Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Session replay has quietly become default infrastructure. Product teams want to see where users stall, support wants to stop asking "can you send a screenshot," and engineering wants a reproduction path for the bug that only happens on one customer's machine. The business case writes itself. The security review, in most organisations, never really happened. So when the question finally comes up - where does this stop being lawful? - most teams go looking for the answer in the wrong place.

A storage expansion often looks simple on a spreadsheet. The organisation needs more capacity, so the infrastructure team adds drives, another enclosure or another rack and moves on. The difficulty is that each expansion carries more with it than raw terabytes. More hardware can mean more power, more cooling, more cabling, more floor space and more equipment to manage later. That is why "more storage" and "storage that scales well" are not the same thing.

Selecting a hypervisor in the UAE is no longer just a technical infrastructure decision. For many enterprises, it now affects licensing predictability, data management, operational resilience, security, and access to regional support. As organizations continue their digital transformation initiatives, infrastructure teams are also reassessing long-standing virtualization platforms. Changes in VMware licensing and ownership have encouraged many enterprises to explore VMware Alternatives and plan potential VMware Migration strategies.

The best digital risk protection platforms in 2026 are CloudSEK XVigil, ZeroFox, Recorded Future, Flashpoint, Check Point External Risk Management, Group-IB, and ReliaQuest GreyMatter DRP. They separate less on what they detect, since every vendor scrapes the same forums, than on whether they validate a finding, remove it, and connect it to an attack path. No two are strong at the same jobs.

The smartest AI model in the room may still be running on someone else's computer. Teams focus on model quality and speed. They compare GPU availability and price. Control of the underlying infrastructure often receives less attention. That gap matters as AI moves into sensitive business systems. Models may process customer records. They may use intellectual property or regulated data. Some connect directly to production operations.

Shadow agents are quickly becoming one of the hardest visibility problems in enterprise security.

The most difficult AI incidents do not necessarily begin with an obviously malicious prompt. An employee can ask an approved agent to summarize customer data. The agent retrieves the correct records, invokes an approved tool, generates the requested output, and then sends it somewhere it should never have gone. Every individual action may look legitimate in isolation. The incident only becomes visible when security can reconstruct the entire sequence and understand what the user intended, what the agent inferred, which systems it touched, and where the execution path diverged.

It's no secret that AI has a significant presence in our daily lives these days. Many people hail it as a great way to save time and help them with basic tasks each day. The problem is, AI has become a part of nearly every single app, product, and device. AI has overreached the limits that the companies selling it promised. It's time for everyone to take action to ensure that AI products and companies are kept under control before they become problematic.

Security cameras in a medical office are often installed for sensible reasons: preventing theft, documenting disruptive incidents, supporting staff safety, and helping administrators understand what happened when a patient, visitor, or employee reports a concern. The challenge is that a clinic is not a typical retail lobby. A camera can capture a patient checking in, a parent discussing a child's appointment, a person entering a specialty practice, or a workstation where appointment details may be visible.

Media captioning and broadcast transcription put different pressure on speech-to-text APIs than general business use cases. Accuracy still matters, but so do low latency for live output, speaker handling, timestamp quality, multilingual support, and the ability to keep working when the audio includes overlapping speakers, background sound, remote contributors, or fast-paced unscripted dialogue.