AI_in_Cloud_IoT_and_Endpoint_Security_Updated.pptx

IoT AI endpoint

1. Artificial Intelligence (AI) in Cloud, IoT, and Endpoint Security M.Sc. Electronics (NEP 2.0) Semester IV - 2025 By: [Your Name]

2. 1. Introduction to AI in Security • AI enhances cybersecurity by automating threat detection and response. • It analyzes large data sets to identify anomalies, malware, and attacks. • AI technologies include machine learning (ML), deep learning, and neural networks. • Goal: Strengthen defense mechanisms across cloud, IoT, and endpoint systems.

3. 2. AI in Cloud Security • Cloud systems store vast data and need continuous protection. • AI improves threat detection, anomaly identification, and access management. • AI tools monitor user behavior and detect unauthorized access in real-time. • Examples: AWS GuardDuty, Microsoft Defender for Cloud, Google Security AI.

4. 3. AI in IoT Security • IoT devices often lack strong in-built security measures. • AI secures IoT networks through device behavior analysis and pattern recognition. • Edge AI enables real-time decision-making at IoT endpoints. • Applications: Smart cities, healthcare, smart grids, industrial automation.

5. 4. AI in Endpoint Security • AI protects endpoints like laptops, mobiles, and IoT devices from evolving threats. • AI-driven Endpoint Detection and Response (EDR) systems track abnormal behavior. • AI-based antivirus software learns and adapts to new threats. • Examples: CrowdStrike Falcon, McAfee Endpoint Security, Sophos Intercept X.

6. 5. Integration of AI with Cloud and IoT • AI provides intelligence and decision-making for IoT and Cloud environments. • Cloud offers computational power and storage for AI models. • IoT devices collect data, AI analyzes it, and Cloud stores/manages it. • Example: AI-driven smart city infrastructure combining IoT sensors and cloud analytics.

7. 6. Challenges and Limitations of AI in Security • High computational and resource requirements. • Data privacy and ethical concerns. • Risk of false positives and adversarial AI attacks. • Need for skilled personnel to manage AI-based systems. • Dependency on quality and quantity of training data.

8. 7. Best Practices for Implementing AI in Security • Use diverse and high-quality datasets for training AI models. • Implement continuous learning and model updates. • Combine AI with human oversight for decision validation. • Ensure strong data encryption and privacy compliance. • Deploy explainable AI (XAI) for transparent decision-making.

9. 8. Conclusion and Future Directions • AI revolutionizes cybersecurity by making it proactive and adaptive. • Integration with Cloud and IoT creates a unified, intelligent security ecosystem. • Future trends: Quantum AI, Zero Trust Security, and Self-healing Networks. • AI will continue to evolve as the foundation of next-generation cybersecurity.