Checkmarx’s new SAST engine isn’t about the LLM. It’s about what happens after.
The major static application security testing (SAST) vendors are now wrapping a large language model around their legacy scanning engines The post Checkmarx’s new SAST engine isn’t about the LLM. It’s about what happens after. appeared first on The New Stack .
The proliferation of Large Language Models (LLMs) is pushing existing cybersecurity solutions to integrate AI, leading to new approaches in static application security testing (SAST).
Sophisticated readers should care because this signifies a maturation of AI integration into critical security infrastructure, shifting focus from merely applying LLMs to leveraging their outputs for enhanced security outcomes.
The focus in application security is shifting from basic LLM integration to optimizing the subsequent analysis and remediation phases, potentially leading to more effective and efficient vulnerability identification and patching.
- · Checkmarx
- · Cybersecurity companies adopting advanced AI
- · Organizations with complex software estates
- · Legacy SAST vendors (slow to adapt)
- · Companies relying solely on traditional scanning methods
SAST tools become more intelligent and proactive in identifying vulnerabilities.
Software development lifecycles (SDLCs) integrate security earlier and more seamlessly, reducing technical debt.
The definition of what constitutes a 'secure' application evolves, raising the bar for software assurance across all industries.
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