Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Most organizations experience a strange DNS problem at some point: The same domain name means something different depending on who's asking for it. For example, a single internal app, workspace.company.com, might need to serve a locked-down instance to finance, a live staging build to engineering, and a stable production build to support — all under one domain and from the same DNS server.

Top tips is a weekly column where we highlight what's trending in the tech world and share practical ways to navigate these shifts. This week, we're looking at spear-phishing: how cybercriminals weaponize social engineering, why your natural instinct can act as a security blind spot, and practical steps to verify suspicious requests before you take action. I would like to make a confession: I’m a well-versed tech expert, but I almost fell for a phishing scam.

Originally published by Cybersecurity Insider. Over the past decade, cybersecurity threats have evolved from a technical issue into a key driver of business and operational risk. Artificial intelligence is accelerating that shift even further, changing the game once again. In 2026, AI in cybersecurity is multifaceted: a tool for defense and incident response, but also a powerful accelerator for attackers. AI-enabled incidents rarely stay contained within the security function.

On August 6, 2026, Apple shipped an emergency, out-of-band fix for CVE-2026-65400, an authentication issue in screensharingd, the daemon behind Screen Sharing, macOS's built-in remote desktop service that listens on TCP port 5900. Apple's advisory describes an attacker who could reach the service over the network and authenticate without valid credentials, then read and write files as root—enough to achieve full remote code execution.

A common misconception is that technology is to blame. That might not be true.

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Quantifying cyber risk in an enterprise IT environment starts from frequency. Incidents of a given type happen at some rate, that rate is observable across enough organizations to be estimated, and severity follows from what was affected. ‍ Operational technology inverts both halves. Frequency data barely exists, and the consequences are already documented in detail by people who have never thought about cyber. Working with that inversion rather than against it is what makes the modeling tractable.

Gartner predicted in mid-2025 that more than forty percent of agentic AI projects would be canceled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls. Treat the figure as a forward-looking estimate rather than a measurement, since canceled projects tend to be quietly renamed, absorbed or left to lapse rather than formally closed. ‍