In 2025, threat actors turned widely used artificial intelligence tools into weapons for launching fast, precise network intrusions. CrowdStrike’s 2026 Global Threat Report found an 89% year-over-year increase in attacks by AI-enabled adversaries, as criminals used automation and machine-generated scripts to cut the time between initial entry and full domain access to under 30 minutes. […]
The post Threat Actors Weaponized AI Tools to Gain Full Domain Access within 30 Minutes appeared first on Cyber Security News.
To protect private information stored in text embeddings, it’s essential to de-identify the text before embedding and storing it in a vector database. In this article, we'll demonstrate how to de-identify and chunk text using Tonic Textual, and then easily embed these chunks and store the data in a Pinecone vector database to use for semantic search in RAG or other LLM applications.
The post How to create de-identified embeddings with Tonic Textual & Pinecone appeared first on Security Boulevard.
Preparing the data for use with generative AI tools is a major impediment that affects time-to-value for enterprise AI use cases. We've expanded Tonic Textual’s functionality to take your unstructured data from raw to AI-ready in just a few minutes, while you ensure that sensitive data is protected.
The post Creating unstructured data pipelines for retrieval augmented generation appeared first on Security Boulevard.
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