Engineering and Technology
| Open Access |
DOI: Adaptive Trust: A Comparative Analysis of Cybersecurity Metrics and AI-Driven Privacy Safety Enforcement. Traditional Fidelity versus AI-Driven Velocity
Savi Grover , Software Engineer in Test , USAAbstract
Cybersecurity Testing and Evaluation (T&E) comprise of a foundational resilience component, moving beyond simple quality assurance to become a critical process for continuous organizational hardening. Effective T&E enhances emergency plans, policies, attacks resistance, filtration, firewall strengthening procedures, promoting the efficient utilization of capabilities required to respond to sophisticated cyber-attacks. In this paper, we are performing comparative analysis of traditional security and cyber evaluation metrics with upcoming AI driven enhanced secure measures. AI-LLM security techniques like early defect prediction, defect clustering, Secure cloud and Automated Incident Response measures are weighted against traditional security techniques in terms of – speed, velocity, criticality and depth of coverage scope. These metrics point towards countless advantages of combining the two for greater holistic impact.
Keywords
Cybersecurity, AI Safety, AI-LLM Security Methodologies
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Copyright (c) 2026 Savi Grover
This work is licensed under a Creative Commons Attribution 4.0 International License.
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