Artificial intelligence techniques for enhancing accuracy and efficiency in digital forensic analysis

Abstract In the digital age, the proliferation and complexity of data present significant challenges for digital forensic analysis. Traditional tools often struggle to keep pace with the volume and sophistication of data, leading to delays in detecting illicit activity. This study addresses these challenges by integrating advanced artificial intelligence techniques, which overcome these limitations and enhance the effectiveness and efficiency of digital forensics. We apply specific artificial intelligence methods, such as convolutional neural networks, supervised machine learning algorithms, and natural language processing techniques, to optimize data processing and analysis in forensic investigations. Our findings demonstrate accelerated data analysis, improved precision, and greater capacity to handle large volumes of information. For instance, convolutional neural networks achieved a remarkable 92% precision in identifying image patterns, while natural language processing techniques achieved 88% precision in extracting relevant text information. This research highlights the transformative potential of artificial intelligence in digital forensics, offering faster and more accurate solutions that are crucial for cybersecurity.