Harsh Panchal

Evidence Analysis plays a valuable role in any investigation carried out for individual or organizations post criminal activities, leveraging AI/ML extensively which helps in automating repetitive and laborious tasks aiding investigators to focus on the significant course of action. AI/ML came up for significant transformations to the field by various techniques for simplification. Advancements allowed experts to dedicate time and other resources to critical aspects of investigation instead. Modern methodologies offers solutions to variegated domains of digital forensics, such as network analysis, device forensics, cybercrime investigations and many more. This paper gives you an overview of how AI and ML techniques can be implemented in digital forensics, by use of different approaches. For example, NLP can be used to analyse large volume of text data, extracting information or identifying patterns. AI driven image and video surveillance tools can detect anomalies, recognize faces or analyse patterns in recordings of live feed. Pattern recognition helps in identifying recurring events or correlations in evidence, like tracing data or detecting frauds in or during cyberattacks. All these benefits come with challenges including data quality problems, bias in algorithm and limitations in complex dataset handling. Inefficiencies in interpreting results due to the black box nature of some AI models that lack the ability of explanation are also encountered by experts. To address these challenges, adopting to advanced algorithms, integration of human expertise with AI tools and ensuring continuous learning to refine models in required. AI's computational power when combined with human insight creates a robust framework for digital forensics.