How AI is being used to detect and fight ransomware attacks, and how criminals could use AI to plot more efficient ransomware attacks
Imagine getting to a courthouse and seeing paper signs stuck to the doors with the message “Systems down.” What about police officers in the field unable …
Context & Ripple Effects
This 2020 VentureBeat explainer was early to frame ransomware as an AI-on-both-sides problem: machine learning models spotting encryption behavior on endpoints, while the same tooling lowers the skill floor for attackers casing networks. Four years later the UK NCSC made that framing official, warning in its all-source intelligence assessment that AI would drive ransomware volume and impact up over the following two years.
The defensive half of the prediction has since been productized: Google's Drive ransomware detection feature was trained on millions of real victim files across strains, exactly the victim-data-driven approach this article described. The offensive half is now treated as a live escalation rather than a hypothetical.
First-order effects
- Security teams gain ML-based detection that flags mass file-encryption behavior faster than signature tools, while ransomware operators get AI assistance for reconnaissance and target selection — both sides of the same coin the article lays out.
- Public-sector targets named in the piece's opening scenario — courthouses, police in the field — face downtime risk from attacks that AI-assisted operators can plan with less manual effort.
Second-order effects
- Vendors compete on training data: Google's model built on millions of encrypted victim files shows that access to real attack corpora becomes the differentiator in ransomware detection, pressuring rivals without such datasets.
- The cat-and-mouse dynamic among cybercriminals, spies, researchers, and corporate defenders intensifies as each AI capability deployed by defenders gets probed for exploitable blind spots by attackers.
Third-order effects
- If the NCSC's trajectory holds, ransomware economics keep worsening — CNBC's related analysis projects costs reaching $265B annually by 2031 as cloud computing, AI, and geopolitics reshape the threat — pushing regulation toward mandating AI-based detection rather than leaving it optional.
- The asymmetry the article flagged in 2020 becomes structural: AI industrializes both attack planning and defense, meaning security outcomes hinge less on attacker skill and more on whose models and data pipelines are better maintained.
The trend: Ransomware is becoming an AI-vs-AI contest where the quality of training data and detection models, not headcount, determines which side of the encryption event an organization lands on.