Part 2 will discuss Artificial Intelligence, Network Detection & Response, and Threat Identification.

Artificial Intelligence (AI)

AI (artificial intelligence) will be used for predictive methods for recognizing unauthorized activities occurring inside a PC or network. Thus, Intrusion Detection Systems will defend against attacks delivered via spam or phishing. Two-factor authentication systems will also help in prevention. AI will also be used for automated vulnerability testing, which is also called “fuzzing,” to identify weak points in a system.

AI will also be used for preventing cybercrime in the communications and social media arenas. New emerging scams that involve manipulated digital content like deepfake video and audio will present challenges to organizations.  

Network Detection & Response (NDR)

Utilizing NDR, cybersecurity teams look for dataflow anomalies. One example is the use of massive data transfers by hackers to conceal the introduction of viruses. Inventive cybercriminals are constantly developing new ways to breach the IT systems of companies and steal data. NDR helps detect such attempts and prevent the serious economic and reputational harm an organization would suffer from a serious data breach.

Threat Identification

Threat identification capability is one of the benefits that arose from the use of predictive analytics in cybersecurity. AI’s data processing capabilities enable complex methods that allow the detection and identification of threats that are delivered through a variety of channels, including dubious IP addresses, malevolent programming, and viruses.

Specific types of cyberattacks can now be anticipated by using predictive analytics, which utilizes data to forecast how and when they will occur. Potential defensive responses that will likely result in a successful outcome are considered and recommended by processing the same data and utilizing algorithms.

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