Data Security and Intelligence Are Not at Odds: How an Unstructured Data Middle Platform Achieves "Controllable AI"
As AI becomes a key engine for accelerating business innovation and decision-making efficiency, another topic has grown increasingly sensitive: data security. Especially in the context of large amounts of unstructured data being used for AI training and inference, enterprises are no longer just concerned about "whether AI can be used," but rather "whether the data AI uses is secure." Vast quantities of unstructured data, such as real business documents, customer records, and contract information, serve as both the "fuel" for AI and a potential "risk" for leaks and compliance violations.
However, this does not mean that intelligence and compliance, security and efficiency are inherently opposed. In fact, by building an unstructured data middle platform, enterprises can fully leverage AI capabilities while maintaining end-to-end control over data, achieving truly "controllable AI."
Where Does the Security Anxiety Around Unstructured Data Come From?
First, there is the issue of "lack of visibility." Traditional IT systems lack a unified view of unstructured data. Documents are scattered across employee local storage, shared drives, and email attachments, making it impossible to identify whether they contain sensitive information.
Second, there is "lack of control." Once AI models start using data for training, enterprises often cannot trace the source of the data corpus, let alone determine whether the model has learned from data containing business secrets or personal privacy information.
Finally, there is "lack of accountability." Without an audit mechanism for data processing, it becomes difficult for enterprises to define responsibility boundaries or trace the source when compliance issues or AI "hallucinations" occur.
The Unstructured Data Middle Platform: Making Data Used by AI Visible, Controllable, and Traceable
Comprehensive Awareness: Automatic Sensitive Information Identification and Classification
The first step of an unstructured data middle platform is to establish "visibility" for enterprises. The platform comes with built-in PII detection rules and custom identification mechanisms, supporting the recognition of ID numbers, bank card numbers, contract numbers, customer information, and more. Once sensitive data is detected in a document, the system automatically tags and classifies it, setting tiered policies for subsequent processing.
Automatic Desensitization: Data Is Usable, but "Key Content Is Invisible"
AI needs data but does not need to see all the original content. The middle platform supports various desensitization strategies (replacement, masking, encryption, etc.), allowing models to learn language structures and business logic without exposing real information, achieving a balance of "data usability without leakage."
Access Control: Preventing AI from "Misusing" Data
Through granular permission management mechanisms, the middle platform sets access scopes for different departments and AI applications, clarifying which data can be used for internal queries, which can be used for model training, and which is limited to manual review. Permission settings no longer rely on manual configuration by operations or development teams but are dynamically managed through role policies and automatic tagging.
Log Auditing: Full Traceability of AI Data Usage
Every piece of data—who accessed it, which model used it, and at which stage it was processed—is recorded in detail by the unstructured data middle platform. This ensures that when model output anomalies occur or compliance audits require tracing, enterprises can quickly identify the source and responsibility, achieving full traceability and explainability.
Controllable AI: Not Just Risk Management, but Future Competitiveness
Enterprises that cannot clearly control the underlying data used for AI training and inference will face significant compliance risks amid increasingly stringent regulations. Once data leakage occurs, it will not only damage user trust, but also may lead to heavy fines and brand damage.
