Artificial Intelligence is used in Cybersecurity to automate repetitive and time-consuming tasks, allowing security analysts to focus on more complex and strategic issues.

Social engineering attacks rely on human interaction and manipulation, and are not typically automated using Artificial Intelligence.

Machine Learning algorithms can analyze large amounts of data and identify patterns and anomalies, allowing them to detect and respond to new threats without the need for manual updates or intervention.

Artificial Intelligence in Cybersecurity faces challenges such as high implementation and maintenance costs, a shortage of skilled professionals, and the potential for bias and discrimination in AI algorithms.

Malicious AI refers to the use of Artificial Intelligence techniques to create software or systems that are designed to attack or exploit computer systems for malicious purposes.

Adversarial Machine Learning involves creating inputs or examples that are designed to fool or mislead AI algorithms, making them less effective in carrying out attacks.

Artificial Intelligence is used in threat intelligence to collect and analyze large volumes of security data from various sources, helping security analysts identify and prioritize threats.

While Artificial Intelligence can assist security analysts and improve their efficiency, it does not eliminate the need for human expertise and judgment in Cybersecurity.

Machine Learning is the broader term that encompasses various techniques that allow AI systems to learn from data and improve their performance over time.

Decision Trees, Support Vector Machines, and Naive Bayes are all common types of Machine Learning algorithms used in Cybersecurity for tasks such as malware detection, intrusion detection, and anomaly detection.

Supervised Learning is a Machine Learning technique where the model is trained on labeled data, meaning that the data is already classified into different categories.

Artificial Intelligence in Cybersecurity faces challenges such as the lack of interpretability and explainability of AI models, the potential for bias and discrimination, and the high cost of implementation and maintenance.

Cyber Threat Intelligence, Attacker Profiling, and Behavioral Analytics are all terms used to describe the use of Artificial Intelligence to analyze and understand the behavior of attackers and their motivations.

Artificial Intelligence is used in Cybersecurity incident response to automate the analysis of security logs and alerts, identify and prioritize security incidents, and recommend appropriate response actions.

Data Privacy, Data Protection, and Data Security are all terms used to describe the use of Artificial Intelligence to protect sensitive data and information from unauthorized access or disclosure.