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.

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. ‍