Cybercrime Investigations in the Era of Artificial Intelligence: Automated Evidence Collection and Analysis through the Use of Machine Learning

Artificial Intelligence (AI) and machine learning (ML) technologies have progressed rapidly and transformed many areas such as law enforcement and cybersecurity. AI-based systems can be applied in a variety of areas related to cybercrime investigation, such as improving the speed and accuracy of evidence collection, analysis, and forensic investigations. This study employs machine learning-based algorithms in order to automate evidence gathering and evaluation processes in cybercrime investigations. Cyber Crime data investigation is not easy task it requires many expert resources, and lot of experience and traditional ways of cyber-crime investigation does not always work due to large amount of data, these data fluctuating, require real-time data insights, encrypted chat dumping is forensic nature etc. But AI-driven tools can automate these functions, allowing for wider detection and analysis of digital trails, bad behavior, and anomalies in the data. With NLP, anomaly detection, and predictive analytics like techniques, AI-powered systems can help recognize patterns, detect malicious actors, and aid decision-making with speed and accuracy. In this paper, we conduct an overview of current trends in AI, ML and cybersecurity, with a specific focus on the tools that allow for cybercrime investigations; detailing how AI can assist with digital forensics, decreasing time aspects for automated evidence collection and behavioral profiling techniques. Scope of AI in Cybersecurity there have been rapid recent developments in the area of AI, ML and cybersecurity, driving a new wave of tools that can address large scale data concerns. Moreover, in further demonstrating how AI can also identify potential ethical and privacy issues, the report stresses the importance of considering data governance and human oversight during AI-powered investigations. Additionally, this research analyzes different case studies and real-life usages of AI technologies in cybercrime investigations, presenting their pros and cons. So, in conclusion, machine learning will automatically improve cybercrime investigation due to its outcome in every domain since Machine learning opens up the maximum number of deterrents for forensics collection in enhancing cyber-crime investigation.