Information Hazards

Mitigation strategy

1. Implement privacy-enhancing technologies (PETs), such as Differential Privacy and k-anonymity, during model training and fine-tuning to mathematically limit the model's ability to memorize and later disclose individual data points. Furthermore, enforce rigorous Data Anonymization and Redaction protocols on all training datasets and user inputs prior to processing. 2. Enforce a Secure-by-Design architecture with mandatory Role-Based Access Controls (RBAC) to apply the principle of least privilege regarding who can interact with the LLM and the permissions granted to AI agents. Complement this with Strict Output Filtering and context-aware mechanisms designed to inspect and prevent the accidental or malicious disclosure of sensitive information in the model's responses. 3. Establish comprehensive Security Information and Event Management (SIEM) and audit logging practices to continuously monitor LLM interactions, tool calls, and data access. This process must include regular post-deployment security audits, penetration testing, and tracking of Key Risk Indicators (KRIs) to detect anomalous behavior, identify vulnerabilities, and measure the sustained effectiveness of the deployed mitigation controls.