With the ever-increasing volume and complexity of data being generated in the digital age, ensuring its security has become a paramount concern. Traditional data security methods are often time-consuming, resource-intensive, and limited in adaptability to evolving threats. This paper presents an efficient framework for securing data using machine learning-based clustering algorithms. The proposed framework leverages the power of clustering algorithms to categorize data into distinct groups, allowing for targeted application of security measures. By employing machine learning techniques, the framework adapts to dynamic data patterns, providing robust protection against potential breaches and attacks. The evaluation of the framework demonstrates its effectiveness in achieving enhanced data security while optimizing computational resources. Our findings indicate that the use of machine learning-based clustering algorithms can significantly improve data protection and contribute to a more resilient data security ecosystem in the era of big data and advanced cyber threats.

data security, machine learning, clustering algorithms, data protection, cyber threats, big data, efficient framework