But that's not all: AI systems also allow, in just a few minutes, the transcription of a large number of audio files, which are increasingly used within instant messaging systems as an alternative to text messages. 

The digital forensics of images, videos, and IoT devices represents a rapidly expanding sector, where AI is employed to analyze multimedia content, detect deepfakes and steganography, and monitor connected devices.
In the context of Digital Forensics and Incident Response (DFIR), AI supports the automation of incident response processes, enabling real-time analysis and proactive investigations. Effective collaboration between AI systems and human investigators is essential to preserve the integrity of decisions and ensure reliable outcomes.

Malware Analysis

The use of AI in malware analysis allows for automatic classification with high accuracy, facilitating the early detection of cyber threats, overcoming the limitation relegated to identifying threats through signatures (a model generally used, along with heuristic analysis, for detecting cyber threats). The latest AI models, in fact, identify hidden patterns and anomalous behaviors that escape traditional defense systems.

Through AI-enabled malware analysis, it is possible to drastically reduce false positives, increasing the reliability of reports and optimizing the allocation of investigative resources. Advanced tools developed by companies like Fortinet and supported by academic research from the University of Catania highlight the effectiveness of such approaches in the field of digital forensics.

Another advantage of adopting AI lies in the model's continuous adaptability. Unlike systems based solely on static signatures, which require manual updates and react only to known threats, models trained on large volumes of samples can generalize and recognize unknown variants belonging to the same malware family.

Images, Videos, and IoT

The forensic analysis of images and videos using AI employs recognition and content analysis techniques to identify digital manipulations such as deepfakes and steganography. These methods are essential for detecting falsifications and ensuring the authenticity of multimedia evidence.

In the IoT context, artificial intelligence supports digital forensics applied to connected devices, addressing the complexities arising from the diversity and proliferation of endpoints. AI solutions facilitate the monitoring and analysis of suspicious events in real-time, overcoming the limitations due to the distributed nature of IoT systems.

The specific challenges of this field require integrated approaches that combine AI and specialized forensic skills, as highlighted by studies from Interpol and the University of Catania, to ensure accuracy and timeliness in investigations.

Challenges, Limitations, and Ethical Issues of AI in Digital Forensics

The use of artificial intelligence in digital forensics presents significant challenges related to algorithmic bias, false positives and negatives, as well as issues of privacy and legal validity. Algorithmic bias, often stemming from unrepresentative training data or flawed designs, can compromise the reliability of analyses and negatively influence investigative decisions.

Transparency and explainability of algorithms are essential to ensure the legal validity of digital evidence and to address any disputes in court. Furthermore, the illicit use of AI and the liability arising from automated decisions require audit mechanisms and significant human oversight.

To these critical issues, the topic of reproducibility of analyses is added, a central element in digital forensics. An artificial intelligence model that produces non-deterministic results, or that depends on undocumented dynamic parameters, risks compromising the principle of independent verifiability of evidence. In judicial contexts, it is not enough to demonstrate the outcome of an analysis: it is necessary to reconstruct the entire logical and technical process that led to that result, allowing third parties to replicate it with equivalent tools and data.