AI in cybersecurity is both an everyday tool and hype; security teams have long used AI-based tools for anomaly detection in user and network behavior, behavior-based endpoint solutions, and email security, with large language models now adding visibility to documentation, scripting, and integration tasks.
American and Chinese AI labs have similar resources and talent, but differ fundamentally in organizational culture, with American labs driven more by individual career ambition while Chinese teams focus on the overarching goal of optimal model development—a difference with measurable impact on outcomes.
As enterprises increasingly adopt public cloud platforms, their attack surfaces have evolved into dynamic ecosystems of services, identities, and APIs, with traditional security tools proving ineffective; Cloud Attack Surface Management offers a more efficient approach to discovering, tracking, and protecting exposed a
Principles such as Zero Trust and the Principle of Least Privilege (PoLP) rely on a core assumption: every digital identity must be uniquely identified, managed, and controlled, and machine identities have become central to modern security architecture as organizations move beyond traditional user-focused approaches.
