AI DRIVEN CYBER THREATS AND DATA PRIVACY: A RESILIENT FRAMEWORK FOR DETECTING AND RESPONDING TO EMERGING CYBERSECURITY RISKS

Rashid Hussain

doi:10.67809/ijetrm.1787494112

Abstract

Artificial intelligence (AI) is simultaneously reshaping the offensive and defensive dimensions of cybersecurity while
introducing new categories of risk that arise from AI systems themselves. Generative and adversarial techniques have
lowered the cost of convincing phishing, deepfake enabled deception, and automated exploitation, while AI based
detection systems offer defenders faster and more adaptive analytics. At the same time, the data intensive nature of
AI driven security tooling creates privacy exposures including model inversion, membership inference, training data
leakage, and prompt injection that existing cybersecurity, privacy, and AI governance frameworks address only
partially and largely in isolation. This paper undertakes a structured review of the scholarly and institutional literature
on AI enabled cyberattacks, AI enabled cyber defence, privacy preserving machine learning, explainable AI, and
adaptive incident response, in order to identify the fragmentation between these bodies of work and the resulting gap
in integrated guidance. Building on that synthesis, the paper proposes the Resilient AI Driven Cybersecurity and Data
Privacy Framework (RA CyDPF), a six-layer conceptual architecture spanning threat intelligence and data acquisition,
privacy and data governance, AI based detection and analytics, explainability and human oversight, adaptive incident
response, and resilience through continuous learning. The framework is developed through thematic synthesis and
comparative analysis of existing frameworks rather than empirical experimentation, and every proposed metric and
evaluation approach is presented as conceptual and illustrative rather than as measured results. The paper's
contribution lies in the explicit integration of security detection, privacy protection, explainability, and organisational
resilience within a single architecture, together with a defensible strategy for its future empirical evaluation. RA
CyDPF is intended to complement, not replace, established standards such as NIST CSF 2.0, NIST AI RMF, ISO/IEC
27001, and ISO/IEC 42001. Implications for security operations centres, privacy officers, AI developers, and
policymakers are discussed, alongside the limitations of a literature derived conceptual framework and the empirical
validation required before operational adoption.

Keywords

Artificial intelligence; cybersecurity; data privacy; explainable AI; adversarial machine learning; privacy preserving machine learning; cyber resilience; incident response; AI governance; conceptual framework

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