Natural Language Generation

Natural Language Generation (NLG) is a subfield of artificial intelligence focused on automatically producing human-readable text from structured or semi-structured data. It is the counterpart to Natural Language Understanding (NLU), which interprets text input. In a risk management context, NLG systems and their training data are critical information assets, subject to the asset management and access control requirements of ISO/IEC 27001. The development and deployment of NLG should be guided by the NIST AI Risk Management Framework (AI RMF) to ensure reliability and fairness. When processing personal data, NLG outputs must comply with regulations like GDPR or Taiwan's PIPA, particularly concerning data minimization and purpose limitation, to mitigate data breach risks.

NLG automates and enhances risk reporting, improving efficiency and consistency. A typical implementation involves three steps: 1) Data Integration: Consolidate and structure risk data from various sources like internal control systems and audit logs. 2) Template Configuration: Define report structures and narrative logic based on regulatory requirements (e.g., SEC filings) or internal standards. 3) Automated Generation & Human Review: The NLG model generates draft reports, which are then validated by risk managers or compliance officers for accuracy and context. For instance, a global bank uses NLG to create daily market risk summaries, reducing preparation time from hours to minutes and enabling faster, data-driven decisions.

Taiwan enterprises face three key challenges. First, the scarcity of high-quality, domain-specific training data in Traditional Chinese affects model accuracy. This can be mitigated by fine-tuning global models with proprietary, anonymized data, governed by a strict data handling policy compliant with Taiwan's PIPA. Second, protecting the NLG model as intellectual property is crucial. The model itself is a trade secret vulnerable to extraction attacks. Solutions include implementing technical safeguards like lexical watermarking and legally defining the model as a trade secret under Taiwan's Trade Secrets Act, enforced with ISO/IEC 27001 access controls. Third, ensuring the accuracy and compliance of generated content is vital. A 'human-in-the-loop' review process, where experts validate outputs before publication, is essential to manage this risk, aligning with the NIST AI RMF's principles of reliability and accountability.

Winners Consulting specializes in natural language generation for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact