Authors : Anil Kumar Pakina

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/mrxjf24b

Scribd : https://tinyurl.com/4crt4e2v

DOI : https://doi.org/10.38124/ijisrt/25dec1365

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The existing state of AI alignment literature is largely devoted to the ethical codes of conduct and safety measures, yet the implications to the operational security and regulatory consistency have lacked adequate academic coverage. This paper presents the notion of alignment drift, which can be seen as a cumulative departure of an AI system in its behavior out of the goals it is validated to fulfill, and suggests that it is one of the key security risks of regulated settings. We propose a detection and mitigation framework, which is built by integrating behavioral baselining, explainable deviation analysis and policy conscious enforcement, and thus is able to identify subtle misalignment phenomena, which are due to the changing data distributions, indirect manipulation and feedback driven adaptation. As opposed to more traditional adversarial defenses, which focus on attacks that are more egregious or performance loss, we focus on latent behavioral drift that can be hidden but increase compliance and systemic risk. The empirical analyses performed in the financial crime detection and identity-verification cases show that it is possible to identify alignment drift in early stages, with a low false-positive rate and insignificant operational inconvenience. Therefore, the given research makes alignment drift security an unwelcomed but essential aspect of the development of trustworthy, compliant AI systems. Altogether, the current piece of work establishes alignment drift security as an important but not well-known aspect of responsible and responsible AI deployment, and thus offers a continuation of the current research on AI safety and robustness to a single view of security compliance.

Keywords : AI Alignment; Alignment Drift; AI Security; Regulatory Compliance; Trustworthy AI; Behavioral Drift Detection; Explainable AI (XAI); Policy Considerable AI Systems.

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