Role of Artificial Intelligence (AI) and Machine Learning (ML) in Cybersecurity


As computing power, data gathering, and storage capacities have increased, Machine Learning (ML) and Artificial Intelligence (AI) are widely used in various sectors and applications. 

Along with the advancements, there is a rise in threats such as ransomware, botnets, malware, and phishing. Though, AI and ML also help identify new exploits and weaknesses, reducing the workload of security professionals. Therefore, alerts them whenever an action is needed. 

What are AI and ML?

Artificial Intelligence is the only solution to defend networks against the various attacks that an antivirus or firewall cannot detect. AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. They are fed huge amounts of data, which helps them to understand the trends. 

Machine learning is a part of artificial intelligence which combines data with statistical tools to predict an output that can be used to make actionable insights. Uses to backtrack and report any deviations or anomalies in the network in real-time. 

Why AI and ML?

The security threat landscape continues to evolve not just in scale, but, more importantly, in sophistication Despite several developments, organisations have found it difficult to stay up with the technologies and tactics used by attackers. 

According to Norton's research, the average data breach recovery costs $3.86 million globally. According to the survey, it takes businesses 196 days on average to recover from a data breach. For this reason, organizations should invest more in AI to avoid waste of time and financial losses.

Threat intelligence, AI, and machine learning can identify trends in data to help security systems learn from the past. In addition, AI and machine learning enable companies to reduce incident response times and comply with security best practices.

AI and ML Application Areas

  • Anti-fraud and identify management
  • Mobile Security
  • Predictive Intelligence
  • Behavioural analytics and anomaly detection
  • Automated Security
  • Cyber-risk management
  • App Security
  • IoT Security
  • Deception Security

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