Module 2

Question 1

What is AI?

Answer 1

AI is the simulation of human intelligence processes by machines, particularly computer systems. It typically requires human intelligence, such as learning, reasoning, and problem solving.

Question 2

What is ML?

Answer 2

ML is a subset of AI that enables computer algorithms to learn from data and then make decisions or predictions about figure data without explicit instructions from programmers.

Question 3

Types of AI

Answer 3

Narrow AI & General AI

Question 4

What can general AI do?

Answer 4

Capable of performing any intellectual task that a human can do.

Performs general tasks with little to no oversight from a user.

Can transfer knowledge from one domain to another.

Question 5

What can Narrow AI do?

Answer 5

Designed to perform specific tasks within a limited context.

Performs specific tasks as instructed by a user.

Unable to transfer knowledge across domains.

Simulates human consciousness but is not conscious.

Question 6

Importance of AI/ML in Cybersecurity.

Answer 6

Analyzing vast amounts of data.

Applications in threat detection.

Malware analysis

Fraud prevention

Enhancing real-time response

Automating routine tasks

Predictive capabilities

Question 7

Bias and Ethics in AI/ML

Answer 7

Use AI for data analysis

Generate original insights

Cross-verify info

Draft with care

Question 8

Bias in AI

Answer 8

Bias in AI can occur with AI systems make decisions that reflect human prejudice, leading to unfair or discriminatory outcomes.

Question 9

Biased training data

Answer 9

Data used to train AI systems may contain inherent biases, which can skew the AI’s decision-making process.

Question 10

Biased human decisions

Answer 10

Human choices in designing and implementing AI can lead to biased systems if not carefully managed.

Question 11

AI/ML in Data Analysis

Answer 11

AI and ML have revolutionized the way we handle data, making in possible to analyze vast datasets quicky and accurately.

Question 12

Importance of Data in AI/ML

Answer 12

Data is the foundation of AI/ML. The quality and quantity of data directly impact the performance of AI models.

Question 13

Pattern Recognition

Answer 13

AI/ML models excel at identifying regular patterns in data. AI can learn the typical behavior of users on a network, such as login times, access patterns, and commonly used applications.

Question 14

Anomaly detection

Answer 14

AI/ML models are also adept and detecting deviations from normal patterns. These anomalies often indicate potential security threats, such as cyber attacks or fraudulent activities.

Question 15

Intrusion detection systems (IDS)

Answer 15

AI/ML models analyze network traffic to detect suspicious activities. These systems can differentiate between normal and potentially harmful traffic alerting to security teams to investigate further.

Question 16

Malware detection

Answer 16

AI/ML algorithms identify malicious software by recognizing known patterns of behavior or code. These systems can detect new and evolving malware strains that traditional signature-based detection might miss.

Question 17

Fraud detection

Answer 17

AI/ML systems monitor transactions to identify unusual activities that may indicate fraud. These systems can adapt to new fraud patterns as they emerge, providing ongoing protection.

Question 18

Benefits of AI/ML in Data Analysis

Answer 18

Speed, Accuracy, Scalability, Adaptability, and Proactive threat hunting.

Question 19

Automation and Efficiency

Answer 19

AI helps automate and improve the efficiency of security operations. By automating routine tasks, AI allows security professionals to focus on more complex issues, thus enhancing overall operational efficiency.

Question 20

Real-time analysis

Answer 20

One of the major advantages of AI in cybersecurity is it’s ability to perform real-time analysis. AI systems can quicky identify threats compared to traditional methods, which might take much longer to process and analyze data.

Question 21

Managing data volumes

Answer 21

AI integration helps manage this vast amount of data effectively, enabling organization to sift through it quickly to find relevant security threats and patterns.