ML and AI in Security Threat Reduction
Cybercrime is not only jeopardizing the technology circle; it also disturbs every individual's life. The WannaCry ransomware attack in May 2017 stands a proof for the above statement. With the increase in the volume of personal and business data, rises the threat of data breaches. Cybercriminals are leveraging advanced methods to exploit the flaws within an organization leading to a security breach. This has induced the acceptance of several technologies in ensuring security.
With the ability to analyze and derive useful insights from a heap of data used in business processes, Artificial Intelligence (AI) is playing a major role in threat mitigation. In order to gather, organize and monitor customer data, many big shot companies are leveraging cognitive tools in the process of data handling and cybersecurity. A research conducted by Wakefield Research and Webroot—a cybersecurity vendor—states that almost 99 percent of the organizations believe in AI to enhance their cybersecurity operation and out of which 87 percent of them have already deployed AI.
AI is never a substitute to security analyst; rather it saves their time by eliminating risk generating factors and human errors. Apart from this, they anticipate and proactively identify attacks to remove threats. AI in behavioral analysis can effectively structure and organize data. It identifies the fraud pattern and produces an accurate threat defense.
Consequently, Machine Learning (ML) in cybersecurity has gained all the attention with its ability to provide efficient and distinct solutions keeping pace with the ever-evolving risk landscape. ML identifies any suspicious activity and passes it to the security analysts for examination whereas in certain cases, advanced ML can respond itself by restricting access to certain users.
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