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Articles tagged with #machine-learning
Guard your LLM against prompt injection with these powerful tools: - https://github.com/protectai/llm-guard - https://github.com/protectai/rebuff - https://github.com/NVIDIA/NeMo-Guardrails - https://github.com/amoffat/HeimdaLLM - https://github.com/...
What could go wrong during the ML model deployment lifecycle (Part 2)? Continuing the example threat model from last time. It is based on the talk "Kubernetes MLSec: Securing AI in Space" by Francesco Beltramini and James Callaghan of ControlPlane. L...
What could go wrong during the ML model development lifecycle? Here is an example threat model based on the talk "Kubernetes MLSec: Securing AI in Space" by Francesco Beltramini and James Callaghan of ControlPlane. Link: [https://www.youtube.com/watc...
SIEM systems assist businesses in detecting and responding to security threats. However, recent research by Rafael Uetz, Marco Herzog, Louis Hackl盲nder, Simon Schwarz, and Martin Henze shows that adversaries can circumvent 43% of established SIEM rul...
Last week's Netflix story highlighted the potential privacy risks associated with naive anonymization techniques. This week, we delve into how Differential Privacy can offer us provable privacy protections and how she is the real Queen of PETs 馃憫. On...
A Netflix story from the past that holds important lessons about privacy risks in today's world of AI and Big Data. Welcome to Part 1 of Queen of PETs (Privacy Enhancing Technologies).
