In machine learning, privacy risks often emerge from inference-based attacks. Model inversion techniques can reconstruct sensitive training data from model outputs. Membership inference attacks allow ...
Could the Innovation in Non-Human Identities Be the Key to Enhanced Secrets Security? Where progressively leaning towards automation and digital transformation, how can we ensure that the creation and ...
How Can Organizations Leverage Non-Human Identities for Better Security? Have you ever wondered how Non-Human Identities (NHIs) are reshaping cybersecurity? With cyber threats evolve, organizations ...
In the first instalment of LCGC International's interview series exploring how artificial intelligence (AI)/machine learning ...
Enterprise adoption of cognitive intelligence platforms has accelerated, yet executive confidence has not kept pace. Many deployments promise ...
AI-powered document processing automates data extraction, classification, and validation with 95-99% accuracyMarket projected ...
The March 2026 issue of NEJM Catalyst Innovations in Care Delivery is a special theme issue on the hard work of implementing artificial intelligence in real-world ...
To prevent algorithmic bias, the authors call for multivariable modeling frameworks that jointly incorporate biological sex, genetic ancestry, and gender-related life-course exposures.
With the rise of ransomware, phishing, zero-day exploits and other cyberthreats, organizations worldwide are confronting a cybersecurity crisis that ...
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