AI & Cyber Forensics: How Does AI Contribute to Digital Forensics?


Artificial intelligence involves a simulation of human intelligence in machines that have been programmed to think and act like us and copy our actions.

Machine learning is often viewed as a subset of artificial intelligence. It is the study of computer algorithms that have the ability to improve with experience. It enables software to make accurate predictions of outcomes without necessarily being programmed to do so.

AI and ML play an important role in digital forensics. With these two tools, investigators are capable of automating their processes so that content and insights are quickly flagged and therefore take less time to uncover.


What is Digital Forensics ?

Digital Forensics is a discipline that incorporates aspects of law and computer science to access and analyze data from networks, computer systems, and storage devices which can then be admissible as digital evidence. This evidence can be used during investigations by an organization or court of law.


The Challenges for Digital Forensics

According to recent cybercrime statistics, cyber-attacks are rapidly on the rise. While digital forensics can be a useful tool in dealing with this, this science does not come without its challenges. Here are a few of them:

  1.   Large volumes of data. Factors such as obtaining, storing, and processing of large amounts of data can cause issues that make it hard for forensic investigators to identify, capture and store criminal records.
  2.   Complex processing. Nowadays evidence is scattered in various physical and virtual locations. These can include online social platforms, the cloud, personal networks, and storage units. Forensic investigators often spend extra time, tools, expertise, and resources to work through these sources.
  3.   Legitimacy issues. There are several ambiguities and issues relating to digital forensics and cybersecurity laws. This is because some countries lack laws and standards for forensic examinations.
  4.   Privacy violation. Acquiring and reconstructing information to identify cybercrime can at times violate the privacy of users, which can be a moral and legal challenge.
  5.   New “anti-forensic” trends. New methods that work against forensics techniques have emerged almost as quickly as digital forensics itself. This thwarts the efforts of forensic scientists. The trends include encryption, cloaking, and information hiding. This can highly compromise digital forensics.


How Can AI Help Digital Forensic Specialists?

Using AI technology increases the chances of identifying and investigating cybercrimes. This helps forensic specialists get to the root cause quickly and efficiently.

AI helps to solve a crime promptly and saves investigators a lot of expenses. This will enable them to focus more on where cybercrime is likely to occur. AI can detect suspicious and criminal activities by sifting through unstructured data that investigators collect.

AI provides cognitive-data analytics which makes it possible to digest and analyze data without much fuss.

It can also enable investigators to easily look through criminal records and identify potential suspects.


Contribution of AI in Digital Forensics

AI can help in the identification of certain elements in photos and videos which are under investigation.

AI also helps in the observation of commonalities in communication, location, as well as time. This enables investigators to identify where the next crime or incident will occur.


Here are specific AI methods that also impact digital forensics:
  1. Knowledge representation

This has to do with what a computer program requires so that it intelligently performs tasks, and also how computational methods can feed this knowledge to the program. It can be utilized to come up with better solutions to keep up with cyber-attacks.

  1. Expert Systems

These explain the reasons behind certain processes, and the conclusions obtained during the digital forensics process. It allows an individual to analyze and critique the process and logic used. This can expose flaws in how conclusions are obtained. They also speed up data analysis.

  1. Pattern recognition

This identifies certain types of clusters of data in an investigation.  It can help determine picture contents, spam emails, and recognize folders in hard drives that contain questionable files. When paired with knowledge discovery it can allow analysts to detect patterns in huge amounts of data.


Where Does AI Fall Short?

Changes in knowledge representation

Where certain vital information goes missing, it can affect forensic results and generate inaccurate conclusions during investigations. The remedy for this can be to add new knowledge-data, although this may be time-consuming.

Limits of AI systems

It’s impossible to use Expert Systems for large amounts of data. On the other hand, Pattern Recognition solutions can generate vast numbers of false positives and false negatives. They are computationally intensive and can cause a burden on computing resources.


Current Problems
  1.   Scalability – This refers to issues around volumes of data in digital forensics. The volume and complexity of data are on the rise, which can create problems for investigators.
  2.   Data encryption and hiding. This issue has cropped up to create a problem for investigators who require the hidden data to gather evidence.
  3.   Evidence Validation – Nowadays the authenticity and methods used to collect criminal evidence in digital forensics are often being called to question and vehemently opposed in courts.
  4.   Lack of hardware equipment – Equipment used in digital forensics is usually expensive and hard to come by and this creates a problem.


Some AI Techniques That Can Help in Digital Forensics
  1.   Live forensics – These identify, limit, and eliminate threats on the spot and enable one to plan on tackling the threat. While at this it’s important to look through criminal records to determine who might be responsible.
  2.   Data recovery – This is the restoration of data that was deleted or destroyed. This is an effective technique of recovering potential evidence in digital forensics.
  3.   Password recovery – This comes in handy during investigations where there are password protected files. It enables access to these files which can be evidence.
  4.   Known File Filtering – This allows investigators to locate files that are relevant to an investigation.
  5.   Timeline Analysis – This tells investigators the order of events that led to the event under investigation.


In light of the above, we can see all the different ways AI can simplify the life of a forensic analyst.  It eliminates the need to look through multiple data sources during investigations, thus saving them time and energy. If used properly, it can simplify the investigation process and enable investigators to acquire essential evidence.



Ben Hartwig is a web operations director at InfoTracer. He authors guides on marketing and entire cybersecurity posture and enjoys sharing the best practices. You can contact the author via LinkedIn