Artificial Intelligence Applications in the Open Source Intelligence Cycle(3) - Data Analysis Stage

In the data analysis stage, further feature extraction is performed on the preprocessed data, and on this basis, the data is integrated and analyzed, and finally forms usable and valuable intelligence. In the era of big data, converting huge, diverse, and unrelated data into intuitive and usable information such as trends, key data points, and situational analysis is a major problem facing open source intelligence. Algorithms and models generated based on specific rules in artificial intelligence technology can further discriminate the collected and processed information, extract the correlation between seemingly useless massive information, and dig out the knowledge behind the large amount of information.

1. Independent analysis

Artificial intelligence tools automate certain types of analysis, allowing open source intelligence personnel to offload more time-intensive tasks to computers. For routine open source intelligence products such as geopolitical events, political and military developments in conflict areas, artificial intelligence tools can complete the integration of relevant information and generate summary reports. Intelligence analysts simply need to update, tweak or supplement it. The Machine-assisted Analytic Rapid-repository System (MARS) of the U.S. Defense Intelligence Agency can connect intelligence data from different sources and automatically analyze raw intelligence, greatly improving the collection, fusion and analysis capabilities of open source intelligence.

2. Pattern recognition and semantic analysis

By sifting and simplifying data, artificial intelligence techniques can assist open source intelligence personnel with semantic analysis and understanding. For example, deep learning algorithms can be used to identify patterns and trends in data streams and infer the relationship between targets; natural language processing technology can perform speech-to-text transcription, speech recognition, text summarization, language translation, etc.; advanced natural semantic analysis and pattern recognition technologies can also improve the accuracy of language, image and video recognition in complex backgrounds and environments.

3. Enhanced monitoring and risk identification

Artificial intelligence can monitor incremental changes in daily intelligence information changes to avoid missing the best time for intelligence information. Through unstructured data processing, dynamic intelligence analysis, and multi-source intelligence fusion, a unified reality scene can be constructed to identify important targets and events, discover laws and deduce their development trends, and detect potential threats and risks. In addition, analysts can also use tools such as artificial intelligence-based data mining, sentiment analysis, and geolocation to help monitor and predict disruptive events such as large-scale protests, and give early warnings of potential social crises and instability.