The Problem Statement and Solutions Offered
Introduction
Intelligence collection is inseparable from the general trends that emerge and persist within the framework of international relations. In the 21st century, an increasing number of such trends revolves around the development of various technological means, including the artificial intelligence and big data analysis. As a result, the field of intelligence faces a key challenge that consist of the need to balance the threats and benefits of technology for data collection. On the one hand, advanced solutions facilitate access to considerable amounts of raw data to process and analyze for further use. This way, experts in this area can work more efficiently, responding swiftly to emerging threats. On the other hand, technology contributes to the complexity of the field, while creating new vulnerabilities within the global security framework. In order to benefit from its advantages, intelligence specialists require a solid operational framework, within which these vulnerabilities are mitigated.
Background
In addition to the general convenience of technology-based communication and work operations, progress provides new avenues for the collection and processing of raw data (Young 2019). With the unparalleled computing power of modern machines and software, in-depth analyses can be completed within minutes (Kosal 2018). High-tech solutions help intelligence experts detect, collect, and categorize the data, combining its pieces into a full-scale picture. Nevertheless, these benefits come at a certain price, which consists of the elevated threats faced by the intelligence community. More specifically, the reliance on technology is associated with cybersecurity and informational threats in the form of various breaches and leaks. Furthermore, the lack of human expertise in the process can result in the incomplete collection of data.
These factors entail a certain paradox, per which today’s intelligence agencies become capable of more complex operations, while remaining exposed to the external threats. In highly sensitive areas of operation, such as national and global security, it is natural for intelligence communities to remain as limited as possible (Hershkovitz 2019). In other words, the number of people who have access to the internal operational framework needs to be reduced to a minimum in order to prevent information leaks. Otherwise, valuable data may become available to those who wish to impose threats to national security. The presence of a strong technological component contradicts this principle of data containment. Specifically, long-distance communication and large amounts of intelligence stored in digital spaces create more openings for potential breaches (CSIS 2020). In this regard, it is imperative to consider possible solutions to help balance the threats and advantages of a high-tech approach to intelligence collection and processing.
Solutions
In light of the ongoing technological progress, the field of intelligence collection is gradually coming to an understanding that new emerging solutions can serve its purpose well. As acknowledged by Hershkovitz (2019), experts in this area are almost ready to move from a traditional, compartmentalized manner of their operational organization to a more up-to-date framework. One of the new models in this regard is the emergence of open-source intelligence platforms. They aggregate the data that is declassified and can legally be made public. The primary purpose of the approach is to attract independent contributors across the global net, thus expanding the number of people involved as volunteers. According to Hershkovitz (2019), this type of information is easier to acquire and faster to process. As a result, open-source intelligence reduces the costs of operations while enhancing their efficiency by a considerable margin. Nevertheless, this framework can only be applied to a limited array of data that does not include sensitive, classified intelligence (Evangelista et al. 2020). Therefore, serious barriers to its further spread are observed, preventing the model from becoming implemented more actively.
At the same time, the amount of information that is produced by humanity on daily basis remains on a stable increase. As a result, the workload of intelligence agencies grows accordingly. Open-source intelligence implies quantitative enhancement of the field’s operational capabilities. On the other hand, for classified data, the key to success may lie in a quality rather than quantity (Stevens 2017). The use of specialized big data models can be an optimal solution to increase the processing ability of intelligence agencies (Hershkovitz 2019). Such models are capable of identifying, collecting, and analyzing large datasets in short periods, which is of major use for the field. However, experienced specialists argue against them, insisting that even the most sophisticated algorithms cannot replace human expertise. The price of an error is particularly high in national security-related areas of intelligence. In addition, this approach implies the involvement of new information technology units, expanding the number of people involved. This way, the pursuit of efficiency can compromise the integrity of the field, causing breaches and leaks.
Preliminary Conclusions
Overall, intelligence collection of the 21st century becomes increasingly complex due to the growing amount of data to analyze and process. This trend is related to the stable increase in the information-producing activity of the human civilization. Along with it, the workload of intelligence specialists grows accordingly, creating a need for more efficient methods of intelligence collection. In the spirit of the time, most of these methods are inherently based on advanced technology. One the optimal solutions consists of implementing big data models into the intelligence gathering framework. In this regard, a strong need for balancing the benefits and risks of the approach emerges. From one perspective, as the amount of data to process grows exponentially, the efficiency of the method is beyond doubt. On the other hand, experts question its reliability, resilience, and safety in the face of global indeterminacy and cyberspace threats. Yet, the projected benefits of the approach speak in favor of its applicability within the field of intelligence collection.
Literature Review
Introduction
In the sphere of intelligence collection of the 21st century, a strong need for technology-based solutions has emerged. This approach is projected to enhance the ability of designated agencies to identify and process vast arrays of data. In fact, the amount of information produced by humanity is on a stable increase, which requires a higher level of efficiency on behalf of the intelligence community (Young 2019). In this regard, the use of big data, artificial intelligence, neural networks, and other advanced solutions appears to be a viable alternative to the traditional methods of intelligence collection and analysis. They can synthesize valuable data from various sources, providing the responsible units with details over a shorted timespan.
Yet, doubts persist regarding the feasibility of technology-assisted intelligence, as well as its safety in terms of breach prevention and precision. This section aims to the explore the perspective that is reflected within the contemporary body of knowledge. Academic articles, monographies, and digital publications have been investigated in regards to high-tech intelligence collection safety and accuracy. In order to examine the body of literature in its current state, only recent publications from the past five years were placed under examination unless well-established, fundamental concepts were discussed.
Review of the Literature
In order to understand the applicability of certain solutions in the field of the 21st-century intelligence, it is imperative to review the key aspects and priorities of the area. Today, this field has become increasingly complex due to the intensification of global processes, data production, and knowledge exchange on all levels. Spoken differently, the amount of information that is produced by the humanity and that should, therefore, be considered by intelligence agencies grows exponentially. According to Hershkovitz (2019), the amount of text to be analyzed by an average intelligence collector has grown by ten times in the past decade, and another tenfold increase is projected for the next five years. Lemieux (2018) reports similar trends, indicating the growing level of social and political complexity in today’s landscape. These tendencies have a direct impact on the presence of global and domestic threats to national security that need to be addressed through intelligence. In part, this mission is supported by the widespread of surveillance technology that permeates most aspects of life today. However, while more intelligence is available now because of it, this data needs to be efficiently collected and identified.
In order to ensure the required level of efficiency when it comes to intelligence gathering, modern experts actively research the benefits of advanced technological solutions. This aspect is particularly important due to the increasing reliance on technology exhibited by modern society. As these advancements become the new norm in most aspects of contemporary life, the field of intelligence is expected to respond in kind by embedding technology in its operations. Konstantinou and Maniatakos (2019) refer to the security of smart grid systems as an example of a vulnerable high-tech sphere. With the presence of microprocessor-based embedded systems, these grids become subject to elevated risks that originate from the digital environment. It is only logical that the corresponding intelligence should be centered on this environment, as well (Keliris et al. 2019). The same shift toward digital data is reported by the U.S. Counterintelligence and Security Center (2021). According to this body, the challenges posed to intelligence collection by the emergence of technologies are unparalleled. Accordingly, the nature of the issue requires the field to utilize new avenues of data collection in order to remain on par with the potential adversary.
Unless technology becomes an integral component of intelligence gathering, responsible units risk underperforming in terms of their crucial duties. One of the key requirements to intelligence is that it is expected to be collected and provided in a timely manner. With the spread of advanced technology, potential threats are developed at a record pace, meaning that intelligence cannot remain behind (Breckinrdige 2019). As a matter of fact, a paradox is created, in which the need for accelerated intelligence collection and analysis is combined with the constant increase of the data to analyze. This is particularly evident in the case of foreign intelligence, as the international landscape currently sees an increase in overall complexity and global indeterminacy (Viola and Laidler 2021). Amid the intensification of the informational space, intelligence units are required to work at the intersection of efficiency and precision, while the supply of raw data reaches previously unseen levels. This information clearly outlines the conundrum, in which the field finds itself today, but it does not shed light on the potential solutions that can address the problem at hand with the required efficacy.
The field responds by increasing the role of technology-based practices in its scope of operations. In some cases, this implies reconsidering the very organizational framework of the work. Open-source intelligence is one of the recent solutions that were proposed in response to such issues. This framework suggests that certain parts of intelligence can be completed on the basis of an open collaboration platform that unites experts across the digital environment. The work by Miller (2018) specifically addresses this concept, noting that its appearance is due to the unprecedented availability of information to society. Open-source intelligence is inherently different from the traditional approach to such work in that it surpasses the conventional compartmentalization of the designated units. Furthermore, its applicability is limited to non-classified sources of data. Miller (2018) notes that this concept is often perceived as an oxymoron, but the reality suggests that the role of open-source. In fact, this domain now generates the vast majority of all data, and some parts of it can seriously improve intelligence agencies’ understanding of the current processes.
This is especially true in the case of social media where a large share of communication happens. Intelligence on its own is a broad sphere, which addresses a variety of contemporary issues. Interestingly, social media often become the platform of public communication that aligns with these spheres, from social activism to organized crime and terrorism (Hassan and Hijazi 2018). At the same time, valuable intelligence is dispersed across vast amounts of data generated on daily basis within the open segment of the digital environment. With the traditional methods of collection, important pieces of it may pass unnoticed, but open-source intelligence seeks to enhance the capabilities in this regard (Riebe et al. 2021). For example, Chinese intelligence relied on the open-source approach to detect the early signals of COVID-19 during the initial stages of the pandemic (Kpozehouen et al. 2020). Reports, statistics, and other open pieces of data were gathered and analyzed to address the time pressure that was imposed by the rapid nature of the pandemic. Overall, while open-source intelligence cannot become an independent method of intelligence collection, it is a viable response to the intensification of the information space.
In light of this information, it appears relevant to complement the existing practices with other high-tech tools that can meet the central needs of intelligence communities without compromising their security and efficiency. Regan et al. (2021) mention that this professional is sphere is characterized by the necessity of making serious decisions in limited time and under increased pressure. Under such circumstances, the possibility of imprecise judgment increases, and the cost of a mistake is considerable, especially in the case of state security intelligence. Accordingly, the experts begin to wonder whether modern technology can completement this process by analyzing the data more efficiently and drawing accurate conclusions. In this context, the applicability of the artificial intelligence (AI) and big data is actively discussed within the current body of literature. Befort et al. (2018) refer to the example of pilots who represent another area of expertise with a need for precise judgment and quick decision-making. Their examination shows that AI-based enhancements hold considerable potential in terms of mitigating the subjective of pilots’ judgment and guiding precise decisions in stressful situations.
Such technologies are already in place across different professional areas, including banking. According to Leonov et al. (2020), not only does AI enable efficiency, but it performs well in terms of handling sensitive data that cannot be breached. This process can be complemented by the use of big data technology, which serves to accumulate disperse sets of data into a single channel, informing decision-making and optimizing the collection stage. Martins et al. (2020) report its positive impact on decision-making in the vital areas of business intelligence, which is an important component of a nation’s economic security. In healthcare, Chinese experts successfully tested big data methods to collect and analyze valuable medical intelligence to prevent and contain the spread of COVID-19 (Wu et al. 2020). Bedubourg et al. (2018) propose a similar model for medical intelligence gathering in the military setting, which would help to form a united framework for the future NATO operations. Therefore, AI and big data are already applied for the collection of various types of intelligence. They have not revealed major flaws in informational security and precision, but the range of their application remains limited.
Conclusion
Overall, the analysis of the contemporary body of literature reveals that most experts and researcher are in line regarding the key challenges faced by the intelligence community today. The leading issue that is being reported is associated with the continuous, exponential increase in the amount of data generated by the human civilization today. With high-tech-powered means of instant global communication, the amount of raw data grows manifold on annual basis. While the traditional methods of collection ensured precision of analysis through the expertise of units, they struggle to handle such a vast influx of information. In this regard, the professional area is expected to develop new solutions that can optimize the process of data gathering without compromising on its precision and security. In light of this need, researchers identify two key approaches, either through quality or through quantity of collection resources.
In terms of the quantity-based models, the consensus is that open-source intelligence is a major improvement that aligns with the properties of current informational environment. A considerable share of all data generated currently exists within open domains, namely the open segment of the Internet and social media, in particular. With the facilitated access to knowledge exchange, people share more data on the web than ever before. This creates new opportunities for intelligence, but valuable information remains scattered across terabytes of irrelevant content. Open-source methods expand the network of intelligence, accumulating vast datasets for analysis.
However, its usability is limited to standard, non-classified data, which does not fully meet the needs of national security. In this case, a quality-based approach to intelligence collection is required. Most researchers agree that the use of artificial intelligence and big data can be the optimal solution to the problem. Specialized secure tools of this kind are already in place to collect different types of intelligence, including medical, business, and environmental. At the same time, while it has proven to support informed decision-making, its use still remains limited. It is possible that a further expansion can result in the emergence of previously unidentified risks.
Findings and Conclusion
Analysis and Findings
In light of this issue, the field faces an increasing need for efficient methods of data collection and processing. As the nature of the problem stems from the technological sphere, it is natural to seek solutions in the same domain. Spoken differently, it appears impossible to address the growing stress on intelligence collection channels without enhancing their capabilities with high-tech solutions. As the examination of contemporary literature suggests, there are several overarching guidelines that inform the pursuit of such new methods of collection. Evidently, the first aspect is related to the swiftness of operations as the key component of efficiency. The first property that is damaged by the exponential increase in raw data supply is the speed of processing.
Illustrated by a basic example, suppose that one intelligence collection unit can process 50,000 words of raw text per hour. Previously, this unit was assigned to 500,000 words of data per day, which implied that 10 hours were required for it. Then, as technology developed, the same channels began to produce 1,000,000 words of data within the same period. A similar adjustment of productivity when does not seem possible when the same methods are used. Thus, the unit now needs 20 hours to process the influx of data and collect intelligence instead of 10. This way, their efficiency becomes depleted, and the time required to detect important information is expanded, thus potentially delaying the identification of serious threats. Simply doubling the resources, including human resources, engaged in the task is not an optimal solution, as it is not efficient. Instead, the leadership is expected to seek new methods that would allow the same unit to process and collect the required amount of data in less time.
Open-source methods are often reported as a viable solution to the current challenges of intelligence collection. In a way, it is a mirrored response that functions upon the same mechanism that produced the initial threat. Easy access to information prompts people to supply more data, and the same tendency enables the use of open-source intelligence. Notwithstanding the opportunities, some experts reasonably doubt its full-scale applicability for sensitive and classified matters (Tiwari et al. 2020). First, there cannot be open access to some sources of information, especially along the channels of law enforcement, government, and foreign intelligence. This data is gathered in a highly specific manner by specialized units with the required level of access (Balding 2020). Opening it to a wider audience would be equal to simply expanding the staff of the unit.
Thus, there is a need for technology-based tools that can raise the productivity of intelligence collection units. As identified in the literature review, the general trend points at the usability of the artificial intelligence and big data in data collection and processing. The potential of this method appears promising due to the vast opportunities provided by data science and neural networks. The computing power of modern solutions is unparalleled, as it is capable of reviewing and analyzing millions of lines of raw data in a matter of hours and even minutes (Lyon and Wood 2020). With all its expertise and experience, a human brain is physically unable to perform at the same level. If a properly set algorithm is in place, the aforementioned increase from 500,000 to 1,000,000 words per unit can be easily mitigated. However, this leads to a controversial position regarding what defines a properly set algorithm.
The combination of the AI and big data technology is expected to work independently. Nevertheless, some experts rightfully highlight a serious gap between the judgment of an experience intelligence worker and a computer. With all its power and swiftness, a machine can lack the judgment of an expert in the field of intelligence (Swanson and Vogel 2018). As a result, it can either ignore potentially valuable pieces of data or overwhelm analysists with unnecessary information that was selected by the algorithm but has not practical use. This idea suggests that a certain degree of human supervision needs to be involved.
Thus, a logical question arises of what this degree is to be. If human experts are to double-check every operation performed by the machines, the entire endeavor becomes virtually pointless. In this regard, it appears necessary to determine by practice the approximate threshold of human involvement that allows experts to be sure of the AI’s precision without it becoming excessively time-consuming. In the end, this can be a fruitful collaboration, in which human expertise will applied with greater efficiency, while the use of machines will negate the growing pressure of the expanding informational space.
Conclusion
Overall, the identified set of solution addresses the central issue in two domains. The share of publicly available intelligence is on the increase, which speaks in favor of the open-source intelligence usability under the current circumstances. Such platforms can navigate between the pieces of valuable data that are scattered across millions of lines of content produced on the Internet and social media. For the classified categories of information and tasks, the use of the artificial intelligence in pair with big data analysis is the optimal solution that can promote intelligence units’ efficiency. This model meets the key criteria associated with the nature of the challenges faced by the field. Nevertheless, the precision and security of the data cannot be compromised, as serious risks may emerge otherwise. Therefore, the field of intelligence collection is required to conduct further work on developing an optimal distribution of operations within the identified framework.
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