By Neelesh KripalaniChief Technology Officer, Clover Infotech
In this increasingly digital world, cybersecurity has become a major concern. As cyberattacks increase in volume and complexity, traditional defense systems are no longer sufficient to protect sensitive information and infrastructure.
To meet this challenge, cyber security experts are turning to artificial intelligence (AI) as a powerful tool to strengthen defense systems and guard 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, growing at a CAGR of 23.3% in during the forecast period.
Here are some ways AI can be leveraged to improve the cybersecurity landscape:
AI in cybersecurity
Identification of cyber threats
AI can analyze massive amounts of data from a variety of sources, including network traffic, system logs, and user behavior, to identify patterns and anomalies that may indicate cyber threats. Machine learning models can be trained to recognize familiar cyberattacks and also adapt to new and evolving attack techniques.
Continuous monitoring
There is no doubt that AI models are more sophisticated than traditional systems because they can continually learn and adapt. AI-based systems are trained on large data sets that encompass various threat scenarios and behaviors, allowing them to improve their detection capabilities over time.
Security log analysis
AI significantly improves security log analysis by automating the processing and analysis of various log data, facilitating real-time threat detection, anomaly identification, and pattern recognition. It reduces false positives, facilitates behavioral profiling, and supports proactive threat hunting.
By providing scalable and efficient analysis of large volumes of data, AI enables early warnings, incident response automation and advanced analytics. However, human expertise remains vital for validating alerts, investigating incidents and making critical decisions, while ethical considerations and biases of AI models must be carefully considered 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 provide complete visibility into network activities, facilitating proactive threat hunting and helping cybersecurity teams effectively investigate and mitigate incidents.
NDR systems leverage machine learning for anomaly detection and user behavior analysis, improving detection of insider threats and advanced attacks.
Extended Detection and Response (XDR)
XDR is a cybersecurity approach that extends 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.
By correlating and analyzing data from multiple sources, XDR aims to improve threat detection, investigation and response capabilities.
In conclusion, the use of AI in cybersecurity aligns well with organizations’ need to create a preventative and proactive security environment.
AI-powered solutions can provide more effective protection against known and unknown threats, using machine learning and natural language processing algorithms to detect and respond to threats in real time. This helps organizations better protect their sensitive data and critical systems.
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