This article explores the applications of Generative AI in cybersecurity, highlighting its potential for enhancing threat detection, incident response, malware analysis, and more. It examines the concept of Agentic AI, which employs large language models (LLMs) to autonomously tackle complex cybersecurity tasks. Despite these advancements, the article addresses significant challenges in implementing Generative AI solutions, such as data quality, hallucinations, and integration complexities. Affected: cybersecurity sector
Keypoints :
- Generative AI serves multiple roles in cybersecurity, aiding both defensive and offensive strategies.
- Applications include threat detection, automated incident response, malware analysis, and phishing detection.
- Agentic AI is capable of executing multi-step tasks autonomously, providing a more sophisticated approach to cybersecurity challenges.
- AI can improve security audits, pentesting, and social engineering defenses.
- Challenges in deploying Generative AI include data quality, reliability, and real-time performance issues.
- The need for explainability and trust in AI decisions is crucial in regulated environments.
- AI systems can be vulnerable to adversarial attacks and require robust guardrails for ethical use.
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