Light Fidelity (LiFi) offers a high-speed, interference-resistant alternative to conventional wireless communication, making it well-suited for sensitive environments such as healthcare, defense, and industrial systems. While LiFi's confinement to line-of-sight communication provides a natural layer of physical security, it remains susceptible to local eavesdropping and insider interception within its coverage area. These limitations underscore the need for additional data-level protection strategies that align with LiFi's operational constraints. This paper introduces a novel steganographic method tailored for data structured in matrix form, a common representation in many digital systems. To demonstrate the effectiveness of the proposed technique, images-naturally represented as two-dimensional matrices-are used as test cases. The approach avoids traditional payload embedding, which can be statistically detectable, and instead applies recursive segmentation, matrix reshaping, and hierarchical tree-based indexing to transform the structure of the data itself. This process produces encrypted outputs that appear statistically random and visually unstructured (i.e., noise-like), concealing both the data content and the presence of hidden communication. Quantitative evaluations using metrics such as entropy, correlation coefficients, contrast, homogeneity, and Bhattacharya distance confirm that while the encrypted data is statistically obfuscated, the original matrix can be losslessly reconstructed through inverse recursion. The method's design ensures lightweight processing is suitable for resource-constrained LiFi-enabled sensor nodes while significantly enhancing communication confidentiality. By restructuring data at the matrix level rather than embedding within it, this approach provides an effective and generalizable framework for secure transmission in physically exposed but bandwidth-rich LiFi networks.