Autonomous AI-Driven Cyber Defense Frameworks for Secure Cloud-Based Enterprise Platforms
DOI:
https://doi.org/10.15680/IJCTECE.2023.0605015Keywords:
Autonomous cybersecurity, artificial intelligence, cloud security, machine learning, cyber defense frameworks, threat detection, enterprise platforms, anomaly detection, adaptive security, zero trust architectureAbstract
The rapid adoption of cloud-based enterprise platforms has introduced new cybersecurity challenges due to increased attack surfaces, dynamic infrastructures, and sophisticated threat actors. Traditional security mechanisms, which rely heavily on manual intervention and static rules, are insufficient to address real-time and evolving cyber threats. This research explores the design and implementation of autonomous AI-driven cyber defense frameworks that leverage machine learning, deep learning, and intelligent automation to enhance cloud security. The proposed framework integrates threat detection, response automation, behavioral analytics, and adaptive learning capabilities to ensure proactive defense against cyberattacks. By utilizing real-time data analysis and predictive modeling, the system can identify anomalies, mitigate risks, and continuously evolve with emerging threats. The study also evaluates the effectiveness, scalability, and resilience of such frameworks in enterprise cloud environments. Furthermore, it highlights the importance of integrating AI with existing security architectures while addressing challenges such as data privacy, model bias, and system complexity. The findings suggest that autonomous AI-driven cyber defense frameworks significantly improve threat detection accuracy, reduce response time, and enhance overall security posture, making them essential for modern cloud-based enterprise platforms.
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