How HuoLala Builds a Robust Data Security Compliance Framework

1. Compliance Requirements and Basis

HuoLala, a large internet freight platform, must protect personal information and critical data in line with national laws and regulations. Recent regulatory actions have introduced standards, laws, and guidelines that the platform must follow.

National Standards and Regulatory Requirements

China has issued a series of data security and personal information protection standards based on the Data Security Law and Personal Information Protection Law, covering data processing, organizational, policy, implementation, and assessment aspects.

Industry Norms

Authorities such as the Cyberspace Administration, Ministry of Industry and Information Technology, Ministry of Public Security, and Ministry of Transport have issued industry‑specific rules, including the 2025 “Internet Road Freight Platform Data Security Management Requirements”.

Business Demand for Data Security

With the digital economy, data has become a key production factor, increasing reliance on data exchange while raising protection requirements. Non‑compliance can lead to fines, service suspension, and reputational damage, especially for platforms handling workers’ rights.

2. Basic Data Security Compliance Framework

HuoLala’s security department builds the framework in three layers:

Organizational layer: Establishes a company‑level information security leadership group, data security management unit, personal information protection team, compliance working group, and external supervision body.

Management layer: Under unified network data security and personal information protection policies, creates ten categories of security policies (physical environment, network, systems, applications, data, personnel, AI, incident response, compliance, etc.).

Technical layer: Leverages cloud infrastructure, mobile security mechanisms, big‑data platforms, and AI‑assisted security to integrate protection with business processes.

3. Data Security System Construction and Operation

The system follows a PDCA (Plan‑Do‑Check‑Act) cycle to keep pace with regulatory changes and emerging technologies such as cloud, mobile, big data, and AI.

Legal monitoring: Build a compliance knowledge base and knowledge graph for dynamic policy tracking.

Policy interpretation and implementation: Deploy a “freight large model” with retrieval‑augmented generation to interpret requirements.

Business collaboration: Apply encryption, watermarking, de‑identification, and anonymization aligned with specific services.

Company‑wide enforcement: Track policy rollout, evaluate effectiveness with quantitative metrics, and audit results.

4. Execution and Implementation of Data Security Management

Management is organized into four parts—classification, management, response, and audit—each with technical solutions and reports.

Data Classification and Grading

Following the Data Security Law and national standards, HuoLala classifies data into operational, management, and office categories, then grades them from L1 to L4 based on sensitivity.

Data Security Lifecycle Management

Policies cover the entire data lifecycle—risk assessment, privacy impact assessment, identity trust, access control, encryption, monitoring, incident response, backup, and recovery—embedding responsibilities into job roles.

Incident Response

A comprehensive emergency plan is regularly exercised, moving toward automation.

Compliance Audits

Annual audits are performed by accredited third‑party institutions, with rotation to ensure independence.

5. Certifications of the Data Security System

HuoLala has obtained several recognitions:

International ISO 27701 privacy‑security certification, repeatedly renewed and praised by BSI.

National GB/T 37988‑2019 maturity level‑3 certification, scoring among the top.

Industry‑specific data security capability certifications, establishing it as a benchmark in freight logistics.

6. Building an AI‑Driven Data Security Maturity Model

Large language models now enable automated data classification and grading. HuoLala is developing its own industry model, combining AI‑assisted compliance copilots with big‑data and compute resources, aiming for an autonomous, “self‑driving” compliance capability.

Overall, data security is a dynamic process; as AI advances, HuoLala’s compliance framework will continuously evolve to support safe freight services for society.