Benefits of using Machine Learning in Intelligence Collection

As the advancement in artificial intelligence and machine learning is rising, every industry is thriving to adopt these technologies in their business processes. The intelligence agency is one of the industries that adopt machine learning in their tasks. As a huge amount of data is being generated day by day, the intelligence agency requires machine learning in collecting, organizing and analysing data. Machine learning processes data in seconds which takes hours for any analyst freeing them from performing more critical tasks. These analytics serve with immense speed and unburden analysts. Implementation of these intelligent tools helps analysts to perform analysis of data instead.

The main purpose of machine learning in intelligence is to manipulate received data within seconds and present the data to analysts for further analysis, which reduces hours of manual labor. Analysts also need to understand and learn how to apply this ML technology to solve complex problems. This evolving ML helps to analyse and make sense of the deluge of data to conclude. General Manager of intelligent solutions business unit at ManTech said that implementation of analytics helps in analysing mounds and mounds of data so that analysts can get free time to work on determining which are the best action courses for national security and to determine various policies.

When the systems are interfaced with ML, analysts can simply connect a database for their requirements. The computer will scan the data present in the database, detect the required information and return the gathered data to analysts for review. Thus machine learning enhances processes, workflows and quality output in intelligence agencies. Many machine learning companies are now offering solutions as per business requirements. The services provided by these companies take the intelligence agency to the next level by delivering optimal results.

North Korean government is now using machine learning in missile launches. During launching a missile main job of an analyst is to check for clear spaces, transporter erector launches along with fuel trucks. Machine learning computers are now engaged to check all the required information by verifying the data and if it meets with any threshold it will alert the human analysts to check the critical situations. Hyper automation is one of the most advanced technologies that create people-centric smart workplaces. As an intelligence agency in the main relay on technology, hyper-automation is one of the best choices. Gartner announces hyper-automation would be the number one strategic technology trends for 2020. AI service providers are now providing hyper-automation by incorporating technologies like AI, RPA and ML to automate all the business processes with utmost speed and error-free.

Combining all the analytics with advanced technologies results in more comprehensive and higher quality intelligence products, derived more professionally and more swiftly. ML service providers are now providing the technologies that can provide better insights from a video in a fraction of seconds with frame by frame analysis and object detection by objects in the video. US, Chain, and Russia have already implemented machine learning technology in the Defence department.

Below are the few capabilities of ML and AI in the CIA:

• Determining threats and thwarting scheduled attacks.
• Deactivating the emails which are labeled as cyber attacks
• Satellite surveillance of doubted areas or high alert areas.
• Regions with social disturbance can be predicted and identified with ML.