AI安全加码新规解读：企业合规需要重点关注的五项措施

For companies preparing for the 2026 AI regulatory cycle, the main challenge is no longer simply deciding whether a system uses artificial intelligence. Teams must be able to explain what the system does, which data and models support it, what risks it creates, and how the organization responds when something goes wrong. The available policy materials center on the EU AI Act and its 2026 implementation developments, including guidance on scope, AI systems, models, computer code, and AI components, as well as Regulation (EU) 2026/1744 on simplifying the implementation of harmonized AI rules.

The policy direction does not support a narrow “check the box” approach. A practical governance system should connect classification, technical review, data management, incident handling, and reporting. The following five measures provide a useful framework for compliance and security teams. They should be mapped against the exact obligations that apply to each system, model, role, and market activity rather than treated as a substitute for legal analysis.

1. Establish a documented model audit process

Model auditing should begin with an inventory of the AI systems and models used, developed, supplied, or integrated by the company. The implementation guidance specifically addresses the scope of the rules and the relationship between AI systems, models, computer code, and AI components. That makes system boundaries an important starting point: a team cannot assess its obligations reliably if it cannot identify the relevant technical and organizational components.

An audit record should describe the system’s intended purpose, operating context, responsible business owner, data sources at a high level, human oversight arrangements, and known limitations. The record should also explain how the system was tested before deployment and how its performance and risks are reviewed afterward. The point is not to create a single universal test for every model. A customer-service assistant, an internal summarization tool, and a system used in a sensitive decision process may require different review depth.

Audit evidence should remain connected to actual release decisions. If a model is changed, moved into a new use case, connected to a new data source, or supplied to another party, the organization should determine whether the previous review remains sufficient. This creates a defensible trail showing not only that an assessment occurred, but also why the company considered deployment acceptable at that time.

2. Build data provenance into the AI inventory

Data provenance means being able to explain where important data came from, how it was handled, and how it entered the AI system’s lifecycle. It is broader than keeping a copy of a dataset. For compliance purposes, teams need a practical record of the data’s role in training, testing, retrieval, fine-tuning, or operation, together with the people or functions responsible for approving its use.

The record does not need to expose confidential information in the article’s sense, nor does it require every organization to use the same technical architecture. It should, however, make material decisions traceable. A reviewer should be able to determine which data sources were considered relevant, what restrictions applied, whether the data was transformed, and whether a later change could affect the system’s risk profile.

Data lineage is especially important when a general-purpose model or external AI component is incorporated into a larger product. Responsibility may be divided among providers, deployers, integrators, and other participants. Clear internal records help the company distinguish what it controls from what it receives from another party, while also identifying the information that must be requested or retained before deployment.

3. Perform risk assessments before and after deployment

Risk Assessment should be tied to the system’s intended purpose and real operating environment. A label such as “AI tool” says little about the consequences of an error. The relevant questions are whether the system influences people, handles sensitive activities, affects access to services or opportunities, or operates with limited human review.

A sound assessment should examine foreseeable harms, affected groups, potential misuse, reliability concerns, security exposure, and the consequences of incorrect or misleading output. It should also document the controls selected to reduce those risks. Those controls might include human review, restricted access, output verification, clear user notices, usage limitations, or a decision not to deploy the system in a particular context.

Assessment should not end at launch. The organization should revisit its conclusions when the purpose, model, data, users, integration, or operating conditions change. The EU AI Act materials indicate that implementation depends on understanding the scope and classification of AI-related systems and models. In practice, that means classification should be treated as a maintained governance decision, not a one-time administrative label.

4. Prepare an AI-specific incident response path

An AI incident may involve more than an outage or a cybersecurity breach. It can include harmful output, systematic errors, unauthorized disclosure, unsafe automation, unexpected behavior after a model change, or a failure of required human oversight. Security and compliance teams should therefore define how an AI-related event is recognized, escalated, investigated, contained, and documented.

The response process should identify the business owner, security contact, legal or compliance reviewer, and technical personnel who can suspend or restrict the affected system. It should also preserve the information needed to understand what happened, such as the relevant model or system version, operating context, input and output records where permitted, user reports, and actions taken during containment.

A response plan is only useful if it fits the company’s existing incident management structure. The durable approach is to add AI-specific decision points to established processes rather than create an isolated procedure that no operational team uses. Those decision points should address whether the incident affects individuals, other customers, downstream systems, contractual commitments, or regulatory responsibilities.

5. Turn compliance reporting into an ongoing governance record

A compliance report should be more than a statement that the company uses “responsible AI.” It should connect the organization’s obligations to evidence: which systems were identified, how they were classified, what assessments were completed, what controls were applied, which incidents occurred, and who approved the relevant decisions.

Reports should distinguish confirmed facts from open questions. When policy details, implementation guidance, or the company’s own classification remain under review, that status should be recorded clearly instead of being presented as settled. The available 2026 materials include both implementation guidance and an amendment described as simplifying the implementation of harmonized AI rules, so teams should maintain version-aware records and confirm which legal text governs their specific situation.

A useful reporting cycle also gives management a view of unresolved risks. Instead of focusing only on completed forms, the report should show overdue reviews, systems awaiting classification, missing supplier information, untested changes, and incidents that require follow-up. This makes compliance reporting a decision tool for governance rather than a document produced only before an audit.

How to organize the five measures

These measures work best as one governance loop. The inventory defines what must be reviewed; provenance records support the review; risk assessment determines the necessary safeguards; incident response tests whether those safeguards work in practice; and reporting gives management a durable record of decisions and gaps.

Before claiming that the organization is ready for the 2026 requirements, teams should be able to answer five practical questions:

• Can we identify every relevant AI system, model, and external component?




• Can we explain the origin and use of material data?




• Can we show why each system received its risk classification and deployment decision?




• Can we contain and investigate an AI-related incident?




• Can we produce evidence that remains accurate when systems and rules change?

The most reliable next step is to select a limited set of representative AI uses, trace them through all five measures, and document where evidence is missing. That exercise reveals governance weaknesses more clearly than a broad policy statement. It also helps the company adapt as implementation guidance and harmonized rules continue to be clarified.