In an era of increasingly sophisticated cyber threats, security teams are seeking more intelligent and agile approaches to threat detection. Detection as code represents a transformative strategy that applies software development principles to security monitoring, enabling organizations to create more robust, flexible, and precise detection mechanisms. Gary Harrison, Staff Detection

It goes without saying that cybersecurity is a strategic priority for business leaders. Cyber threats are a persistent business risk that mature enterprises seek to address at all levels of operations. This increased focus is not theoretical but is tangibly evident in the growing budgets for cybersecurity expertise and products.

Computer vision is crucial in cybersecurity for enhancing threat detection through pattern recognition and anomaly identification. Its ability to quickly and accurately analyze visual data empowers security systems to identify potential threats—like unusual user behavior or malicious software—that could compromise networks and sensitive information. Automation is crucial in a landscape where

In today’s digital landscape, where remote work, mobile access, and cloud services have become the norm, traditional methods of securing systems and data are no longer sufficient. Passwords alone, even when paired with multi-factor authentication (MFA), can fall short against persistent and sophisticated cyber threats. As cybercriminals refine their tactics,

Artificial intelligence (AI) and machine learning (ML) are making noteworthy contributions to cybersecurity. They automate threat detection, analysis, and response. As cyberattacks become more sophisticated, so are AI-based systems. AI can swiftly analyze data sets to identify emerging threats and anomalous behavior patterns. ML algorithms enhance these capabilities by learning