Analysis Of Forensic Readiness For Ai-Assisted Cybercrime Evidence Collection And Preservation

06 Oct 2025 --
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Case 1: Sony Pictures Hack (USA, 2014)

Facts:

Sony Pictures Entertainment suffered a massive cyberattack allegedly by North Korean hackers, leading to the theft of emails, unreleased films, and sensitive employee information.

Attackers used malware and spear-phishing to gain access to corporate networks.

Evidence was partly preserved in logs, intrusion detection systems, and backups.

Forensic Issues:

Digital evidence needed to be collected from multiple sources: servers, employee devices, cloud storage.

Chain of custody had to be maintained for potential litigation and insurance claims.

AI-assisted forensic tools were used to identify malware patterns and trace attack vectors.

Outcome:

FBI investigation traced the attack to North Korean actors.

Civil lawsuits and internal disciplinary actions followed.

Sony improved forensic readiness by implementing endpoint monitoring, log retention policies, and AI-assisted anomaly detection.

Lessons Learned:

Importance of proactive forensic readiness: pre-configured logging, backups, and AI-based anomaly detection.

Evidence collection must be timely and legally compliant.

AI tools enhance speed but human oversight is essential to maintain integrity and admissibility.

Case 2: Bangladesh Bank Heist (Bangladesh, 2016)

Facts:

Hackers attempted to steal $951 million from Bangladesh Bank’s account at the Federal Reserve Bank of New York via the SWIFT system.

They successfully transferred $81 million before the fraud was detected.

Malware and automated scripts were used to manipulate payment instructions.

Forensic Issues:

Critical evidence included SWIFT logs, malware samples, and network traffic data.

AI-assisted analytics helped trace suspicious transactions and detect abnormal patterns in SWIFT messages.

Ensuring integrity and preservation of network logs was essential for prosecution.

Outcome:

Some perpetrators remain unidentified, but forensic investigations led to improvements in banking cybersecurity worldwide.

SWIFT systems incorporated AI-driven anomaly detection to prevent future heists.

Lessons Learned:

Forensic readiness requires collecting transaction logs and system logs in a tamper-proof manner.

AI can assist in real-time detection and post-event evidence analysis.

Proper chain of custody and secure storage of digital evidence is critical for legal proceedings.

Case 3: Capital One Data Breach (USA, 2019)

Facts:

Paige Thompson, a former AWS engineer, exploited a misconfigured firewall to access sensitive data of over 100 million customers.

Stolen data included social security numbers, bank account numbers, and credit histories.

AI-assisted forensic tools were used to analyze large-scale log data to reconstruct Thompson’s actions.

Forensic Issues:

Massive volume of data required AI-assisted forensic triage to identify relevant evidence.

Logs, cloud snapshots, and access control records were critical.

Ensuring proper preservation and chain of custody was crucial for criminal prosecution.

Outcome:

Thompson was arrested and charged under the Computer Fraud and Abuse Act.

Capital One strengthened its forensic readiness: automated logging, AI-driven anomaly detection, and secure evidence storage.

Lessons Learned:

Cloud environments demand forensic readiness, including AI-assisted analysis for large datasets.

Incident response plans and logging standards are crucial for preserving evidence.

AI can help detect patterns invisible to humans, but human verification remains necessary for admissibility.

Case 4: Ransomware Attack on Colonial Pipeline (USA, 2021)

Facts:

Colonial Pipeline suffered a ransomware attack that disrupted fuel supplies across the U.S. East Coast.

Hackers used DarkSide ransomware, encrypting company systems and demanding payment.

Digital evidence was scattered across endpoints, servers, and backup systems.

Forensic Issues:

Rapid collection of logs, encrypted files, and network traffic was essential.

AI-assisted forensic tools helped identify the ransomware variant, trace the initial intrusion, and map lateral movement within the network.

Evidence had to be preserved to assist in law enforcement investigations.

Outcome:

Colonial Pipeline paid a ransom, but digital forensic analysis provided intelligence for FBI tracing.

Cybersecurity and forensic readiness measures were improved post-attack, including automated logging, AI monitoring, and pre-configured forensic kits.

Lessons Learned:

Ransomware cases highlight the importance of proactive forensic readiness.

AI accelerates analysis of large-scale, multi-source digital evidence.

Legal compliance and secure chain of custody are essential for potential litigation or insurance claims.

Case 5: Marriott International Data Breach (USA/Global, 2018)

Facts:

Hackers accessed the Starwood guest reservation database, exposing information of 500 million guests.

Unauthorized access reportedly began four years prior to detection.

AI-assisted tools were used to detect unusual database queries and access patterns.

Forensic Issues:

Digital evidence included access logs, authentication records, and cloud database snapshots.

AI-assisted analytics were critical for timeline reconstruction over several years.

Chain of custody was necessary to support regulatory reporting and class-action lawsuits.

Outcome:

Marriott faced regulatory fines and lawsuits from affected guests.

Strengthened forensic readiness protocols were implemented: centralized logging, anomaly detection, and automated evidence collection.

Lessons Learned:

Long-term undetected breaches require AI-assisted forensics for efficient evidence analysis.

Preserving logs over years is part of forensic readiness.

AI improves detection speed but must be complemented by human forensic expertise for legal defensibility.

Key Analysis and Patterns in Forensic Readiness

Aspect	Observation Across Cases
AI Role	Pattern recognition, anomaly detection, and automated triage of large datasets.
Evidence Sources	Logs, servers, cloud snapshots, network traffic, endpoint devices, emails.
Chain of Custody	Critical to maintain admissibility in court; AI tools must log access and maintain integrity.
Challenges	Massive data volume, multi-year breaches, cloud environments, and ransomware encryption.
Lessons Learned	Proactive forensic readiness—secure logging, AI monitoring, automated preservation—is key to rapid, legally admissible evidence collection.