The analysis and assessment of threats constitute a primary focus of intelligence. However, it seems at times that analysts appear to encounter more difficulties in analysing threats than in conducting risk assessments.
Some of these difficulties are related to analytic rigour. Often there is an overreliance on analysts’ judgements in preparing threat analysis which is shown in the process of data interpretation and the weight allocated to different factors. Among the problems identified in connection with this issue are the following:
- Inability to identify activities that fall ‘below the radar’ or what is known as the identification of weak signals. As these signals are small precursors, they are usually lost in the middle of a large amount of noisy information.
- De-sensitization of the law enforcement community to noisy event. Often, such events are dismissed as ‘weird’ or odd and may mimic an action/incident that is fully explainable.
- Quantification difficulties and differences. In the case of threat analysis especially, the assessment of magnitude and likelihood is a key component. Most of intelligence organisations use an approach that relies on curated sets of linguistic probability terms. However, this system has both practical and methodological flaws. It lacks interoperability (practical flaws), and the linguistic probability phrases are inherently vague and context-dependent (methodological flaws).
A second challenge refers to the use of the OSINT, and the distinction made between OSINT and SOCMINT). As social media became a new space for collecting and analysing online information, it was initially considered an OSINT sub-domain. However, with the diversification of social media platforms and contents, the OSINT framework seems to be less applicable and usable, therefore many scholars are now arguing that SOCMINT is closer to SIGINT than to OSINT. Within the threat analysis it is important to make a distinction due to the fact that the information collected, validated and analysed depends on the category it falls under. Given that experts agree that social media is a very useful resources for assessing threat intelligence by, for example, identifying intention, it is very important to understand some of the challenges of collecting and analysing SOCMINT.
- One of the key challenges is related to information verification, or in other words which are the standards that should be applied to social media information, given its interdisciplinary nature, those of OSINT or of SIGINT, HUMINT or a combination of all of the above.
- Information access and completeness of data retrieved. Social media aggregation companies that market social media data often provide only a fraction of the data from a social media platform or dataset from only a specific window of time.
- Too much focus on social media data from US-based platforms, to the detriment of native platforms, which may be more relevant for intelligence purposes.
- The data sets do not provide a representative sample of a population as different demographic groups use social media unevenly.
- Content is very dynamic. The acquisition and retention of social media content must, therefore occur in real-time and be constant, as impactful content may be posted and removed in a short period of time.
- Content is published in a variety of formats (e.g. from text, such as Reddit threads to live videos on Twitch).
A third challenge is related to the categories of data included. Threat analysis can refer to static factors (historical elements; factors that cannot be changed or change only in one direction), but it can also include dynamic factors (behavioural, social, attitudinal, etc.). For example, in the case of irregular migration, migrants’ attitudes towards different topics may be included under dynamic factors. The main problem, in the case of dynamic factors, is that, as the name implies, these changes and may fluctuate over time, making them difficult to quantify and analyse. A good example of these challenges can be found when trying to analyse attitudes in order to identify threats, as shown in the MIRROR Perception Model.
A fourth challenge is related to the content, and more specifically to the definition and nature of the threat. In terms of definition, the main problem is the very process of threat identification, which can be significantly influenced by what is labelled as the ‘securitisation of discourse’ and its implication for threat analysis. The main concern is that the set of narratives build around ‘securitising’ migration, are employed to inform discourses, policies and practices of both state and non-state actors. In some cases, for example, the human security discourse has been employed to legitimise the tightening of borders in the name of migrants’ own ‘security’. The threat-migrant association can give rise to an important number of problems such as:
- Algorithmic bias for AI technologies employed in a border security setting. The ‘securitisation’ of migration is directly linked to the use of surveillance and data collection. However, some experts argue that this approach can be very problematic, due to the inherent vulnerability of migrants; the concern being that technological ’solutions’ are employed on refugees and migrants because they can scarcely object and often lack even basic knowledge about what is involved.
- Bias in the analysis. The framing of all migrants as threats has important consequences for the objectivity of intelligence analysis and the instruments it would recommend to employ in addressing the problem.
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