Are GPT-Powered AI Systems Superior to Traditional Cybersecurity Tools: Applications and Challenges
Bibliographic record
Abstract
Generative Pre-trained Transformer (GPT) models are revolutionizing cybersecurity by enhancing threat detection, risk evaluation, phishing defense, and automatic vulnerability analysis.This study delves into the various applications of GPT Technologies in security operations, emphasizing their competence in processing security information of large volume, anomaly detections, and providing real-time insights.Case studies cite quantifiable benefits: Anomaly detection by AI reached a high of 80% accuracy, malware and phishing classification 75-95% accuracy, and Microsoft Copilot reduced phishing attacks by 45% in commercial settings.VirusTotal and Cylance AI improved malware categorization accuracy by 38%, reducing false positives by 35%.Incident response effectiveness was improved by as high as 40% in reported deployments.However, GPT models are also exposed to adversarial exploitation, gaps in explanation, integration issues, and dependence on previous data.This paper lists countermeasures, such as prompt engineering, fine-tuning, domain-specific training, and hybrid AI-human decision systems.Findings further highlight the significance of continuous updates, interdisciplinary collaboration with adherence to ethical frameworks to reap the full benefits of GPT-powered cybersecurity.So, take into consideration integrating these models into present security ecosystems.This way, organizations may strengthen their defenses, improve risk management, and make resilience against cyber threats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".