Volume 2 - Issue 1, January - February 2026

AISafeHoneynet introduces a shift - by weaving AI insights into core safeguards, it counters misuse before harm spreads. Most setups gather logs but leave exits unchecked; danger slips through those gaps. Instead of just logging attacks, this approach watches for escape routes attackers might take. Automation often lags, leaving systems open when threats evolve fast. Here, responses adjust in real time, shaped by live traffic patterns. Old models collect data passively; this one act early, guided by behavioral clues. Programmable networks enable tighter rules that adapt without manual input. Safety improves not by adding layers, but by making each layer respond wisely. The result? A honeynet less likely to turn against its owner. Observations from honeypot activity feed into machine learning systems that detect suspicious patterns as they happen. Instead of relying on fixed protocols, responses adapt based on live data, shaping a more responsive defense layer. Unusual outbound traffic gets blocked without disrupting the appearance of vulnerability. Insights gathered during attacks travel across network nodes, strengthening each segment through shared experience. Automation handles threat isolation and reporting, freeing personnel from repetitive oversight tasks. Because judgments emerge from ongoing signals, not pre-set conditions, the system supports deployments that stay resilient amid evolving threats.

Swapnil Rajesh Bhopi, Aarohi Chandrakant Keny, Jayesh Shinde,"AISafeHoneynet: AI-Orchestrated System-Level Defenses for Future-Proof Honeynets Against Botnet Hijacking and DDoS Exploitation with Federated Threat Intelligence" International Journal of Scientific Research and Engineering Development, V2(1): Page(510-517) Jan-Feb 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.