Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Why AI-Native Endpoint DLP Is The Foundation of Modern Data Security

For a long time, data loss prevention (DLP) lived in the margins of security programs. It was something teams deployed to satisfy a requirement or reduce obvious risk. A handful of policies, some visibility into network traffic, maybe a scan of cloud storage. That was usually enough. That model reflected how work used to happen. Data moved more slowly, lived in fewer places, and followed more predictable paths. That is no longer true.

Reach Recognized in Gartner Emerging Tech Report on Domain-Specific Language Models for SecOps

In its January 2026 report, Emerging Tech: Tech Innovators in Domain-Specific Language Models for SecOps, Gartner examines how domain-specific language models (DSLMs) are reshaping security operations. The report explains that DSLMs are designed to address the limitations of general-purpose language models by focusing on a particular task or use case – in this case, cybersecurity.

Beyond the Hype: Navigating the Security Risks and Safeguards of Generative AI Video

The rapid evolution of generative AI video models, such as Seedance 2.0, Kling 3.0 and OpenAI's Sora, has unlocked unprecedented creative potential. However, for cybersecurity professionals, these advancements represent a significant expansion of the corporate attack surface. In an era where "seeing is no longer believing," the integration of synthetic media into the enterprise workflow demands a rigorous security framework. This article explores the dual nature of AI video: the sophisticated threats it enables and how modern, enterprise-grade platforms are architecting defenses to mitigate these risks.

Entropy vs. Polymorphic Tokenization: Which One Actually Protects Your AI Pipeline?

If you’re building AI applications that touch sensitive data, tokenization isn’t optional. It’s the layer that decides whether your pipeline leaks PHI, PII, or financial data to your LLM, or keeps it protected. But here’s where most teams stop thinking: not all tokenization is the same. Two approaches you’ll encounter most often are entropy-based tokenization and polymorphic tokenization. They sound similar. They serve completely different purposes.

What is Data Masking

AI adoption is growing fast. But so are data risks. From Samsung’s internal code leak via ChatGPT to chatbot failures at global brands, recent incidents show one thing clearly: sensitive data can escape in unexpected ways. Most breaches today are not traditional hacks. They happen through AI tools, prompts, and automation workflows. This is why understanding what data masking is is critical. It helps organizations protect sensitive information without slowing innovation or breaking AI accuracy.

AI Access Without Add-Ons or Limits

Artificial intelligence (AI) within security operations has shifted from basic summarization to fully agentic systems that participate in threat detection, investigation, and response (TDIR). As these capabilities evolve, many vendors restrict access through add-ons, credits, or gated previews. The result is predictable: Analysts use AI less, trust it less, and see less value from it. Agentic AI capabilities should be available the moment analysts need it, not controlled through tiers or metering.

What is SIEM migration and how can AI automate the transfer?

Understand what SIEM migration involves and how AI can automate rule conversion, data transfer, and validation processes. Learn how AI reduces migration time while maintaining accuracy and security. Additional Resources: About Elastic Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale. Elastic’s solutions for search, observability, and security are built on the Elastic Search AI Platform — the development platform used by thousands of companies, including more than 50% of the Fortune 500.

Agent-to-Agent Attacks Are Coming: What API Security Teaches Us About Securing AI Systems

AI systems are no longer just isolated models responding to human prompts. In modern production environments, they are increasingly chained together – delegating tasks, calling tools, and coordinating decisions with limited or no human oversight. Almost all that communication happens through APIs. This shift offers enormous productivity benefits. But it has also complicated security. Because as soon as systems can talk to each other, they can be attacked through each other.

AI Deepfakes & Laptop Farms: Inside the 2026 Cloudflare Threat Report

In this episode of This Week in NET, host João Tomé is joined by Cloudflare threat intelligence experts Brian Carter and Chris Pacey to break down the 2026 Cloudflare Threat Report and what it reveals about today’s cyber threat landscape. We discuss how threat intelligence helps organizations prioritize risks, how attackers are increasingly leveraging automation and AI tools, and why botnets, supply-chain attacks, and credential-theft campaigns continue to evolve.

AI Impact Summit 2026 Highlights | FinTech, AI & Data Security Insights #ai

AI Impact Summit 2026 Highlights | AI, FinTech & Data Security Insights from Delhi This video covers our 5-day experience at AI Impact Summit 2026 in New Delhi, one of India's leading technology events focused on Artificial Intelligence, FinTech, Data Security, and Compliance. During the summit, we connected with industry leaders, CISOs, FinTech professionals, and AI innovators, discussing the latest developments in data protection, AI governance, cybersecurity, and enterprise AI adoption.