Whereas data processing refers to administrative or technical data management practices, in the analysis phase data becomes information that is relevant for political decision-making. Different automated data mining methods serve different purposes and are governed by their own specific rules. Large datasets are used both to identify links between already known individuals or organizations as well as to “search for traces of activity by individuals who may not yet be known but who surface in the course of an investigation, or to identify patterns of activity that might indicate a threat.” For example, contact chaining is one common method used for target discovery: “Starting from a seed selector (perhaps obtained from HUMINT), by looking at the people whom the seed communicates with, and the people they in turn communicate with (the 2-out neighbourhood from the seed), the analyst begins a painstaking process of assembling information about a terrorist cell or network.”
Many intelligence agencies embrace new analytical tools to cope with the information overload challenge in our digitally connected societies. For example, pattern analysis and anomaly detection increasingly rely on self-learning algorithms, commonly referred to as artificial intelligence (AI). AI is expected to be particularly useful for signals intelligence (SIGINT) agencies due to the vast and rapidly expanding datasets at their disposal. However, the risks and benefits generally associated with AI also challenge existing oversight methods and legal safeguards; they also push legislators as well as oversight practitioners to creatively engage with AI as a dual-use technology. Conversely, malicious use of AI creates new security threats that must be mitigated.