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.

1. How AI Is Paving the Way
for Improved Surveillance
and Cybersecurity
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3. 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.

4. 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.

5. 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.

6. AI in Surveillance and Cybersecurity

7. 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.

8. 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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