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International Journal of Advance Scientific Research SCIENTIFIC IDEAS. INTERNATIONAL PERSPECTIVES.
ISSN 2750-1396 12 issues per year · Monthly
Crossref DOI: 10.37547/ijasr
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Articles Published:

Autonomous Risk Mitigation across Multi-Tenant Platforms through Artificial Intelligence–Based Diversion Techniques

Vol. 6 No. 03 (2026): Volume 06 Issue 03 Pages 168-179
Authors
  • Dr. Rafael Souza
    Faculty of Intelligent Computing, Federal Center for Advanced Informatics, São Paulo, Brazil
RESEARCH OVERVIEW

Abstract

Multi-tenant computing platforms have become foundational to modern cloud-native ecosystems, enabling shared infrastructure utilization across heterogeneous users, applications, and services. While multi-tenancy enhances scalability and cost efficiency, it simultaneously introduces amplified security risks due to shared resource contention, cross-tenant interference, and expanded attack surfaces. Traditional isolation mechanisms and perimeter-based security models are increasingly insufficient to mitigate adaptive cyber threats that exploit tenant adjacency, workload co-location, and dynamic resource orchestration.

This paper proposes an autonomous risk mitigation framework for multi-tenant platforms based on artificial intelligence–driven diversion techniques. The framework introduces adaptive traffic diversion, intelligent workload redirection, and deception-based risk absorption mechanisms designed to minimize exposure of critical tenant assets while preserving service continuity. The proposed system integrates machine learning–based risk scoring, behavioral anomaly detection, and reinforcement learning–enabled diversion policies to dynamically reconfigure traffic flows in response to evolving threat conditions.

A game-theoretic and decision-theoretic model is developed to represent adversarial interactions between malicious actors and autonomous mitigation agents within multi-tenant environments. The framework further incorporates ethical constraints and safety requirements derived from autonomous system governance standards, ensuring alignment with responsible AI principles and regulatory expectations (Arkin, 2016; Jobin et al., 2019). The methodology is evaluated through theoretical simulation of cloud-based multi-tenant infrastructures under attack scenarios involving lateral movement, tenant isolation bypass attempts, and workload manipulation.

Results indicate that AI-based diversion techniques significantly reduce successful cross-tenant attack propagation, improve isolation robustness, and enhance system survivability under coordinated adversarial pressure. The system demonstrates strong adaptability under concept drift conditions and evolving attack patterns (Gama et al., 2014). Furthermore, integration of reinforcement learning–driven deception strategies enhances real-time responsiveness and reduces mean exposure windows, consistent with prior findings in adaptive cyber deception research (Pesaramilli & Gudisa, 2025).

The study contributes a unified framework for autonomous risk mitigation in multi-tenant systems, bridging cybersecurity, artificial intelligence, and distributed systems engineering. It further identifies limitations related to diversion detectability, computational overhead, and ethical constraints in autonomous decision-making systems.

Keywords

  • Multi-Tenant Systems
  • Artificial Intelligence Security
  • Risk Mitigation
  • Traffic Diversion
SOURCES & CITATIONS

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