Dark Reading (1 hour ago)
Cyber leaders can get ahead of AI liability with rigorous documentation, transparency, and smart contract negotiations with AI vendors.
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This is going to be very bad for transparency.
The University of New Mexico Health Sciences Center disclosed a cyberattack that caused system outages and disrupted patient care and academic activities.
It’s not an honest mistake; it’s the start of an attack on your wallet.
The findings suggest possible surveillance interest in countries where Starlink is banned or restricted.
A credit card skimming group reportedly compromised a shared analytics script used by e-commerce platform Convertr to steal shoppers’ payment data during checkout.
A common mix of impersonation, domain infrastructure, and trusted software is helping attackers scale Microsoft Teams intrusions across organizations.
A recent Shai-Hulud attack shows defenders need real-time visibility into production code and affected services to respond to supply chain compromises.
Nathan Hamiel outlines potential SecOps horror in the AI midst, as core technical capabilities go the way of reading an atlas or remembering phone numbers.
The malware’s focus on AI tools expands the traditional infostealer playbook to expose a forest of AI sessions, sensitive business data, and developer infrastructure from a single compromised endpoint.
Measuring AI model security gets complex fast — a growing patchwork of benchmarks targets different variables in the equation, but none replace testing against your own code and workflows.
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