Software security has become a critical priority for modern digital reliability, yet traditional testing methods like static and dynamic analysis struggle with speed, scalability, and detection completeness. The increasing complexity of software systems necessitates more effective tools for vulnerability identification and threat prevention.

Researchers are now turning to artificial intelligence to supplement or replace conventional methods. These AI-based approaches enable the early detection of threats, improve risk prioritization, and reduce manual security workloads, which enhances the overall resilience of digital systems.

While integrating large language models into cybersecurity workflows presents significant promise for automating threat detection, current research indicates that further development is required to refine capabilities and address inherent technical limitations for widespread deployment.

Software security has become a critical priority for modern digital reliability, yet traditional testing methods like static and dynamic analysis struggle with speed, scalability, and detection completeness. The increasing complexity of software systems necessitates more effective tools for vulnerability identification and threat prevention.

Researchers are now turning to artificial intelligence to supplement or replace conventional methods. These AI-based approaches enable the early detection of threats, improve risk prioritization, and reduce manual security workloads, which enhances the overall resilience of digital systems.

While integrating large language models into cybersecurity workflows presents significant promise for automating threat detection, current research indicates that further development is required to refine capabilities and address inherent technical limitations for widespread deployment.