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How AI Enhances Digital Forensics

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Digital forensics professionals can use artificial intelligence to accelerate and enhance their current processes, shrinking their investigation time and improving efficiency. However, while its impact is mostly positive, some issues do exist. Can AI replace forensics analysts? More importantly, would AI-driven findings even hold up in court?

What Is Digital Forensic Science?

Digital forensic science — formerly known as computer forensics — is a branch of forensic science that deals exclusively with electronic devices. A forensic analyst’s job is to investigate cybercrimes and recover data to produce evidence.

Industry professionals use computer science and investigation techniques to uncover data on computers, phones, flash drives and tablets. They aim to find, preserve, examine and analyze data relevant to their case.

How Does Digital Forensics Work?

Digital forensic science generally follows a multi-step process.

1. Seizure

Teams must first seize the media in question from their suspect. At this point, they start a chain of custody — a chronological electronic trail — to track where the evidence is and how they use it. This step is critical if they go to trial.

2. Preservation

Investigators must preserve the original data’s integrity, so they begin their examination by making copies. They aim to decrypt or recover as much hidden or deleted information as possible. They must also secure it from unauthorized access by removing its internet connection and placing it in secure storage.

3. Analysis

Forensic examiners analyze data with various methods and tools. Since devices store information every time their user downloads something, visits a website or creates a post, a sort of electronic paper trail exits. Experts can check hard drives, metadata, data packets, network access logs or email exchanges to find, collect, and process information.

4. Reporting

Analysts must document every action they take to ensure their evidence holds up in a criminal or civil court later on. When they conclude their investigation, they report their findings — either to law enforcement agencies, the court or the company that hired them.

Who Uses Digital Forensics? 

Digital forensics investigates unlawful activity related to electronic devices, so law enforcement agencies use it often. Interestingly enough, they don’t solely pursue cybercrime. Any misconduct — whether it’s a violent crime, civil offense or white-collar crime — that may be connected to a phone, computer or flash drive is fair game.

Businesses often hire forensic analysts after experiencing a data breach or becoming cybercrime victims. Considering ransomware attacks can cost over 30% of an organization’s operating income, it’s not uncommon for leaders to hire expert investigators to try and recoup some of their losses.

AI’s Role in Digital Forensic Science 

A digital forensics investigation is typically a complex, drawn-out process. Depending on the offense’s type and severity — and the number of Megabtyes investigators must sift through — a single case can take weeks, months or even years. AI’s unmatched speed and versatility make it one of the best solutions.

Forensic analysts can use AI in several ways. They can use machine learning (ML), natural language processing (NLP) and generative models for pattern recognition, predictive analysis, information seeking, or collaborative brainstorming. It can handle their mundane everyday duties or advanced analysis.

Ways AI Could Improve Digital Forensics

AI could substantially improve multiple aspects of digital forensic science, permanently changing how investigators do their jobs.

Automate Processes

Automation is one of AI’s greatest capabilities. Since it can work autonomously — without human intervention — analysts can let it handle repetitive, time-consuming work while they prioritize critical, high-priority responsibilities.

The experts hired by brands benefit, too, since 51% of security decision-makers agree their workplace’s alert volumes are overwhelming, with 55% admitting they lack confidence in their team’s ability to prioritize and respond in time. They can use AI automation to review past logs, making identifying cybercrime, network breaches and data leaks more manageable.

Provide Vital Insights

An ML model can continuously log real-world cybercrime events and scour the dark web, enabling it to detect emerging cyberthreats before human investigators become aware of them. Alternatively, it can learn to scan code for hidden malware so teams can find the source of cyberattacks or breaches faster.

Accelerate Processes

Investigators can use AI to accelerate examination, analysis and reporting significantly since these algorithms can rapidly analyze large amounts of data. For example, they can use it to brute force a password on a locked phone, type up a rough draft of a report or summarize a weeks-long email exchange.

AI’s speed would be especially useful to the experts businesses hire since many IT departments move too slowly. For instance, in 2023, companies took 277 days on average to respond to a data breach. An ML model can process, analyze and output faster than any human, so it’s ideal for time-sensitive applications.

Find Critical Evidence

An NLP-equipped model can scan communications to identify and flag suspicious activity. Investigators can train or prompt it to seek case-specific information. For example, if they ask it to search for words related to embezzlement, it could direct them toward texts where the suspect admits to misappropriating corporate funds.

Challenges AI Has to Overcome

While AI could be a powerful forensics tool — potentially accelerating cases by weeks — its utilization isn’t without downsides. Like most technology-centric solutions, it has numerous privacy, security and ethical issues.

The “black box” problem — where algorithms can’t explain their decision-making process — is the most pressing. Transparency is vital in the courtroom, where analysts provide expert testimony for criminal and civil cases.

If they can’t describe how their AI analyzed data, they can’t use its findings in court. According to the Federal Rules of Evidence — standards governing what proof is admissible in U.S. courts — an AI-powered digital forensic tool is only acceptable if the witness demonstrates personal knowledge of its functions, expertly explains how it came to its conclusions and proves its findings are accurate.

If algorithms were always accurate, the black box problem wouldn’t be an issue. Unfortunately, they often hallucinate, especially when unintentional prompt engineering is involved. An investigator asking an NLP model to show them instances where the suspect stole enterprise data might seem harmless but can result in a fake answer to satisfy the query.

Mistakes aren’t uncommon since algorithms cannot reason, understand context or interpret situations comprehensively. Ultimately, an improperly trained AI tool may give investigators more work since they’ll have to sort through false negatives and positives.

Prejudice and defects can make those issues more pronounced. For example, an AI told to find proof of cybercrime may overlook some cyberattack types based on bias developed during training. Alternatively, it could disregard signs of associated crimes, believing it must overprioritize a specific kind of evidence.

Will AI Replace Investigative Experts?

AI’s automation and rapid processing features could compress months-long cases into a few weeks, helping teams put cybercrime perpetrators behind bars. Unfortunately, this technology is still relatively new, and U.S. courts aren’t fond of unproven, boundary-pushing technologies.

For now — and likely decades to come — AI won’t replace digital forensics analysts. Instead, it will assist them with everyday duties, help guide their decision-making processes and automate repetitive responsibilities. Human oversight will remain necessary until they solve the black box problem for good and the legal system finds a permanent place for AI.

Zac Amos is a tech writer who focuses on artificial intelligence. He is also the Features Editor at ReHack, where you can read more of his work.