Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Seven years after the General Data Protection Regulation came into force, organizations are still learning a hard lesson: GDPR compliance is a technical lesson as much as it is a legal one. Application security for GDPR compliance sits at the center of that reality, because the personal data GDPR protects lives in your software: your web applications, your APIs, your cloud-native services, your third-party integrations.

Security teams are managing more endpoints, identities, networks, and security events than ever. Each control adds valuable protection, but the work of connecting its signals, explaining incidents, and deciding what needs attention often falls to analysts. That is why the next evolution of WatchGuard ThreatSync and Rai Analyst matters. ThreatSync provides the foundation for cross-product detection, correlation, incident management, and coordinated response.

Integrating artificial intelligence into cybersecurity has fundamentally changed vulnerability research – and not necessarily for the better. For decades, discovering and verifying Common Vulnerabilities and Exposures (CVEs) required deep technical expertise, creating a disciplined process that kept disclosure volume relatively manageable. Today, Generative AI has obliterated that barrier to entry.

Shadow AI refers to the use of artificial intelligence tools, applications, models, or AI-powered features without the organization's required IT approval, security review, or governance. It is often associated with employees using personal accounts for tools such as ChatGPT or Gemini. However, Shadow AI extends well beyond public AI chatbots.

Endpoint visibility is the ability to identify connected devices, understand their activity and health, and assess their security posture. Without it, IT teams struggle to determine which endpoints access business resources, identify security weaknesses, and investigate operational problems. Remote employees, personal phones, contractors, cloud-connected systems, and specialized equipment expand the endpoint landscape.

AI used to live in a browser tab. Today it also runs inside the apps employees use every day, like Copilot in Word, in dedicated apps like ChatGPT and in coding agents such as Claude Code, Codex and Cursor. Coding agents work differently from chat apps. A chat app answers a question and waits. A coding agent is given tools and takes actions on its own, with the developer’s access. So every endpoint is becoming an agentic endpoint.

“We don’t train on your data” does not mean “we don’t retain it.” Neither promise tells you where your data is processed. Zero data retention (ZDR) closes one of those gaps. It does not close all three.

TrustCloud showed up across the security and analyst landscape in recent months. CEO Sravish Sridhar published three columns in SecurityWeek and SC Media on why point-in-time assurance no longer holds up, and both IDC and 451 Research covered TrustCloud’s continuous, AI-native approach. The pieces come at it from different angles, but they land on a common point: boards and buyers increasingly want proof a control is working now, not a snapshot from last quarter.

Originally published by the PGA of America. Operating a golf facility means keeping track of a lot more than what’s happening on the course. Tee times, lesson schedules, payroll and vendor payments all depend on technology working as it should. Keeping those systems secure is easy to put off when everything is running smoothly, but it’s worth making time before a problem interrupts the day.

The Journey to Autonomous DLP · Context Intelligence Understand the content and the boundary first. History is a secondary factor. Our vision for Autonomous DLP starts with understanding data well enough to make the right decision in real time. What does the document contain? Does it hold the user’s own information, someone else’s records, or confidential company material?