforcement agency
or shared by a partner. In a world characterised by increasingly
sophiscated crime, it is increasingly important to draw together
the fragments of informaon available from mulple sources.
As Bigo (2000: 84) has noted: ‘A large amount of police work
now concerns patrolling the data in order to spot connecons
between criminals, and much of this goes beyond the merely
local or even naonal.’ This might include ‘patrolling’ informaon
from (among others) law enforcement agencies, intelligence
agencies, government departments, nancial instuons and
telecommunicaons providers, each of which may provide a
dierent insight into the acvies of a criminal group.
Abstract | Criminal intelligence has
become a central tool in modern
crime control that is used by law
enforcement to understand rapid
changes in crime and criminality. The
development of intelligence relies on
the sharing of informaon between
agencies—including law enforcement,
other government agencies and
private sector enes.
However, there are a number of
legislave, technical and cultural
barriers that limit the free ow of
informaon. This report examines
those barriers and develops an
informaon sharing matrix that
explains the barriers associated with
dierent types of informaon sharing.
The matrix develops a typology of
informaon sharing based on two
dimensions: the level of interacon
and the level of connecvity.
No. 566 December 2018
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No. 566 December 2018
This paper explores some of the ways in which informaon is shared between law enforcement
agencies to help create aconable criminal intelligence. It addresses the benets of and barriers
to eecve informaon sharing and outlines a way of assessing the maturity of informaon
sharing arrangements.
Terminology
As a starng point, it is important to dene what is meant by informaon and intelligence and
to clarify how the two are dierent. Although there is no naonally agreed denion of criminal
intelligence in Australia, it is dened here as ‘informaon that is collected about crime and criminals
and evaluated, analysed and disseminated’ (Parliamentary Joint Commiee on Law Enforcement
2013: 5). Informaon is the raw material used to create intelligence. This can come in many forms
including unprocessed data (eg telecommunicaons metadata), details of criminal records, or
informaon reports entered into a law enforcement intelligence database (eg a sighng of an
individual at a parcular locaon). Informaon may also include material processed by another law
enforcement agency to produce a knowledge product that provides further understanding about a
parcular type of criminality or criminal group. Informaon, oen from mulple sources, is collated
and analysed to provide intelligence, which is typically aconable and intended to inuence decision
making about how to tackle crime or criminals. While Ratclie (2016) describes data, informaon,
knowledge and intelligence as being on a connuum, the rst three are characterised here as building
blocks for the laer.
In this paper, informaon sharing is discussed in the context of law enforcement. It may also include
sharing with those working in a naonal security or private sector seng, but this is not an essenal
requirement. Further, informaon may be shared without an immediate intent but rather for some
future unknown requirement, it may be shared in support of a specic operaon or invesgaon, or
it may be shared in response to an urgent request.
Benets of informaon sharing
Sharing informaon to improve criminal intelligence serves rst and foremost to increase knowledge
available for decision making. This can help not only to idenfy which criminal groups to target,
but also, in an age of resource constraints, to triage out lower risk cases (Ratclie 2016). It can also
help to prevent ‘linkage blindness’, where informaon available in one jurisdicon is not available in
other jurisdicons. This can result in the full extent of criminality that transcends law enforcement
boundaries remaining hidden (Jackson & Brown 2007; Sheptycki 2004). Linkage blindness was a
problem idened by the 9/11 Commission in the United States, which found that a number of
intelligence agencies held informaon on the acvies of those involved but failed to connect
the dots (Naonal Commission on Terrorist Aacks Upon the United States 2004). Similarly, in the
United Kingdom the Bichard inquiry into the murder of two children by a school caretaker idened
failings in intelligence sharing between police forces and child protecon services resulng in
linkage blindness. The report of the inquiry recommended the implementaon of a naonal police
intelligence system as a maer of urgency (Bichard 2004). In each case, improved informaon sharing
between jurisdicons could have resulted in an enhanced intelligence picture.
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Informaon sharing also helps to support deconicon (Mapel 2014; Police Foundaon 2016), which
aims to prevent what might be considered the opposite of linkage blindness—when more than one
law enforcement agency is simultaneously invesgang the same individual or group. Informaon
sharing under these circumstances can result in savings in me and resources for each agency
concerned by prevenng duplicaon of eort (Parliamentary Joint Commiee on Law Enforcement
2013). This is pernent in Australia, where criminal intelligence on an enty may be held in mulple
databases. A 2013 parliamentary inquiry into Australian criminal intelligence found that such
informaon might be held on more than 30 separate systems operated by mulple law enforcement,
policing and government agencies (Parliamentary Joint Commiee on Law Enforcement 2013).
Law enforcement informaon sharing can also result in greater outcomes than would be possible for
a single agency working alone. For example, in the context of drug law enforcement invesgaons
insgated by the US Drug Enforcement Administraon, Lemieux (2010) found a posive correlaon
between the number of police agencies involved and the average size of a drug seizure. Further,
invesgaons involving mulple overseas law enforcement agencies tended to result in signicantly
more arrests and greater seizures than solo invesgaons.
Risks of informaon sharing
While there are clear benets to informaon sharing, there are also risks. Maintaining and
transming informaon securely is a signicant task for law enforcement agencies. It requires
trusng that recipient agencies handle and share their informaon securely. This means that those
operang within an informaon sharing network are expected to abide by a common set of security
standards governing the storage and disseminaon of informaon. Sheptycki (2017) has noted that
this may demarcate ‘insiders’—those with whom the police will share informaon—from ‘outsiders’,
with whom they will not. This can be parcularly pernent when dealing with some foreign
jurisdicons, where trust may be eroded by high levels of public sector corrupon.
Yet even within a trusted environment there can be informaon leakage from internal sources (eg
corrupt employees), or external sources (eg data the resulng from hacking). This can reduce
the level of trust that other members of the network have in those subject to leaks, which in turn
may impede informaon ows. As a result, there is a constant trade-o between the need to share
informaon and the need to ensure that, when it is shared, it is held securely.
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Barriers to eecve informaon sharing
While signicant benets can be derived from informaon sharing, there remain a number of barriers
that prevent informaon from being shared with others who might use it to generate intelligence.
These can be divided into legislave, cultural and technological barriers.
Legislave barriers
Across Australia, there is no agreed legal denion of criminal intelligence among the eight states
and territories and the Commonwealth (Parliamentary Joint Commiee on Law Enforcement 2013).
Rickards (2016) noted that each Australian state and territory has its own legislave framework
governing informaon sharing between agencies, with a further set of frameworks to negoate at
the Commonwealth level. One example of Commonwealth informaon sharing restricons can be
found in secon 59 of the Australian Crime Commission Act 2002, which places strict limits on whom
the Australian Criminal Intelligence Commission (ACIC) can share informaon with, due to concerns
over the security and privacy of that informaon. For example, telecommunicaons intercept
material can only be shared with a small number of specied enes. Further, telecommunicaons
companies may provide details of the same subscriber’s phone records to mulple law enforcement
agencies under separate disseminaon processes due to dierent laws in each state/territory
and the Commonwealth. Each agency receiving the informaon may be unaware that others
have also requested and received it under their own legal powers, thereby creang ineciencies
(Parliamentary Joint Commiee on Law Enforcement 2013).
In the context of terrorist events, the inquest in the wake of the Lindt Café siege raised quesons
about the limits placed on informaon sharing by privacy legislaon and recommended a review of
informaon sharing arrangements between agencies during terrorist events (State Coroner of New
South Wales 2017).
Cultural barriers
Creang a culture of informaon sharing has previously been idened as a crical success factor
in implemenng an Australian criminal intelligence management strategy (Commonwealth of
Australia 2017). Here, ‘culture’ is dened as the rituals, values and behaviours (Schein 2017) of the
overall criminal intelligence community (rather than its constuent parts) that give rise to the overall
framework that shapes the willingness of individual enes to share informaon. The literature on
this issue has idened signicant cultural barriers that can inhibit the ow of informaon between
agencies. Some of these relate to the organisaonal structures that shape the operang environment,
such as by creang silos. Organisaons, and indeed secons or teams within organisaons, oen
gain specialist knowledge about parcular crime problems or criminal groups as a result of invesng
resources or using designated legal powers to target issues of concern to the agency. There can
be a tendency to focus internally on building intelligence holdings, rather than cooperang with
other agencies (Sales 2010). This can create a network of siloed intelligence holdings (also known
as ‘stovepipes’) and reduce opportunies for informaon from mulple sources to be synthesised
(Occhipin 2010).
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Many cultural atudes to informaon sharing are a result of relaonal properes that determine
how agencies interact (Whelan 2016). These include:
• ego—an agency can develop an ego that seeks to control. Intelligence can act like a currency,
with power derived from sharing, trading or withholding informaon. Organisaons may be
inhibited from sharing informaon if they believe there will be addional gain from holding onto
that informaon (Carter & Carter 2009). Ego can also lead to conict between law enforcement
agencies over who should take the credit for successful outcomes in mul-jurisdiconal
invesgaons (Guille 2010; Lemieux 2010);
• compeon—organisaonal concern with sharing credit may extend to direct compeon with
other law enforcement agencies over achieving posive outcomes. If sharing informaon is
expected to enhance the reputaon of another agency, which could ulmately result in addional
resources for that agency, a decision may be made to withhold the informaon (Aviram & Tor
2003; Jackson & Brown 2007; Sales 2010);
• ltering—agencies may lter the informaon they provide to others, thereby providing only some
of that which is available (Jackson 2014);
• distrust—agencies may not provide informaon because they distrust the recipient agency. This
can be due to concerns over whether the informaon will be held securely, or how it will be used
(Monahan & Palmer 2009);
• mutuality—informaon sharing may be inhibited in circumstances where a degree of reciprocity is
expected. An agency may provide informaon on the expectaon that it will receive something in
return, whether that be informaon, intelligence or recognion of the contribuon made by that
agency to any subsequent successful outcome. Failure to reciprocate can lead to a ‘once bien,
twice shy’ mentality (Sales 2010);
• competence—informaon may not be shared because it raises embarrassing quesons about the
quality of the informaon, in terms of its accuracy, its meliness, or the method by which it was
obtained (Jackson & Brown 2007; Taylor & Russell 2012);
• need-to-know—there may be a culture of not sharing informaon widely because of a belief
in a need-to-know policy, as opposed to a need-to-share policy (Carter & Carter 2009; Naonal
Commission on Terrorist Aacks Upon the United States 2004). This means that an agency may
judge whether its informaon has ulity for another agency, rather than the recipient agency
having an opportunity to evaluate that informaon for itself; and
• culture of secrecy—associated with the need-to-know ethos, there may be a general fear of
sensive informaon that could do harm to society geng into the wrong hands, leading to a non-
sharing default posion (Connery 2016).
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Technological barriers
Technological problems have long plagued law enforcement agencies’ aempts to share informaon.
Incompable IT systems that inhibit the transfer of informaon between enes have posed
signicant problems (Jackson & Brown 2007; Monahan & Palmer 2009; Plecas et al. 2011; Rickards
2016). In Australia, each of the eight states and territories has its own IT infrastructure for managing
criminal intelligence, with systems that evolved at dierent mes, with dierent operang systems.
This has resulted in there being ‘…no single and complete “point-of-truth” for Australian criminal
intelligence holdings nor an automated process for searching across all such systems simultaneously’
(Parliamentary Joint Commiee on Law Enforcement 2013: 38).
Other technical issues can also inhibit sharing. For example, dierences in the way that names and
other details are captured can mean that links between systems are missed, or duplicate records
created when matching data between systems (Plecas et al. 2011).
Modes of informaon sharing
Despite the benets of informaon sharing noted earlier, it remains decepvely dicult to achieve as
a roune way of doing business. So what can be done to improve the way in which law enforcement
agencies share informaon in a secure and mutually benecial way? One small rst step to answering
this queson is to understand the dierent approaches to sharing informaon and to recognise
their inherent strengths and weaknesses. Indeed, there are mulple forms of sharing, some of
which are context dependent and not all of which achieve the same outcome. The remainder
of this paper therefore seeks to develop a typology of informaon sharing which can be used to
assess the sophiscaon of informaon sharing processes within a law enforcement environment.
Sophiscaon is determined here by the ease with which informaon is shared and the complexity of
establishing the informaon sharing system. This assumes there is a hierarchy of intelligence sharing
approaches with, by denion, some forms of sharing being superior to others. This is not to suggest
that all informaon sharing should seek to achieve the same standard, but rather to acknowledge
that decisions made about how informaon is shared shape subsequent approaches to working with
that informaon.
Figure 1 introduces a prototype informaon sharing matrix. This takes into account the type of
interacon and type of connecvity involved in the informaon sharing process. Movement along
either axis in the matrix represents an increase in the sophiscaon of informaon sharing. In this
context, ‘interacon’ relates to how the provider and the recipient of the informaon relate to each
other. This can range from no interacon beyond the transmission of the informaon through to
regular, direct interacon. Connecvity refers largely to the method of sharing.
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Figure 1: Informaon sharing matrix
Type of interacon
The inclusion of interacon in Figure 1 is recognion that informaon sharing is a means to an
end that is dened by the provision of beer intelligence. At some point, there needs to be a
degree of processing applied to the informaon before it can inform decision making. The term
‘interacon’ therefore situates informaon processing at the me the informaon is shared or at
some later point. In the informaon sharing matrix, interacons are described as either connected
or collaborave, although these should be considered as opposite ends of a connuum, rather than
discrete categories.
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Connected interacon
Connected interacons involve the delivery of intelligence from one law enforcement agency to
another. This may be associated with a ‘push’ approach to informaon sharing whereby one agency
passes informaon to another agency, or a ‘pull’ approach where informaon is requested (Jackson
2014). However, a key feature of connected interacons is limited communicaon between enes
beyond the sharing of informaon. This communicaon may include limited context about how or
why the informaon was collected or requested and there may be limited joint acvity between the
two enes beyond the informaon sharing. Examples include law enforcement ocers entering
informaon reports into a centralised intelligence database without knowing who might use such
informaon and for what purpose (Ratclie & Walden 2010). In fact, these interacons are likely to
constute the majority of intelligence sharing.
Collaborave interacon
Collaborave interacon refers to intelligence sharing that is characterised by greater reciprocity
among two or more agencies. Informaon may lead collaborang agencies to ask new quesons
that require addional intelligence gathering, thereby creang an iterave process of collecon
and analysis.
Type of connecvity
‘Connecvity’ refers to the way in which informaon is shared between enes. At its simplest,
informaon sharing involves an exchange between just two enes (bilateral connecvity). This
can be as simple as one law enforcement ocer phoning another to obtain a parcular piece
of informaon.
Bilateral connecvity
In cases of bilateral connecvity, the provision of informaon is oen narrowly dened between two
enes. This may be based on a request for specic informaon, or it may be unsolicited informaon
that is sent out to a receiving agency. Bilateral connecvity may also involve more person-to-person
transfer of informaon than other forms of connecvity. This may parcularly be the case where the
goal of the informaon sharing is to share knowledge and experience between individuals (Jackson
2014). Liaison ocers and mul-agency task forces both share informaon in a way that could
typically be described as bilateral connecvity.
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Liaison ocers are outposted, or seconded from one law enforcement agency to another. On an
internaonal scale, they may be co-located with a diplomac mission and are somemes given
diplomac status. They act as representaves for the home law enforcement agency and are the
conduit for informaon to ow between agencies. They operate by building networks of trusted
bilateral relaonships that are intended to be used when either the home or host agency requires
informaon, dialogue or support from the other agency. As such, they can act as ‘facilitator, broker,
negoator and trustor’ (den Boer 2010: 58). In some cases (as with Interpol and Europol), liaison
ocers will be located in units with liaison ocers from other countries. While this may create
mulple opportunies for collaboraon, those relaonships remain bilateral, between the home law
enforcement agency and the one with whom the liaison ocer is collaborang. Liaison ocers will
also oen avoid involvement in invesgaons, instead focusing on more strategic liaison acvies
(Bigo 2000).
On the informaon sharing matrix, liaison ocers will oen play a Connected-Bilateral role, but might
also be more collaborave in some instances. The benet of this approach is the relave simplicity of
implementaon, relying on individual ocers rather than technological soluons. The disadvantage
from an informaon sharing perspecve is that connecvity is constrained by the personal
relaonships between liaison ocers and their contacts. The ‘bandwidth’ for informaon sharing
transacons will be narrow for even the best of liaison ocers.
Task forces have been recognised as an eecve means of sharing informaon with other law
enforcement agencies (McGarrell & Schlegel 1993; Parliamentary Joint Commiee on Law
Enforcement 2013; Perras & Lemieux 2010; Rickards 2016). They typically involve ocers from
mulple law enforcement agencies seconded to a host agency to address a parcular form of
criminality. As seconded members, they are able to access the IT systems of their home agencies
and can legally share informaon with other members of the task force, thereby enabling a more
comprehensive understanding of criminality to be developed. This informaon sharing within a
task force seng is oen made possible through a formal memorandum of understanding between
the agency hosng the task force and the agencies seconding ocers.
Task forces dier from liaison ocers in that the former are typically focused on operaons and
invesgaons and work together over a limited me, while the targeted criminality remains a
concern. Such task forces will typically play a Collaborave-Bilateral role, although the nature of the
sharing may also push them towards a more centralised role. As with liaison ocers, task forces
are easy to implement and rely on the deployment of ocers. They also overcome some of the
cultural barriers to informaon sharing, parcularly those associated with compeon, distrust and
mutuality, as members of the task force will work towards a common goal. However, as with liaison
ocers, task forces typically share informaon among a small group of individuals, resulng in
siloing—those outside of the task force working on similar issues may be unaware of the informaon
gathered by the task force.
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Centralised connecvity
Centralised connecvity describes an informaon sharing structure in which mulple agencies
send informaon to a central storage facility where it can be combined and analysed. This analysis
may be undertaken by any one of the agencies providing the informaon, as is the case with the
ACIC’s Australian Criminal Intelligence Database (ACID), which holds criminal intelligence from all
jurisdicons in Australia (Parliamentary Joint Commiee on Law Enforcement 2013). Alternavely,
analysis may be undertaken solely by the central authority receiving the informaon, which
subsequently disseminates the derived intelligence products. This is a model oen used by US Fusion
Centers, which draw informaon from mulple sources to produce intelligence with the aim of
prevenng terrorism (Chermak et al. 2013; Monahan & Palmer 2009). Data hubs of this kind, which
allow for informaon to be provided but not used by those providing it, can best be described as
a Connected-Centralised model (Figure 1). In contrast, databases such as the ACIC’s ACID can be
described as a Collaborave-Centralised model in that several agencies may provide informaon, the
informaon is visible to all and agencies can work together.
A benet of centralised connecvity is that, by joining together data from mulple sources, it may
help to address the linkage blindness that can result from siloing. Centralised connecvity can be
achieved through technological soluons that automate data capture and collaon (Plecas et al.
2011). However, these automated processes can be burdensome if they share so much data that
it cannot be analysed (Jackson 2014), although this can also be exaggerated and used as an excuse
for not sharing (Markle Foundaon 2003). Centralised systems can also be cricised as being ‘black
holes’ that suck in data but return lile intelligence to the agencies providing the original informaon.
Networked connecvity
A third form of connecvity involves networked arrangements in which each law enforcement
agency in the network stores its own informaon but allows other agencies in the network to access
its holdings, essenally creang a ‘free market’ in informaon exchange between law enforcement
agencies (Anderson 1989). This approach could involve any agency in the network accessing
data holdings of each of the other agencies to view informaon pernent to its own operaonal
requirements. This could be a passive form of interacon, as described by a Connected-Networked
model, in which the agency uses informaon made available by others but produces its own
intelligence products without the acve involvement of others in the network. Alternavely, it could
be a more acve form of interacon, as described by a Collaborave-Networked model, in which
access to devolved data may subsequently lead to further engagement from network partners. This is
the model envisaged by the Markle Foundaon (2002) in support of US naonal security in the wake
of 9/11.
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In pracce, law enforcement informaon sharing relying on fully networked connecvity is rare,
although the ACIC’s planned Naonal Criminal Intelligence System appears to be a hybrid between a
centralised and networked system. The system would give all law enforcement jurisdicons a search
capacity but would rely on data being sent by each jurisdicon to a secure cloud (Hendry 2017). The
benets of such systems are that they potenally provide real-me access to informaon across
jurisdicons, thereby prevenng siloing and the problems associated with mulple jurisdicons
following the same lines of inquiry. However, such approaches are likely to be dicult to implement
because of both the legislaon and the technology required. From a cultural perspecve, they may
also require a rebalancing of control over informaon, away from the centre of the network and
towards those working at the edge—oen in operaonal roles (Alberts & Hayes 2003). The focus
therefore shis from how to collect and store informaon to how to use it.
Comparing connecvity
The key dierence between the three types of connecvity is how the informaon is accessed by the
user. In the case of bilateral arrangements, the informaon will be provided on request by the owner
of the informaon—described as retail sharing by Sales (2010). In the case of centralised systems, the
supplier of the data will typically deposit the data in advance for use by the end user. The informaon
supplied may be a set of data agreed with the central agency (warehouse sharing), or informaon
may be provided in the expectaon that it will be of use to others, known as volunteer sharing (Sales
2010). For networked conguraons, the user will extract the informaon directly from the other
agencies’ systems. While access controls exist at each level, these will typically be more signicant in
bilateral arrangements than in networked arrangements, although in each case decisions are made
about what informaon to disclose.
In general, informaon sharing varies from the least sophiscated, in the boom le segment of
Figure 1 (the Connected-Bilateral model), through to the most sophiscated in the top right segment
(Collaborave-Networked). It should be noted, however, that the choice of informaon sharing
model is context dependent. For an immigraon ocer who needs further informaon on a traveller
agged as being of interest, a call to a contact in the country of origin (the Connected-Bilateral
model) may be the most ecient method of obtaining informaon. In contrast, a Collaborave-
Networked model may be more desirable when building mul-jurisdiconal regional, naonal or
internaonal informaon sharing arrangements where large volumes of data are used to produce
mely intelligence products. Therefore, the most appropriate level of sophiscaon will depend on
the desired outcome of sharing the informaon.
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Discussion and conclusions
Recognising the desired end-state for an informaon sharing arrangement will help to idenfy
the legislave, technical and cultural barriers that need to be addressed to achieve success. These
barriers will vary according to the types of interacon and connecvity involved. For example, in the
case of an outposted liaison ocer playing a Connected-Bilateral role, technical barriers are likely to
be minimal, extending no further than the need for remote access to the home agency’s systems.
Depending on with whom the informaon is shared, legislave barriers may also be minimal if the
sharing is covered by the organisaon’s exisng legislaon. In contrast, cultural barriers may be
signicant where the informaon sharing is rst dependent on building a relaonship of trust and
reciprocity.
At the other end of the spectrum, agencies that aspire to Collaborave-Networked informaon
sharing are likely to experience legislave, technical and cultural barriers. The ability to freely share
informaon within the network may be restricted by the legislaon governing each agency in the
network. Dierent agencies may have dierent laws specifying how informaon can be disseminated
and to whom, requiring legislave changes to be negoated on a case-by-case basis. One advantage
of this approach, however, is that the failure of one jurisdicon to change the necessary legislaon
does not preclude others in the network from sharing. Technical issues will focus on how nodes in the
network can be connected, while cultural issues will focus on how to get law enforcement agencies to
work together more interacvely, once the informaon is made available.
Concluding remarks
This paper has demonstrated the benets of law enforcement informaon sharing for criminal
intelligence purposes, yet many hurdles to improving informaon ows remain. One small step
towards addressing the legal, technical and cultural barriers to informaon sharing is to understand
the nature of the informaon sharing mechanism, as dened by its level of interacon and
connecvity. This helps to understand on the one hand the restricons imposed by the informaon
sharing context, and on the other hand the increasingly complex issues that are likely to be
experienced as the level of sophiscaon grows.
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