In the wake of new AI frontier models' abilities to discover vulnerabilities and autonomously execute multi-stage attacks on ...

The White House introduced orders to oversee powerful AI models like Anthropic’s Mythos, which posed cybersecurity risks. Anthropic limited access due to security fears, and government actions ...

The company said White House review of AI releases shouldn’t become the norm.

Data drift happens when the statistical properties of a machine learning (ML) model's input data change over time, eventually rendering its predictions less accurate. Cybersecurity professionals who ...

One malicious prompt gets blocked, while ten prompts get through. That gap defines the difference between passing benchmarks and withstanding real-world attacks — and it's a gap most enterprises don't ...

Enterprises are racing to embed large language models (LLMs) into critical workflows ranging from contract review to customer support. But most organizations remain wedded to perimeter-based security ...

As companies rush to develop and test artificial intelligence and machine learning (AI/ML) models in their products and daily operations, the security of the models is often an afterthought, putting ...

Enterprises cannot secure AI agents by making the underlying models more robust and must instead enforce security controls at the system level around them, researchers behind a paper published this ...

With systems only growing more sophisticated, the potential for new semiconductor vulnerabilities continues to rise. Consumers and hardware partners are counting on organizations meeting their due ...

Traditionally, enterprise security operating models operated a fixed and regular cycle: Findings surfaced through periodic scans, security teams triaged results and remediation followed through ticket ...