Policymakers recognize that the intelligence system in place before September 11 failed to get the right information to the right people at the right time. This included other federal agencies as well as state and local authorities.
To date, administration and congressional efforts to reorganize national security intelligence have focused mainly on reducing barriers to sharing information among federal agencies, improving federal information technology capabilities, coordinating analysis of federal and local law enforcement and intelligence data, and supporting state and local emergency communication.
At the borders, customs officials have enhanced cargo screening using radiation detectors and x-ray scanners and immigration authorities have upgraded checks on foreign visitors using improved databases. Around the country, newly expanded joint terrorism task forces bring together federal and local law enforcement officials.
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Federal airport security officers conduct more rigorous screening of passengers under the terms of the Aviation and Transportation Security Act, enacted two months after the attacks. A new Terrorist Threat Integration Center, under the supervision of the director of central intelligence, is charged with synthesizing counterterrorism intelligence from all sources.
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Some of the changes have created serious concerns about potential conflicts between national security measures and principles of personal privacy and government openness. The Defense Advanced Projects Administration DARPA , which sponsored research into data mining and pattern recognition technologies under its Terrorist Information Awareness formally Total Information Awareness program, was temporarily halted by Congress because the sponsors failed to address potential privacy concerns.
Similar concerns have arisen about conflicts with government openness, especially when secrecy has been expanded without public debate. Rejecting a bipartisan compromise, the administration supported a broad new exemption to FOIA in the Homeland Security Act for information voluntarily provided by businesses to the government about infrastructure vulnerabilities that might cause massive casualties or disruptions in a terrorist attack. Many of these actions were couched as emergency measures—extraordinary steps to counter extraordinary threats. However, nearly two years after September 11, it is clear that they represent important building blocks for a new generation of intelligence policy.
More security issues that affect openness and privacy will be decided in the coming months. Congress has promised to revisit the broad and controversial requirement in the Homeland Security Act that allows the government to withhold information about infrastructure vulnerabilities, and key components of the PATRIOT Act expire in Defending against terrorism threats will require policymakers to replace the formal, hierarchical intelligence structure with a horizontal, cooperative, and fluid architecture that gets information from those who have it to those who need it through the development of virtual communities of information sources, analysts, and users.
Advances in information technology can facilitate this transformation.
Internet and teleconferencing technologies allow virtual communities to gather and share information in real time. Instead of focusing on central control, federal officials should spend more time setting priorities, coordinating communication, supplying technical assistance, and assuring data quality. Collecting more information from more sources will require more federal analytical capability to prevent information overload.
The first step in designing an intelligence system to fight terrorism while protecting openness and privacy is to understand what information is needed to support each homeland security challenge. To detect potential terrorist threats within the United States, we need to enhance traditional investigative techniques by cross-referencing databases such as airline reservation records, phone logs, and credit histories with government law enforcement, immigration, and intelligence information.
To protect critical infrastructure in areas such as agriculture, food, water, public health, emergency services, telecommunications, energy, transportation, banking, and finance, we need to map vulnerabilities against capabilities of potential terrorists, people who have access to those infrastructures, and the means available to carry out effective attacks. To respond to emergencies, we need two-way communication in real time between first responders and other officials about the extent and nature of the attack, the resources available to respond, and the risk of further terrorist action.
The long-term acceptance by the American people of an enhanced intelligence effort will depend heavily on the adoption of clear, public guidelines governing the collection, retention, and dissemination of information, and the development of strong procedures for oversight and accountability. Modern information technology can play an important role in helping to implement and enforce these policies. In principle, no one disputes that anti-terrorism measures should protect the values that anchor democratic processes and personal security in the United States.
In practice, however, policymakers must make difficult choices. Guidelines to promote security while furthering openness and privacy should be a matter of public debate and will need mid-course corrections as policymakers and analysts gain experience with new information practices and technologies. The recommendations that follow provide a framework for beginning such a deliberative process. Emphasize information sharing. Openness can further security. Quickly identifying terrorist threats and infrastructure vulnerabilities calls for cooperative, fluid information networks.
Reducing barriers to information-sharing rather than compartmentalizing secrets represents the greatest challenge in fostering such networks. State and local governments, the private sector, and the public have a central role to play in identifying suspicious activities and individuals and in finding and correcting security vulnerabilities.
Sharing information about threats and infrastructure vulnerabilities enhances security by multiplying sources of information, empowering Americans to make their own choices about what risks they are prepared to accept, and creating market incentives and political pressures to reduce vulnerabilities. On the other hand, security or commercial interests will sometimes override a presumption of openness. There may be little public benefit and considerable security risk in revealing floor plans of nuclear power plants or exact locations of military weapons or vaccine stockpiles, for example.
In addition, trade secrets, which provide an underpinning for competitive enterprise, should continue to receive careful protection under federal laws.
An analogous situation occurs in the field of computer security, where software companies grapple with the question of whether to alert customers about vulnerabilities that hackers can exploit. Proponents of secrecy argue that revealing security weaknesses invites exploitation of those weaknesses. Proponents of openness argue that public knowledge helps spur solutions and provides users with information to guard against breaches.
There is growing support for the idea of making programming code more accessible as a way to enhance overall security. At the same time, intelligence analyses and assessments have become a vital component of modern approaches in policing, with policy implications for crime prevention, especially in the fight against organized crime. In this study, we have a unique opportunity to examine one specific Swedish street gang with three different datasets.
These datasets are the most common information sources in studies of criminal networks: intelligence, surveillance and co-offending data. We use the data sources to build networks, and compare them by computing distance, centrality, and clustering measures. This study shows the complexity factor by which different data sources about the same object of study have a fundamental impact on the results.
The same individuals have different importance ranking depending on the dataset and measure. Consequently, the data source plays a vital role in grasping the complexity of the phenomenon under study. Researchers, policy makers, and practitioners should therefore pay greater attention to the biases affecting the sources of the analysis, and be cautious when drawing conclusions based on intelligence assessments and limited network data.
This study contributes to strengthening social network analysis as a reliable tool for understanding and analyzing criminality and criminal networks. Social network analysis SNA has been used to tackle a variety of research questions in different contexts. For example, its methods have been used to predict population displacement after natural disasters [ 1 ], to study disease control [ 2 ] and online social behavior [ 3 ]. In the case of criminal networks, the applications range from general criminality to terrorist networks, organized crime and street gangs.
SNA has become an important method for understanding, assessing, and controlling crime networks and has implications for criminal policy. The value of SNA methods lies in their focus on the structure of the relationships in a network, rather than on the characteristics of individual actors [ 4 ]. SNA provides a framework for the abstraction and representation of a phenomenon in terms of interacting units and their relationships [ 5 ]. It comprises a set of methods and tools to gather, process, visualize, and model social network data [ 6 ].
In this context, network analysis has been a key tool of criminal intelligence analysis since the s [ 7 ], from a basic technique in the form of link analysis in criminal intelligence and investigations, to the more recent implementation of core concepts and measures of social network analysis in problem solving strategies and criminal intelligence assessments e. Assessments of law enforcement serve as a guide in policymaking; consequently intelligence analysis based on SNA has implications for lawmaking and criminal policy. It is therefore crucial to better understand the complexity of different empirical data sources as they have implications for the results of network studies and assessments.
Apart from the fact that crime networks are by definition difficult to access, the data on these networks are complex, to the extent that they come from different sources and suffer from different biases. In this study we have the unique opportunity of having access to three different datasets on one specific Swedish street gang.
These datasets are the most common information sources in studies of criminal networks, but it is uncommon that the data sources, intelligence, surveillance and co-offending data, are available for the same case. In this study we explore if different network datasets influence the results of criminal network studies and its consequences for intelligence assessments.
We do it by building networks out of the datasets, and comparing them by computing distance, centrality, and clustering measures.
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Law enforcement has special responsibilities like counterterrorism and controlling gangs and other crime networks [ 9 — 11 ]. The standard model of policing is a traditional reactive response to crime, random patrolling, crime investigations once an offence has been detected, and reliance on suppressive force and the legal system as the primary means of controlling and reducing crime [ 12 , 13 ].
However, policing in the last three decades have moved in the direction of problem-solving as a central strategy [ 14 ]. During the modern era of policing, several paradigms in police strategies have developed to curb the limitations of the standard model of policing, such as Community-Led Policing, Problem-Oriented Policing, Intelligence-Led Policing, and CompStat e.
The essence of these strategies is to make policing more proactive in response to crime and to have police conduct special duties by acting on their own initiative, processing information about crime, and strategizing reduction and prevention [ 16 ]. One core element of these proactive strategies is based on crime and intelligence analysis, often based on SNA methods. Law enforcement collects or receives information on a problem they are trying to control, such as crime rates, other statistics, gang membership, terrorist organizations, crime networks, or an imminent crime etc.
The intelligence is then assessed and serves to guide police management and operations in detecting, reducing, and disrupting of criminal activity, or solving a problem [ 15 , 17 ]. Social network analysis and intelligence-led policing have also gained popularity within the Scandinavian law enforcement and criminology community e. The Swedish police, like many other law enforcement agencies, are paying more attention to intelligence assessments and as such to network analysis, for detecting, reducing, and disrupting the criminal activities of gangs and other networks [ 8 ].
In Denmark, law enforcement is using network analysis to detect gang members who may potentially be induced to leave their gangs [ 22 ]. As, such, network analysis is becoming an important component in both crime investigations and tactical and strategic intelligence assessments [ 7 ].
Together, crime intelligence has both a tactical and strategic role to play in policing, and constitutes a basis for planned operations and threat assessments for upcoming major events. Threat assessments by law enforcement agencies such as Europol, the Swedish Police, and the FBI are also used as a guide in policy-making. Therefore, intelligence analysis has implications for law-making and crime policy. SNA has gained popularity in the study of social phenomena in general, and in studies of criminal networks in particular.
It is furthermore becoming a key component of intelligence assessments. As more information is processed into the network framework, knowledge is accumulated and researchers will be able to provide better advice to policy makers. But this promising endeavor entails challenges as well. One of these challenges regards the use of different data sources to study the empirical phenomenon from a network perspective.
This is a fundamental methodological issue within network science. However, this issue has not received enough attention in network studies, particularly in studies of crime networks. The aim of this study is to explore if different network datasets on the same phenomenon influence the results of criminal network studies, and its consequences for intelligence assessments.
The methodological contribution of this paper is to use the case of a Swedish street gang to illustrate the complexity and reliability of empirical sources in analyzing criminal networks from a social network perspective. Our data for this study are extracted from the National Swedish Police Intelligence NSPI , which has overall responsibility for collecting data on gang membership.
Every regional and local crime intelligence unit in Sweden has the task of collecting information from police officers, units, and other relevant intelligence sources to identify gang membership and report this to NSPI [ 25 ]. NSPI then collects and registers the information. When the information is assessed, the Swedish Crime Intelligence Unit in the region where the gang affiliation was observed registers the information in the Police Intelligence Registry PIR , which consists of several different databases.