Navigating the Dual Edge: A Comprehensive Technical Survey of Security, Privacy, and Countermeasures in the Era of Artificial Intelligence

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 13 | 01 | Page :
    By

    Shubhashree Pattanayak,

  • Suman Sahoo,

  • Sanjay Kumar Sahoo,

  1. Assistant professor, Department of Computer Science and Engineering, Gandhi Institute of Excellent Technocrats, Bhubaneswar, Odisha, India
  2. Student, Department of Computer Science and Engineering, Gandhi Institute of Excellent Technocrats, Bhubaneswar, Odisha, India
  3. Student, Department of Computer Science and Engineering, Gandhi Institute of Excellent Technocrats, Bhubaneswar, Odisha, India

Abstract

Artificial Intelligence (AI) is seamlessly weaved into vital sectors such as self-driving cars, high-speed trading systems, and defense strategies, it has triggered a counterintuitive development in advanced cyber-attacks. This survey paper attempts to perform an in-depth technical analysis on “AI Attack Surface.” There are threats across three main vectors. Data Integrity Attacks focuses specifically examining ‘Clean Label’ poisoning and backdoor injection. Model Confidentiality Breaches is discussing the mathematics behind Model Inversion and Membership Inference Attacks. Generative Exploitation is examining Prompt Injection and Hallucinations in Large Models.  This paper presents a comprehensive technical survey of the dual nature of AI: its potential to both bolster security and be exploited as an attack vector. We first catalog and analyze the principal threat models associated with AI systems, including adversarial examples, model inversion, data poisoning, and inference attacks. We then explore privacy vulnerabilities stemming from training data leakage, unauthorized model access, and collaborative learning paradigms like federated learning. Through this lens, the survey examines how traditional threats evolve in AI contexts and highlights new attack vectors unique to learning-based systems. Next, we systematically review state-of-the-art countermeasures across defensive categories—robust training, certified defenses, differential privacy, cryptographic approaches, and secure multi-party computation—emphasizing both strengths and limitations. Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD), to name a few. In addition to that, we investigate the combined realms of AI and Internet of Things (IoT) through the ZIRCON framework. Instead, we promote a need for a “Zero Watermarking” approach. In closing, we have a strategic outlook for 2025–2075. In fact, we believe that “Explainability” or XAI is not just a norm for regulation. Rather, it is a key to unlocking a future full of Artificial General Intelligence.

Keywords: Adversarial Machine Learning, Differential Privacy, Large Language Models, Prompt Injection, Internet of Things (IoT) Security, Explainable AI (XAI), ZIRCON, Data Poisoning

How to cite this article:
Shubhashree Pattanayak, Suman Sahoo, Sanjay Kumar Sahoo. Navigating the Dual Edge: A Comprehensive Technical Survey of Security, Privacy, and Countermeasures in the Era of Artificial Intelligence. Journal of Operating Systems Development & Trends. 2026; 13(01):-.
How to cite this URL:
Shubhashree Pattanayak, Suman Sahoo, Sanjay Kumar Sahoo. Navigating the Dual Edge: A Comprehensive Technical Survey of Security, Privacy, and Countermeasures in the Era of Artificial Intelligence. Journal of Operating Systems Development & Trends. 2026; 13(01):-. Available from: https://journals.stmjournals.com/joosdt/article=2026/view=242316


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Ahead of Print Subscription Review Article
Volume 13
01
Received 27/01/2026
Accepted 31/01/2026
Published 20/03/2026
Publication Time 52 Days
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