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AI-Powered Incident Response (CY0-001) Flashcards
CompTIA SecAI+ CY0-001 Flashcards
| Front | Back |
| How can AI assist in compliance auditing | By automating checks for adherence to regulatory standards and detecting deviations. |
| How can AI facilitate better security training | By generating simulated attack scenarios to help teams practice effective incident response. |
| How can AI help with insider threat detection | By monitoring user behaviors and identifying deviations that may indicate malicious activity. |
| How can AI reduce response time in cybersecurity | By automating repetitive tasks and providing real-time analysis of threats. |
| How do neural networks enhance threat detection | They process complex data patterns to identify subtle or hidden security threats. |
| How does AI contribute to threat hunting | AI tools can scan networks and systems proactively to identify vulnerabilities or suspicious activity. |
| How does AI handle unstructured data | By using algorithms to analyze and extract patterns or meaning from data such as text, images, or logs. |
| How does AI improve scalability in security operations | By handling large volumes of data and incidents simultaneously, reducing manual effort. |
| How does AI improve threat detection | AI analyzes large datasets to identify patterns and anomalies indicative of potential threats. |
| How does AI integrate with endpoint detection and response (EDR) | By enhancing real-time monitoring and protection of end-user devices against threats. |
| How does AI support post-incident analysis | By generating reports and insights to understand threats and improve future responses. |
| How does AI-powered remediation work | AI suggests or automatically implements solutions to resolve detected security issues. |
| How does deep learning contribute to cybersecurity | It enables advanced pattern recognition and prediction through multi-layered neural network systems. |
| How does unsupervised learning aid incident response | It detects patterns or anomalies in data without predefined labels by clustering or recognizing outliers. |
| Name a key challenge of implementing AI in cybersecurity | Ensuring data privacy and reducing false positives or negatives in threat detection. |
| Name one advantage of using AI in cybersecurity | AI provides faster and more accurate threat detection compared to manual processes. |
| What are AI-driven deception techniques | Deploying decoys or honeypots to mislead attackers and gather intelligence about attack methods. |
| What challenges arise with algorithmic bias in AI-powered security | It can lead to inaccurate threat detection or prioritization if the training data is unbalanced. |
| What does real-time monitoring mean in AI incident response | Continuous tracking of systems and activities to detect and respond to threats instantly. |
| What is a key benefit of AI-driven forensics | Automating the analysis of logs and system data to trace the origin and impact of a security breach. |
| What is adversarial machine learning in cybersecurity | Techniques where attackers exploit AI models by feeding deceptive inputs to bypass detection. |
| What is automated triage in incident response | The process of prioritizing incidents based on severity using AI algorithms. |
| What is contextual analysis in incident response | Evaluating the environment or situational factors around a threat to improve response accuracy. |
| What is incident response | The process of identifying, managing, and addressing security breaches or cyber threats. |
| What is machine learning's role in incident response | It enables systems to learn and improve over time by analyzing data and refining models for better detection and response. |
| What is natural language processing (NLP) in incident response | NLP analyzes text-based alerts or logs to extract actionable intelligence for threat responses. |
| What is phishing detection with AI | Using AI to analyze emails or messages for signs of phishing attempts, such as suspicious URLs or language patterns. |
| What is predictive analytics | Using AI to forecast potential security threats based on historical data and trends. |
| What is proactive defense in AI-powered security | Detecting and mitigating threats before they can impact systems or data. |
| What is sentiment analysis in security operations | The use of AI to gauge the urgency or severity of incidents from communication logs, texts, or reports. |
| What is SOAR in cybersecurity | Security Orchestration, Automation, and Response—a framework enhanced by AI for efficient incident management. |
| What is supervised learning in AI incident response | A machine learning approach where AI models are trained on labeled datasets to identify and respond to threats. |
| What is the importance of explainability in AI incident response | Making AI decisions transparent to improve trust and identify errors in threat analysis. |
| What is the role of anomaly detection in AI-powered security | Identifying deviations from normal behavior that may indicate a security threat. |
| What is the role of reinforcement learning in AI-powered security | AI systems learn from trial and error to optimize responses to threats and improve decision-making. |
| What is the significance of continuous learning in AI systems | It ensures AI models adapt to new threats and improve over time. |
| What is zero-day attack detection with AI | Using machine learning to identify unusual behavior indicative of new or unknown attacks. |
| Why is data important for AI in incident response | AI relies on large datasets to train models and improve its accuracy in detecting and addressing threats. |
| Why is multi-modal data analysis important in AI security | Combining data from various sources like text, images, and logs for holistic threat assessment. |
This deck provides an overview of AI applications in incident response, covering automation in threat detection, triage, and remediation processes.