AI is Ready, But Is Digital Forensics Ready?
AI Is Ready. Is Digital Forensics Practice Ready?
The pace of AI development is truly remarkable. Not long ago, AI was mostly discussed as a supplementary tool that could assist with security work. However, the atmosphere has changed significantly. AI has moved beyond simply organizing text or summarizing complex information. It now feels capable of supporting real-world work by offering perspectives we might otherwise miss and assisting with judgment in practical workflows.
The security field is no exception. AI is influencing both attackers and defenders. Attackers can search for information faster, attempt attacks more easily, and operate against a larger number of targets. At the same time, defenders are also trying to use AI to improve the efficiency of detection, response, and analysis.
Amid these changes, incident cases are becoming more complex, and the amount of data that needs to be analyzed continues to grow. In this environment, how should digital forensic analysts view AI?
I do not believe AI can completely replace digital forensic analysts. However, I do believe it has already reached a level where it can assist analysts. It can help reduce repetitive documentation work, quickly review complex information, and provide useful perspectives during the analysis process.
But this is where a realistic concern begins to emerge. The real issue is not AI’s capability.
In digital forensics work related to incident response, there is a more important question.
Can we show real incident data to AI?
For digital forensic analysts, this question is not easy to answer. Regardless of how advanced AI may be, the nature of the data we handle is very different from ordinary business data.
Being Able to Use AI Is Not the Same as Being Allowed to Use It
As AI continues to evolve, its potential use in many areas of work is being discussed. For example, in software development, there are already discussions about using AI to review company source code or identify errors. Of course, even in that case, security policies and internal regulations are necessary. However, source code is at least fundamentally an asset of the company. If the company permits it and the proper conditions are in place, the organization can decide whether AI may be used.
Incident response and digital forensics are different.
The data handled by digital forensic analysts is not limited to a customer’s files or server information. A compromised system may contain not only the customer organization’s business information, but also information belonging to that customer’s own customers. Personal information, access records, service usage history, internal account information, business documents, and traces left during the incident may all be mixed together.
For example, consider an incident involving the leakage of customer information. An analyst needs to examine the affected server. They need to determine what was leaked, when the issue began, and through which path access occurred. But what happens if the data collected during that process is entered into an AI system?
This may not be a matter that ends simply with the customer organization’s permission. The data may contain information about the customer organization’s customers—in other words, third-party information. The customer organization may agree to provide materials for analysis, but whether information belonging to third parties can also be entered into AI is a separate issue.
Ultimately, not all incident data can be transferred to an external processing environment simply because it is needed for analysis. This is where digital forensic analysts face a concern that is different from ordinary AI usage. How smart AI is, how fast its results are, and how convenient it is all come later. Before that, we must first decide whether the data can be shown to AI at all.
Incident Data Is Not Just Ordinary Material
Incident data is different from ordinary business material. It contains clues needed to identify the cause of an incident and determine the scope of damage. At the same time, it may also include the customer organization’s internal information, customers’ personal information, the organization’s security posture, vulnerable points, and traces left by the attacker.
To an analyst, this may be necessary material. From another perspective, however, it is highly sensitive information. In particular, individual pieces of incident data may look like simple logs or file paths when viewed separately. But once multiple pieces of information are connected, they can reveal the broader context of the incident.
What makes this even more difficult is that it is not easy to determine what must be removed in order to make the data safe. Removing visible values such as personal information does not necessarily make incident data safe. The flow of the incident, internal structure, access paths, and the names or locations of specific systems may also be sensitive depending on the situation.
For example, an attacker’s payload may not contain actual personal information values. However, table names, column names, and query conditions included in the payload may still allow us to infer what kind of data was targeted. For an analyst, this is an important clue. But if exposed externally, it could reveal the customer organization’s data structure or vulnerable points.
In the end, incident data cannot simply be viewed as “input for analysis.” It is the context of the incident, evidence, someone’s information, and material directly connected to the customer organization’s trust.
That is why using AI in digital forensics should be viewed differently from adopting a general productivity tool. Before asking whether AI can help, we must first consider what data we would need to show AI in order to receive that help.
If AI Performs the Analysis, How Far Can We Trust the Results?
Another concern is the analysis results produced by AI.
In digital forensics, the result alone is not what matters. Analysts must be able to explain why they reached a particular conclusion. They must be able to state what data they reviewed, what process they followed, and what evidence led them to their conclusion. Incident analysis results may influence a customer organization’s response strategy and may even lead to legal disputes or responsibility assessments.
But what if AI summarized an important part of the analysis?
How can we verify whether AI’s result is correct? How can we confirm that AI has not created something that does not exist but sounds plausible? If we review the same material again, can we reach the same conclusion? And if the result must be explained in a report or during a dispute, how much responsibility can the analyst take for it?
Of course, analysts should never blindly trust and use AI-generated results. However, in practice, the more plausible AI’s output appears, the more careful we must be. Just because the writing is natural and the explanation is smooth does not mean it is a verified fact.
Ultimately, what matters in digital forensics is not a plausible interpretation, but verifiable evidence. AI can be used as a reference, but it cannot replace final judgment. Even if AI’s output appears useful, the analyst must return to the original data, verify whether it matches the actual evidence, and reach a conclusion based on their own judgment.
From this perspective, AI may reduce some of the work performed by digital forensic analysts, but it also creates a new responsibility for verification. Using AI does not simply mean that the workload decreases. It also means that we need new standards for how to verify AI-generated results and how far we should accept them.
Questions We Must Ask Before the Technology
The technical potential for using AI in digital forensics has already grown significantly. However, the questions we face in actual practice are not only technical ones.
Can this data be shown to AI? How should third-party information contained within it be protected? Is the customer organization’s consent enough? How far can we rely on AI-generated results? Who is responsible if those results are wrong? Can the analyst explain the reasoning process behind the judgment?
These questions are different from simply knowing how to use a tool well. They arise because of the nature of the data handled by digital forensic analysts and the weight carried by analysis results.
That is why I believe the key question for digital forensic analysts in the age of AI is not “Can we use AI?” but rather “How far are we allowed to use AI?” AI can certainly be helpful. Even if it cannot fully replace analysts, it can become a tool that supports them in many parts of their work.
However, in real-world incident response and digital forensics, there are still problems that technology alone cannot solve. These include the sensitivity of incident data, the protection of third-party information, the reliability and explainability of analysis results, and the issue of responsibility. If we focus only on AI adoption without carefully considering these issues, we may end up creating new risks while trying to improve analysis efficiency.
In the end, I do not think the answer is to reject AI entirely or to adopt it blindly. What we need is a realistic set of standards based on an understanding of digital forensics practice: what data must never be entered, what tasks can be supported by AI, and how AI-generated results should be verified.
AI may be ready. But digital forensics practice still seems to be standing in front of many unanswered questions.
How far should digital forensic analysts use AI in this era? And where should we draw the line?
What do you think?