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.
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.
Alert fatigue from excessive false alarms threatens the effectiveness of security teams; when tools like anti-malware, SIEM, IDS, or EDR generate too many false positives, responsiveness to genuine threats diminishes, and security managers must actively understand and control their tools’ error rates.
