Cybersecurity Expert

By Brett Candon, VP International at Dropzone AI Trust has always been critical in security operations, but in the UK and Europe it carries significant regulatory weight. GDPR, NIS2 and similar related data‑protection frameworks shape far more than legal risk, they directly influence architectural decisions, supplier selection, and how security data can be accessed, processed and reviewed. That becomes more pronounced as autonomous AI systems move from proof‑of‑concept to daily SOC tooling.  The appeal is undeniable. Faster investigations, more consistent outcomes, and the ability to scale Tier‑1 response are all compelling. However, without clear answers on data flows, access and accountability, AI introduces risk as easily as it removes it. And speed alone does not result in trust. Against this backdrop, AI‑native approaches to SOC operations are gaining traction, grounded in the idea that autonomy, transparency, and repeatability must be foundational design principles rather tha...

Dan Petrillo, VP of Product at  BlueVoyant     As artificial intelligence (AI) becomes more deeply embedded in security operations, a divide has emerged in how its role is defined. Some argue the security operations centre (SOC) should be fully autonomous, with AI replacing human analysts. Others believe that augmentation is the right path, using AI to support and extend existing teams.    Augmentation probably reflects how SOCs operate in practice. It helps analysts triage alerts, investigate incidents faster, and it brings better context into their work, while still ensuring humans are accountable for decisions.     Complete autonomy assumes a level of reliable, end-to-end decision-making that can operate without continuous human oversight. That’s a high bar. In real SOC environments, the technology, data quality, and operational constraints rarely support that ...