AI/ML is the Knight to Save the Day against Cybersecurity Threats


Organizations today are looking at security tools, methods and expertise that can help catch Zero-day threats. The security tools should spot attacks by simply noting anomalous activities. With growing enterprise surface attacks, it is becoming very difficult to identify the potential threats and by the time strategies are developed to counter the same, the attacks would have caused substantial damage. On a daily basis, cybersecurity teams come across a huge number of alerts which are difficult to analyse and the enormous number of connected devices in organizations has made it even more difficult.  Artificial Intelligence (AI) along with Machine Learning (ML) based tools have come as a great help for information security teams to reduce breach risk and improve their security posture efficiently and effectively.

According to a report by Capgemini Research Institute, 61% of organizations say they will not be able to identify critical threats without AI, while 69% believe AI will be necessary to respond to cyberattacks. Indeed, the market for AI in cybersecurity is expected to grow to $46.3 billion by 2027. AL & ML are designed and developed in such a way that they continuously keep learning over time, drawing solutions from past data analysis and incident occurrences. Histories of behaviour build profiles on users, assets and networks, allowing AI to detect and respond to deviations from established norms. With this, they analyse billions of events and alert on the present and upcoming threats. It has helped in identifying malware exploiting zero-day vulnerabilities to identifying risky behaviour that might lead to a phishing attack or download of malicious code.

Artificial intelligence in cyber security is beneficial because it improves how security experts analyse, study and understand cybercrime. It enhances the cyber security technologies that companies use to combat cybercriminals and helps keep organizations and customers safe. While there are a lot of benefits that can be reaped from AI and ML, it is also very important to train them correctly with the right directional data which will help in identifying and stopping attacks and protecting sensitive organizational data.

AI/ML systems can help in the following key areas:

  • Quick deduction of anomalies and threats from its learning and analysis
  • Detecting and identifying accurate fraud-related patterns
  • AI systems can be trained to identify good bots and block malicious ones
  • AI can be used for automating the discovery of all key devices and applications
  • To manage Security alerts appropriately AI-powered systems can help with incident responses

Benefits of Artificial Intelligence and Machine Learning

The IT teams or organizations can reap the following benefits by incorporating AI & ML into their cyber security programs

Quick detection of threats and alerts: With more and more learning, the AL & ML tools become more and faster to detect, analyse and report threats. They do this in a matter of seconds which is not possible manually. What’s more, they can implement patches and remediate threats in near real-time, dramatically improving response times.

Lowering IT costs: AI and machine learning reduce the effort required to detect and respond to cyber threats, making them cost-effective technologies. The average cost reduction is 12%, with some organizations lowering their costs by more than 15%, according to the Capgemini report.

Cyber Analysts to be more effective: Cyber Analysts do not have to manually sift through the data logs, AL & ML do that for them decreeing the workload. These technologies can alert cyber analysts about an attack while classifying the type of attack—better preparing them to make the right response. Through this cyber analysts are in a better position to manage and counter the most complex threats coming their way.

Protecting the macro and micro levels of organizational infrastructure: With time AI & ML learn more and become stronger to deal with complex threats. With the combination of past learning and present situations, they become more proficient at identifying suspicious activity. They improve the overall security posture of the organization by creating more effective barriers than can be achieved using manual methods.

About the author:

Gaurav Ranade is CTO at RAH Infotech.