In today’s digital age, the field of digital forensics is rapidly evolving, driven by technological advancements that enhance the identification, preservation, and analysis of electronic evidence. AI and machine learning have enabled revolutionary advancements across various industries, including the field of digital forensics.
These days, all activities are conducted on devices. Just look around you and you’ll see what we mean! Everybody is on a device, creating records of their daily conversations, professional activities, and even their search history. Such information is likely to provide ample clues about a person’s personal motives or beliefs — don’t you think?
In context of digital forensics, a large amount of digital data is likely to be retrieved from digital records such as emails, text messages, search histories, and other online activities that can provide a wealth of evidence for those who know how to analyze them.
Many Forensics Departments Are Using AI
Modern AI algorithms are capable of automatically identifying and extracting relevant data from large volumes of digital evidence, including files, emails, and logs. Furthermore, machine learning can read this data to detect patterns indicating irregularities or suspicious activities.
Even when direct evidence is not available, AI can analyze the “digital footprint” of suspects or individuals involved in a crime. This includes user behavior on online messaging apps, social media platforms, shopping apps, dating apps, hotel booking apps, emails, and business-related software.
Would it be possible to conduct this type of an investigation without AI? Next to impossible as this type of data could not be processed by a person alone — or even a whole team of people! It’s too large to compute manually, and this is where AI is instrumental.
AI’s knack for spotting patterns in digital activities can be a game-changer for investigations. Even if it doesn’t provide direct clues, it can still steer things in the right direction.
AI’s capability to identify patterns and anomalies are unlike any human detection abilities. As an example: AI has the ability to correlate user activity logs from a dating app with social media interactions. It can find correlations between purchase history from a shopping app with social media interactions. These types of associations are near impossible to detect by just the human eye.
These days, AI is transforming business by providing insights into correlations between (consensually provided) consumer data across different apps. This helps us further understand what consumers really want. As someone who has used AI to uncover such correlations, it is easy to imagine how this type of information could be invaluable to crime scene investigators!
Also related to AI-powered data is Natural language processing (NLP). NLP analyzes the natural human language in a context that is more colloquial and human than mechanical or robotic.
NLP can also play a crucial role in analyzing vast amounts of textual data from emails, chat logs, reviews, and other written communications. This is since NLP conducts sentiment analysis to uncover deeper emotions, viewpoints, values, and beliefs relevant to crime scenes.
The Role of Sentiment Analysis in Crime Scene Investigations
Sentiment analysis, while commonly used in business analytics, is particularly useful in crime scene investigations.
Crimes are driven by sentiments. Often, those who try to cover up crimes may also harbour sentimental motives. As the crime fiction writer, Agatha Christie says, “Very few of us are what we seem. Every murderer is probably somebody’s old friend.” The complexities of human emotions are often hard to decipher. To uncover the deep sentiments that drive human motives, data is the only answer.
Sentiment analysis can provide valuable insights into the intense human emotions behind criminal acts. By analyzing digital communications or transcribed oral communications, investigators can determine the overall perspective of a person towards various topics, identifying whether their opinion is neutral, positive, or negative.
Sentiment analysis, using AI and machine learning along with other sophisticated software, is gaining traction across industries. Imagine going from guessing your way through purchase-histories of your customers for an idea on their preferences to having access to their very opinions and attitudes towards your product!
This type of data isn’t limited to business. In digital forensics, the analysis of sentiment using AI can lead to crucial clues about their mental state, motives, emotional triggers, and potential behavioral patterns related to a crime.
PHI Learning’s alignment with forensics and NEP 2020
The National Education Policy (NEP) 2020 aims to transform India’s educational landscape by fostering multidisciplinary learning, critical thinking, and practical applications. PHI Learning’s book “Laws of Electronic Evidence and Digital Forensics” aligns with these objectives by blending theoretical concepts with real-world applications. It is tailored for a diverse audience, including LLB and LLM students, B.Sc. and M.Sc. students in Digital Forensics and Information Security, B.Tech students in Computer Science (Cyber Security and Digital Forensics), and those pursuing PG Diplomas in Cyber Security and Digital Forensics.
This book provides a comprehensive understanding of digital forensics and its legal implications, equipping students with the knowledge and skills necessary to excel in their careers. By addressing recent technological advancements and aligning with NEP 2020’s goals, “Laws of Electronic Evidence and Digital Forensics” serves as an essential guide for students and professionals navigating the complexities of electronic evidence and digital investigations.