Impact of Artificial Intelligence on Cybersecurity
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Impact on AI on Cybersecurity
![The Impact of AI on Cybersecurity
• How Artificial Intelligence is Shaping the
Future of Cyber Defense
• Presented by: [Your Name]
• Date: [Presentation Date]](https://image.slidesharecdn.com/impactofaioncybersecurity-250417212228-e1ad2f8f/85/Impact-of-Artificial-Intelligence-on-Cybersecurity-1-320.jpg)
Impact of Artificial Intelligence on Cybersecurity
- 1. The Impact of AI on Cybersecurity • How Artificial Intelligence is Shaping the Future of Cyber Defense • Presented by: [Your Name] • Date: [Presentation Date]
- 2. Introduction • - What is AI? • • Machine learning, natural language processing, and intelligent automation. • - Why AI in cybersecurity? • • Rising threats, need for real-time response, massive data processing.
- 3. The Cybersecurity Landscape Today • - Constantly evolving threats: malware, ransomware, phishing. • - Human limitations in analyzing and responding to threats at scale. • - Growing attack surface: IoT, cloud, remote work.
- 4. AI Applications in Cybersecurity • - Threat detection and prediction • - Behavioral analytics • - Automated incident response • - Vulnerability management
- 5. AI in Threat Detection • - Analyzes patterns to identify anomalies • - Flags zero-day exploits • - Real-time scanning of network traffic and logs
- 6. Behavioral Analytics • - Learns normal user behavior • - Flags suspicious activities (e.g., login from odd location/time) • - Prevents insider threats
- 7. AI for Automated Response • - Immediate isolation of threats • - Auto-patching vulnerabilities • - Alerts and suggestions for human analysts
- 8. Benefits of AI in Cybersecurity • - Faster response time • - Reduced false positives • - Scalability across large environments • - Continuous learning and adaptation
- 9. Challenges & Risks • - Adversarial AI (hackers using AI too) • - Data privacy and ethical concerns • - Over-reliance on automation • - Need for skilled professionals to manage AI systems
- 10. Case Studies • - Darktrace: AI-powered anomaly detection • - IBM Watson for Cyber Security: Cognitive threat analysis • - Google Chronicle: Cloud-native threat hunting
- 11. The Future of AI in Cybersecurity • - Integration with quantum computing • - AI-assisted ethical hacking (red teaming) • - Federated learning for secure AI collaboration
- 12. Conclusion • - AI is a double-edged sword in cybersecurity • - Offers major advantages in speed and accuracy • - Must be paired with human intelligence and ethical oversight
- 13. Q&A • - Open the floor for questions • - Encourage discussion on ethical and practical concerns