Breakthrough AI-powered method secures data across every lifecycle phase

With the rapidly increasing cyber threats, data breaches, and ever-expanding information ecosystems, organizations are under growing pressure to ensure airtight security across the entire lifespan of their data. From collection and storage to transmission and destruction, the potential for compromise looms at every turn. Yet most existing security frameworks fall short, relying on fragmented models that leave critical vulnerabilities unchecked

A new study has proposed a groundbreaking methodology for full lifecycle data security risk assessment, leveraging artificial intelligence and an attention-based neural network architecture. Published in Symmetry, the paper titled "An Intelligent Risk Assessment Methodology for the Full Lifecycle Security of Data", details a comprehensive, automated approach designed to evaluate risks from data collection through destruction, marking a significant advancement in digital security management.

What vulnerabilities arise across the full data lifecycle?

The research underscores that as data becomes increasingly integrated into all aspects of life, from industry to governance, their security must be assured at every stage. The authors outline a six-phase lifecycle of data: collection, transmission, storage, processing, exchange, and destruction. Each stage carries unique vulnerabilities. For example, poor source credibility or lack of encryption during transmission may expose data to tampering or unauthorized access. Meanwhile, insufficient key management or inadequate destruction methods risk long-term data leakage and unauthorized recovery.

The authors define thirty precise indicators across these six phases, including aspects such as encryption status, access controls, privilege management, and interface security. These indicators serve as inputs to the risk model. Notably, the study reveals a significant variation in the weight of each factor, with indicators like "data leakage during processing" (P3) carrying the highest risk weight at 8.16%. This granular approach ensures that risk management strategies can be tailored precisely to the most critical vulnerabilities in a system.

How does the proposed methodology overcome limitations of previous models?

The current landscape of risk assessment methods often relies on either subjective judgment or limited sets of quantitative indicators. Many models focus primarily on traditional information system security without adequately addressing data-specific lifecycle risks. Moreover, conventional models tend to assign indicator weights either arbitrarily or based on a single methodology, which can undermine the credibility of results.

In contrast, the proposed system uses a dual-weighting framework combining the Analytic Hierarchy Process (AHP) for expert-driven insights and the Entropy Weight Method (EWM) for objective data-based calibration. These are fused into a final composite weight for each risk factor. Furthermore, the authors apply a fuzzy comprehensive evaluation method to label each data instance by risk level, low, medium, or high, prior to model training.

The core innovation lies in the integration of a bidirectional row–column attention mechanism within a neural network. This allows the model to capture complex intra-feature and inter-sample relationships. For example, intra-row attention models the dependencies among features within a single sample, while intra-column attention tracks how a particular feature behaves across the dataset. Together with multilayer perceptrons and residual connections, this architecture allows for nuanced and high-accuracy predictions of risk levels.

How effective is the model in real-world application?

To validate their model, the researchers applied it to a dataset from a gas system's data management infrastructure, capturing over 5,700 samples with full lifecycle metrics. Using an 80/20 train-test split, the model demonstrated a risk classification accuracy of 97.14%, a macro-precision of 97.13%, and a macro-F1 score of 97.15%. Importantly, the system maintained its high performance even under stress conditions: a sensitivity analysis using Gaussian noise injections of up to ±20% showed minimal impact on output accuracy.

Compared with traditional models like support vector machines (SVMs) and feedforward neural networks (FFNNs), the proposed model significantly outperformed both. While the SVM achieved 92.3% accuracy, the FFNN lagged behind at 73.8%, particularly struggling with small sample sizes - a common issue in real-world risk assessment settings. Moreover, confusion matrix analysis revealed the proposed model had the fewest misclassifications between risk levels, a critical consideration given the potential fallout from underestimating high-risk data.

Beyond performance, the model’s design enhances interpretability and adaptability. Its structure enables automated learning of high-order patterns and nonlinear interactions, supporting robust generalization even in dynamic security environments. This makes it particularly valuable for organizations needing real-time risk monitoring and adaptive threat response.