AI-Driven Cybersecurity

The artificial intelligence in cybersecurity market size was valued at USD 13.29 billion in 2021 and might expand at a compound annual growth rate (CAGR) of 24.3% from 2022 to 2030.

In this increasingly digitized world, cybersecurity has become a critical concern. As cyberattacks have grown in volume and complexity, traditional defense systems can no longer protect sensitive information and infrastructure. 
To combat this challenge, cybersecurity experts are turning to artificial intelligence (AI) as a powerful tool to strengthen defense systems and safeguard against cyber threats. According to a report by MarketsandMarkets, the global AI in cybersecurity market size is expected to grow from $8.8 billion in 2020 to $38.2 billion by 2026, at a CAGR of 23.3% during the forecast period.
Here are some ways in which AI can be leveraged to enhance the cybersecurity landscape: 
Cyberthreats identification - AI can analyze massive amounts of data from various sources, including network traffic, system logs, and user behavior to identify patterns and anomalies that might indicate cyber threats. Machine learning models can be trained to recognize familiar cyberattacks and adapt to new and evolving attack techniques. 
Continuous monitoring - There is no doubt that AI models are more sophisticated than traditional systems as they have the ability to learn and adapt continuously. AI-based systems are trained on vast datasets that encompass diverse threat scenarios and behaviors, enabling them to improve their detection capabilities over time.
Security log analysis - AI significantly enhances security log analysis by automating the processing and analysis of diverse log data, aiding in real-time threat detection, anomaly identification, and pattern recognition. It reduces false positives, helps in behavioral profiling, and supports proactive threat hunting. 
AI enables early warnings, incident response automation, and advanced analytics by providing scalable and efficient analysis of large volumes of data. However, human expertise remains vital for validating alerts, investigating incidents, and making critical decisions, while ethical considerations and biases in AI models must be carefully addressed during implementation.
Network detection and response (NDR) - NDR solutions detect abnormal system behavior by applying behavioral analytics to network traffic data. They continuously analyze raw network packets between internal and public networks. These solutions offer comprehensive visibility into network activities, aiding in proactive threat hunting and helping cybersecurity teams investigate and mitigate incidents effectively. NDR systems leverage machine learning for anomaly detection and user behavior analysis, enhancing the detection of insider threats and advanced attacks.
Extended detection and response (XDR) - XDR is a cybersecurity approach that expands upon the capabilities of traditional Endpoint Detection and Response (EDR) solutions. XDR integrates data from various security sources across an organization's environment, including endpoints, networks, cloud platforms, and applications, to provide a more comprehensive and contextualized view of potential threats. XDR aims to improve threat detection, investigation, and response capabilities by correlating and analyzing data from multiple sources.
In conclusion, the use of AI in cybersecurity aligns well with the organizations’ need for building a pre-emptive and proactive security landscape. AI-based solutions can provide more effective protection against both known and unknown threats – using machine learning and natural language processing algorithms to detect and respond to threats in real-time. This helps organizations to safeguard their sensitive data and critical systems better.
Neelesh Kripalani is a Chief Technology Officer, Clover Infotech
Image Source- Freepik

Add new comment