MIND Launches Autonomous DLP Platform for Simplified Data Protection
MIND has announced the general availability of its autonomous data loss prevention (DLP) platform, as stated in a press release. This platform is designed to automate the entire lifecycle of data protection, offering features such as automated data discovery, AI-powered classification, and effortless remediation.
The platform aims to reduce manual work and prevent sensitive data leaks by providing real-time, context-aware controls. It supports various IT environments, including GenAI, SaaS, endpoints, emails, and on-premise file shares. MIND's solution is noted for its rapid deployment, delivering security value within days.
MIND's platform has been recognized for its innovation, earning a spot in Fortune's Top 50 Cybersecurity Companies of 2025 and receiving an Honorable Mention in the Black Hat USA 2025 Startup Spotlight Competition. The platform is now available for organizations seeking to enhance their data protection strategies.
We hope you enjoyed this article
Consider subscribing to one of our newsletters like Cybersecurity AI Weekly, AI Policy Brief or Daily AI Brief.
Also, consider following us on social media:
More from Cybersecurity
Sep 16 AV-Comparatives Certifies 11 Endpoint Security Products in 2026 Test Sep 16 Cohesity Adds AI Agent Backup and Recovery to Data Cloud Sep 15 Hexnode Introduces Synapse for IT and Security Operations Sep 15 Cisco Expands Splunk AI for Private and Isolated Environments Sep 15 Zip Security Joins CrowdStrike Coalition to Protect Small BusinessesAI Policy Brief
Weekly report on AI regulations, safety standards, government policies, and compliance requirements worldwide.
Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
Read moreYou may also like
Zeit AI raises €5 million for autonomous data engineering agent
OpenMatter Network Adds Secure AI and Data Collaboration Features
QuantaMind Study Reports DFT Level Accuracy for Reactive Molecular Simulations
Milliman MedInsight Introduces MIKE for Healthcare Analytics
MixMode Gets US Defense and Intelligence Approvals for AI Cyber Platform
Daily AI Brief: the AI news that matters, in your inbox.