AI Is Finding Software Vulnerabilities Faster. Can Digital Forensics Keep Up?

Artificial intelligence is accelerating the discovery of software vulnerabilities, raising concerns that attackers could exploit weaknesses before organisations have time to investigate, patch and secure affected systems. A new Reuters report published on 23 October 2026 highlights the growing pressure on security teams as AI changes the vulnerability-discovery process.

The report follows a week of internet disruptions linked to newly disclosed software weaknesses, including a vulnerability in PHP, a programming language used across a large portion of the web. Some website operators temporarily took their services offline while assessing the risk and preparing fixes.

Reuters cited research from cybersecurity intelligence firm VulnCheck indicating that the number of discovered software flaws had doubled year-on-year, with AI contributing to the increase. Experts interviewed by Reuters warned that AI can help identify weaknesses and potentially accelerate the development of exploits. However, they disagreed about whether more discoveries would necessarily result in more successful cyberattacks. At the time of the report, researchers had observed aggressive attempts to exploit the PHP vulnerability, but a working exploit had not been confirmed by one threat-intelligence firm.

For digital forensic investigators, the changing timeline presents a practical challenge: how can an investigation establish whether a newly discovered vulnerability was actually exploited before a system was patched?

Investigators may need to correlate web-server logs, application errors, endpoint telemetry, network traffic and authentication records against the vulnerability's disclosure and patch timeline. Suspicious requests alone do not prove successful exploitation. Stronger conclusions require evidence of execution, unauthorised access, changes to system state or other corroborating indicators.

Where logs are incomplete or have been overwritten, investigators may struggle to determine the initial access time, the scope of compromise or whether data was accessed before remediation. Organisations should therefore preserve relevant logs and snapshots, record patching actions, and document the timing of containment measures.

AI-assisted vulnerability discovery may ultimately improve security by exposing flaws that would otherwise remain hidden. But it also increases the importance of rapid, evidence-based incident response.

The forensic priority is not simply to find the vulnerability. It is to establish whether it was exploited, reconstruct what happened, and distinguish attempted attacks from confirmed compromise.