Developing an Algorithmic ICT Security Engineering Framework for Data Privacy Compliance in Zambian SME Law Firms
Keywords:
Data privacy compliance, ICT security engineering, SME law firms, machine learning, Zambia Data Protection ActAbstract
COVID-19 has accelerated the digital transformation of small- and medium-sized enterprises in Zambia, with even law firms now having a firm foothold online. Nevertheless, it has also increased the complexity of data privacy compliance and revealed many deficiencies with manual approaches to compliance. Under the Data Protection Act No. 3 of 2021, this challenge has a greater urgency as it requires organisations dealing with personal and sensitive information to adopt more robust governance, monitoring and protection systems. Zambian SME law firms are particularly open to such attacks because they handle sensitive client information while operating under financial, infrastructural, and technical limitations. Such aided approaches can remedy that, hence presented and established in this study was an algorithmic ICT security engineering framework for automating data privacy compliance monitoring, assessment, and management among such systems. The study employed a four-phase design science and mixed-methods framework consisting of: (1) algorithm development & optimisation; (2) simulation-based testing; (3) controlled experimental validation; and (4) real-world implementation assessment. The framework consisted of a machine learning-based compliance assessment engine, an intelligent privacy management module and a security engineering component that can adapt over time. Performance metrics included Compliance Assessment Accuracy (CAA), Precision, Recall, F1-score, Resource Utilisation Efficiency (RUE), Computational Overhead Ratio (COR), Threat Detection Rate (TDR), Mean Time to Detection (MTTD), Security Incident Response Time (SIRT), Implementation Success Index (ISI) and Return on Investment (ROI). Under a compact 18-feature configuration, the proposed hybrid compliance engine reached 91.8% CAA, 91.7% F1-score and considerable computational efficiency The framework was stably effective across a variety of simulated small to medium enterprise (SME) scenarios and showed an overall performance with respect to threat detection of 89.7%, mean-time-to-detection of just 5.8 seconds, and statistically significant improvements versus manual, rule-based, several conventional machine learning discrimination baselines. Materials and methods: Data were generated from network communications collected in the LaBrea honeypot (open-source). In the loop of continuous improvement, the pilot implementation led to a further enhancement in average compliance index from 60.2% up to 85.6%, resulting in a reduction of manual hours for audit from 36.7 to 13.7 per month, as well as the generation of an average security ROI at 44.3%. The results demonstrate that resource- aware compliance automation is achievable for Zambian SME law firms, and provide a tool to enhance privacy protection without the need for enterprise-scale infrastructure.
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