SECURE DEPLOYMENT OF AI-DRIVEN OCR AND NLP SYSTEMS IN ENTERPRISE ENVIRONMENTS

Abstract

Secure deployment of AI-driven Optical Character Recognition (OCR) and Natural Language Processing (NLP) systems in enterprise environments requires a balanced integration of performance, scalability, and robust security controls. This paper presents a comprehensive framework for deploying AI-enabled document processing systems with a focus on data privacy, regulatory compliance, and operational resilience. Enterprises increasingly rely on OCR and NLP to automate document workflows, extract actionable insights, and support decision-making; however, these systems introduce risks related to data leakage, model vulnerabilities, and unauthorized access. The proposed approach emphasizes secure architecture design, encrypted data pipelines, access control mechanisms, and continuous monitoring to mitigate cyber threats. It also discusses best practices for model governance, auditability, and lifecycle management to ensure trustworthy AI operations. By integrating security-by-design principles with scalable cloud and hybrid infrastructures, organizations can achieve efficient, compliant, and secure AI deployments. The findings highlight the importance of aligning technical safeguards with organizational policies to build resilient AI ecosystems that support enterprise-grade document intelligence.