AI risk register for fashion: what to log and review quarterly

Fashion companies deploying AI for design, merchandising, demand forecasting and customer service need a formal risk register that logs five categories of threat: algorithmic bias, hallucination, data leakage, vendor lock-in and regulatory risk. A quarterly review process allows executives to track incidents, assess severity and adjust controls before problems escalate. This article provides a template structure and explains what to record in each section.

What belongs in an AI risk register?

An AI risk register is a living document that lists every identified risk associated with AI systems, assigns ownership, records likelihood and impact scores, and tracks mitigation actions. For fashion businesses, the register should cover all AI deployments: generative design tools, sizing and fit algorithms, chatbots, visual search, inventory optimisation and any third-party platforms that use machine learning.

Each entry should include the risk description, the system or vendor it relates to, the business function affected, the person responsible for monitoring, the current status and the date of last review. Risks are typically scored on likelihood and impact scales, with high-risk items flagged for immediate action.

How should fashion companies log bias risk?

Algorithmic bias in fashion can manifest in product recommendations that exclude certain body types, hiring tools that screen out diverse candidates, or marketing content that reinforces stereotypes. The register should log any instance where an AI system produces discriminatory outputs or decisions that do not reflect the brand's stated values or the diversity of its customer base.

Key entries for bias risk include the data sources used to train models, the demographic groups potentially affected, and the testing protocols in place. Fashion companies should document whether algorithms have been audited for fairness across gender, size, age, ethnicity and geography. If a fit recommendation tool consistently fails for plus-size customers, that incident goes in the register with severity score, date discovered and corrective action taken.

What does hallucination risk mean for fashion brands?

Hallucination refers to AI-generated content that is factually incorrect, misleading or nonsensical. In fashion, this risk arises when generative tools create product descriptions with false fabric claims, chatbots provide wrong care instructions, or design assistants suggest supply chain partners that do not exist. Any customer-facing or operational error caused by fabricated AI output should be logged.

The register should note which generative models are in use, whether outputs are reviewed by humans before publication, and the frequency of detected errors. Fashion companies should also track the commercial impact: if a chatbot gave incorrect sizing advice that led to returns, record the cost and the date the issue was resolved.

How do you track data leakage and vendor lock-in?

Data leakage occurs when sensitive information,design sketches, supplier pricing, customer lists,is exposed through AI systems, either via inadequate access controls or by uploading proprietary data to third-party platforms. The risk register should document which datasets are used by each AI tool, where data is stored, and whether confidentiality agreements are in place.

Vendor lock-in arises when a fashion company becomes dependent on a single AI provider whose models, APIs or data formats are not portable. If switching costs are high or data cannot be exported, the register should flag the dependency, note the contract term and outline contingency plans. Both risks require named owners, usually from IT, legal or procurement, and should be reviewed each quarter as vendors update terms or systems expand.

What regulatory risks apply to fashion AI in 2025 and 2026?

The EU AI Act entered into force in August 2024 and began phased implementation in 2025, with bans on certain prohibited practices taking effect and obligations for high-risk AI systems coming into force. Fashion companies operating in Europe must assess whether their AI systems fall into high-risk categories, such as those used in employment decisions, and maintain technical documentation and risk assessments.

The risk register should list each AI system, its regulatory classification, the compliance deadlines and the responsible legal or compliance officer. Companies should also monitor guidance from national regulators and trade bodies, as interpretation and enforcement practices continue to develop. Any regulatory notices, audits or fines should be logged with dates and outcomes.

How often should fashion executives review the register?

A quarterly review cycle allows leadership to spot trends, assess whether mitigation measures are working and decide which risks require escalation. The review meeting should include representatives from IT, legal, merchandising, design and any business unit deploying AI. Each risk is discussed in turn: has the likelihood or impact changed? Have new incidents occurred? Are controls adequate?

The quarterly cadence balances the need for oversight with the pace of AI deployment in fashion. Monthly reviews may be excessive for stable systems, while annual reviews risk missing emerging threats. The register should be updated between meetings whenever a new system launches, an incident occurs or regulations change, but the formal review ensures accountability and visibility at executive level.

Below is a sample structure for a fashion AI risk register:

By maintaining a risk register and committing to quarterly reviews, fashion companies create a structured process for managing AI deployment. The register becomes a shared reference point for technical, commercial and legal teams, ensuring that risks are visible, ownership is clear and mitigation efforts are tracked over time.