Emerging technologies’ role in the evolution of the intelligence cycle

Teksti | Janel Coburn , Pasi Hario

Amidst the global push toward more technologically advanced societies, so too are the world’s superpowers recognizing how these emerging technologies are and will continue to rapidly change their collective military and law enforcement intelligence communities. A well-known and globally adapted process that facilitates the basic operations of any intelligence organization is known as the ‘Intelligence Cycle’. This cycle, which consists of five basic phases; planning, collection, processing, analysis, and dissemination, is also facing imminent change as advances in artificial intelligence (AI), machine learning (ML), and quantum technology (QT) emerge at ever-increasing rates (Katz 2020; Smith and Brooks 2013).

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Photo: Artem Saranin/Pexels

This article will discuss how these three rapidly emerging technologies, acting as powerful force multipliers are predicted to fundamentally change the intelligence cycle and enhance intelligence and law enforcement capabilities by improving the rate at which global intelligence communities collect, store, process, produce and disseminate high-quality intelligence.

NOTIONES – a project to catalyse interaction between intelligence practitioners and technology providers

The realization of these emerging technologies and the need for EU member states to urgently adopt them to their current processes is a key objective of the EU-funded project NOTIONES (iNteracting netwOrk of inTelligence and securIty practitiOners with iNdustry and acadEmia actorS). NOTIONES brings together 29 EU and third-country security practitioners and academics with the goal of creating impactful synergies that support faster identification of the most relevant emerging technologies appropriate for uptake by the security industry, as well as working to lessen the process times to legally adopt and integrate new technologies. These efforts support the EU to remain on the leading edge of the current technological wave. (NOTIONES 2022.)

Laurea UAS plays a key role in this project by collecting the perspectives of various EU practitioners regarding their current experiences and future needs related to emerging technologies suited for their security and intelligence purposes. Laurea leads multiple tasks to facilitate workshops, working groups, and conferences that bring together academics, technical experts, and intelligence practitioners to find solutions to the current technological challenges and threats which face EU security and intelligence practitioners.

The Classic Intelligence Cycle

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Figure 1. The Intelligence Cycle (based on Smith and Brooks 2013)

The classic five-phase intelligence cycle (Figure 1) has remained somewhat unchanged after being widely adopted by the United States military and western allies after the Second World War. The cycle has been criticised as a bureaucratic representation of the intelligence process having little to do with the actual application of intelligence practices. Nevertheless, it is still widely used as the theoretical model to describe the macrolevel of intelligence activities (Hulnick 2006).

Following Smith and Brooks (2013), the five phases of the classic intelligence cycle are described as:

Planning, Direction, Needs, and Requirements: Determining what issues need to be addressed and what information must be gathered to provide the applicable answers. Policymakers and other strategic offices initiate requests for intelligence which guides information requirements and collection. The phase includes examining finished intel from previous cycles.

Collection: Gathering raw intel from many different sources; open, covert, electronic, and satellite. Includes at least imagery intelligence (IMINT), human intelligence (HUMINT), electronic intelligence (ELINT), open-source intelligence (OSINT), measurement and signature intelligence (MASINT), communications intelligence (COMINT), geospatial intelligent (GEOINT), and signals intelligence (SIGINT).

Processing: Synthesizing raw intelligence into usable form and organizing data. This takes place by exploiting imagery, decoding messages, translating broadcasts, telemetry analysis, cataloguing various intel reports, and various other means.

Analysis: Integrating, evaluating, and, analysing all available data, to create finished intelligence products and reports. Intelligence analysts also develop new collection requirements.

Dissemination: Distributing intelligence products to requestors, which can lead to new intelligence requests.

One of the goals of NOTIONES -project was to investigate the validity and functionality of the classical five-phase intelligence cycle in contemporary world. In order to perform the task, the project gathered feedback from law enforcement agencies and civilian intelligence practitioners from Bulgaria, Finland, France, Italy, Spain and the United Kingdoms. Even though the activities and tools inside each phase have changed during the last decades, the practitioners concurred the applicability of the classical division. (NOTIONES 2022b.)

Artificial intelligence (AI), machine learning (ML), and quantum technology (QT) as game changers

OECD (2020) has named artificial intelligence and quantum technology as examples of the most important game changers in our digitalizing societies. However, from intelligence community’s perspective, the development of quantum computing, artificial intelligence and machine learning, as an overlapping domain to AI, are the most promising technologies to change and catalyse the intelligence activities (Katz 2020).

Artificial intelligence is the broad science of training machines to perform human tasks and emulate human abilities by combining computer science and robust datasets, to enable problem-solving. AI encompasses machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence (IBM 2020; NATO 2020).

Machine Learning is a sub-set of artificial intelligence which trains a machine how to learn via pattern recognition. Machine learning models look for patterns in large volumes of diverse granular data to draw conclusions about various environments and learn about them to predict outcomes and risks (OECD 2020; SAS 2021).

Quantum Technology is a multi-disciplinary field of nanophysics, computer science and electrical engineering, which relies on the principles of quantum physics. QT utilises complicated physical laws to develop augmented processing capacity and computers that can solve problems beyond our current reach. Quantum mechanics and the accelerated processing capacity of quantum computers augment the development of new technologies, quantum cryptography included. (Aalto 2021; NATO 2020).

The emerging technologies in different phases of the intelligence cycle

The three emerging technologies, central to the NOTIONES project, have potential applications that span the entire intelligence cycle and are the result of innovations that intersect and build on each other. As Katz (2020) succinctly explains generic technological trends, “driving this change is the convergence of four technological trends: a proliferation of networked, multimodal sensors; massive growth in “big data,” both classified and unclassified; improvements in AI algorithms and applications particularly suited to intelligence, such as computer vision and natural language processing; and exponential growth in computing. The technologies have separate and overlapping effects to all phases of the classic intelligence cycle.

Planning phase

Various types of artificial intelligence (AI) and machine learning (ML) algorithms will have an impact on the planning phase of the intelligence cycle by using big data analytics to assist decision-makers in identifying information requirements and prioritizing collection targets at much faster rates.

Collection Phase

A multitude of advanced sensors using AI and ML are being created which fit naturally into the collection phase of the intelligence cycle. In particular, the imaging sensors (GEOINT/IMINT) and other highly sensitive Intelligence, Surveillance and Reconnaissance (ISR) sensors that capture signals (SIGINT) and emissions (ELINT) will benefit from the enhanced processing power of quantum technology (QT) to conduct ultra-precise calculations and correlations leading to improved analytics. Additionally, a technology known as quantum radar; a form of quantum remote sensing (QRS), could potentially detect stealth technologies. Quantum radar uses quantum illumination or quantum interferometry and other quantum enabled sensing and allows for the detection of gravitational and magnetic anomalies that will enable georeferenced mapping with a precision that is much higher than current levels (Evaldes et al. 2021).

Likewise, as classic commercial AI and ML augmented sensors improve, OSINT collections are proving to be just a powerful as the traditional “exquisite” intelligence technologies which have for some time been overburdened and might soon be used to focus on higher collection priorities. As for HUMINT operations, AI algorithms and advanced analytics could be used to assist operators to better assess potential sources for recruitment and predicting counterintelligence risks in the process. (Katz 2020)

Processing Phase

AI, ML and QT are particularly well suited to process the large sets of sensor-derived data from the various types of technical intelligence  like MASINT, ELINT, GEOINT, COMINT, and SIGINT. AI technology, such as computer vision, can assist GEOINT collection by automating the processing of imagery data and performing time-intensive image recognition and categorization. Meanwhile, natural language processing and speech-to-text translation and transcriptions can assist with rapid COMINT collection processing (Katz 2020). Further, quantum optimizations exist for all of these technologies within the intelligence cycle, providing faster and ultra-precise numerical simulations, measurement capabilities, sensing, precision and computational power, and efficiency of current and future military technology (Krelina 2021).

Analysis Phase

AI and ML automation may greatly assist analysts to more efficiently identify patterns, trends, and threats among an ever-growing data stream as well as improve their deduction skills and test their theories against AI-derived analysis resulting in high-quality finished intelligence products. Emerging data visualization technologies may also transform how analysts perceive processed intelligence resulting in fresh insights and acute analysis which policy and decision-makers depend on. Furthermore, using AI and ML technologies to conduct advanced analytics applying big data could help analysts avoid pitfalls such as groupthink phenomena or cognitive biases and allow them to spend more time focused on areas of analysis where more nuanced skills are needed (Katz 2020).

Dissemination Phase

Finally, within the dissemination phase, AI and ML can be utilized to create automated, filtered, and intelligent dissemination of finished and real-time intelligence to the appropriate users. Further, cloud-enabled collaboration and sharing of finished intelligence reports among partners and allies may strengthen analytic findings, build shared missions, and provide consistent and timely feedback to collectors and customers (ODNI 2019). Lastly, quantum communications and cryptography are technologies that must be obtained and implemented to secure lines of communication where intelligence is shared and to protect from quantum attacks (Krelina 2021).


Based on NOTIONES- projects findings, the current five-phase intelligence cycle is still a valid framework for intelligence activities performed by law enforcement agencies and civilian intelligence practitioners. However, the aforementioned technologies will certainly shift how the various phases of the intelligence cycle are implemented and enhance all levels of their output, but they also impact the intelligence profession itself. As the more traditional intelligence technologies are made commercially available, some purposes of military intelligence are then questioned as potentially redundant and the traditional costs to taxpayers unjustified. Additionally, technologies emerge and merge with other intelligence tasks and entire responsibilities once assigned to various analysts or agents virtually disappear. Therefore, fundamental changes loom for the analyst themselves. For example, how are analysts now recruited and trained, what skills and attributes might they need to possess (Katz 2020)?

The NOTIONES project works to support the intelligence and security practitioners of the EU to prepare for these emerging technologies. The project helps the practitioners to discover, adopt, and integrate technologies as they are developed at increasingly speed. Furthermore, the NOTIONES community will identify how these emerging technologies impact current intelligence operations and what new challenges may be presented as a result of their existence.

If these emerging technologies will be rapidly adopted and integrated to intelligence work, it will improve the quality and rate at which intelligence communities collect, store, process, produce and disseminate high-quality intelligence. This would fundamentally change and enhance the traditional intelligence cycle and contribute to the development of more agile, efficient and informed intelligence and law enforcement communities.

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101021853.


URN http://urn.fi/URN:NBN:fi-fe2022060242042

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