How AI Is Paving the Way for Improved Surveillance and Cybersecurity
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Overview Cybersecurity is increasingly leaning towards artificial intelligence (AI) to help mitigate threats because of the innate ability AI has to turn big data into actionable insights. Rightly so, because the threat to data security is real, and across all industries. For instance, while there were fewer than 50 million unique malware cases in 2010, the number had risen to more than 900 million malicious executables in 2019, per the statistics of the AV-TEST Institute. Another report states that malware is the most concerning cyberthreat targeting organizations, with phishing and ransomware jointly ranked second.
AI, Machine Learning, and Data Security AI capabilities are rooted in machine learning (ML) tasks such as natural language processing (NLP) as well as applications like graphical processing units (GPUs) for 3D data or Google’s own application-specific integrated circuits tensor processing units (TPUs) to accelerate machine learning workloads. These powerful tools help train complex models of neural networks as they discover trends and patterns and trigger actions in text and video data to detect security risks.
Text and Video Analytics AI software collects a large amount of security event data from different sources and analyzes it using text analytics and background modeling for videos. It accesses sources like social media comments, user-generated videos on accounts like TikTok, Facebook, tweets, phone messages, etc. and identify anything that is an anomaly. It also compiles incident reports. This facet is used increasingly by law and order agencies to mitigate national security threats, child endangerment, help in suicide prevention, and other critical areas.
AI in Surveillance and Cybersecurity
1. Text analysis and incident reporting AI algorithms gather data across numerous sources including social media, chat forums, and cell phone and app messages to detect cyber threats or vulnerabilities. Natural language processing tasks further identify specific keywords, extract them from whichever source they occur in, compile, and summarize it. These algorithms can also gather information on the origin of the text, the latitude, and longitude, as well as the IP address of the user. Intelligence Reports NLP also enables AI programs to generate automated cyber threat intelligence reports (CTI) that can give early indicators and warning signs of unusual activities on a given network. Reports like these have helped financial institutions tremendously in mitigating fraud and thefts – so also industries like hospitality and healthcare.
2. Video content analysis Powerful ML algorithms can analyze videos for their content by converting audio to text and extracting any topics or words that have been deemed dangerous to the public. Video content analysis, importantly, also includes identification and extraction of background imagery, logos, objects, and any other key features that can point to anything that is a threat to the public. Video analysis is used to detect not just threats to security promulgated by terrorist organizations but also those that are spread by way of misinformation that can cause great damage to society or create chaos in governance. The recent example of conspiracy theorists taking to social media and spreading misinformation on COVID-19 lockdowns and targeting governments, as well as the anti-vax movements, are lucid examples of how cyberspace can be used by anyone for vicious activities.
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