An AI governance framework like ISACA's AISAM provides the blueprint, but building a secure system requires translating those principles into specific, technical controls. For Large Language Models (LLMs), this means mapping high-level requirements directly onto the model's lifecycle, from data ingestion to user-facing inference.

This process ensures that governance isn't an afterthought or a separate auditing function. Instead, it becomes an integral part of the engineering workflow, embedded in the architecture itself. We'll explore how to apply AISAM domains to the distinct phases of an LLM's existence, transforming abstract policy into tangible security measures.

The LLM lifecycle can be broken into four primary stages, each with unique risks and corresponding governance requirements. Mapping AISAM controls to these stages creates a clear implementation path.

1. Data Sourcing & Preparation This initial stage is where the AISAM Data and AI Models domain is paramount. Controls must verify the provenance of training data, ensuring it's ethically sourced and appropriately licensed. Automated scanners should be used to detect and mitigate biases related to protected attributes like race, gender, or age before this data ever enters a training pipeline. Data must be sanitized to remove personally identifiable information (PII) or other sensitive content.

2. Model Training & Fine-Tuning Here, the focus shifts to the Key AI System Components and Security domains. The training environment itself—the compute instances, storage buckets, and networking—must be hardened. Access controls should be strictly limited to authorized ML engineers. Every training run must be logged, capturing the dataset version, code commit hash, and hyperparameters used. This creates an auditable chain of custody for the model artifact, ensuring reproducibility and integrity.

3. Deployment & Inference When the model is live, governance concerns are different. While training governance focuses on integrity and reproducibility, inference governance is about real-time security and safe usage. Controls here map to the AISAM Security domain. This includes securing the API endpoint with authentication and rate limiting, implementing robust input validation to guard against prompt injection attacks, and filtering model outputs to prevent data leakage or the generation of harmful content. The goal is to create a secure wrapper around the model that enforces usage policies.

4. Monitoring & Retraining Post-deployment, the Performance Monitoring domain becomes critical. Automated systems should monitor for model drift, where performance degrades as real-world data deviates from the training data. All prompts and responses (with PII removed) must be logged for auditing and analysis. This creates a secure feedback loop that informs when a model needs to be retrained or fine-tuned, starting the cycle anew with fresh data and updated governance checks.

Technical controls are ineffective without clear ownership. A responsibility matrix, such as a RACI chart, is essential for assigning governance tasks to specific roles within the organization. This clarifies who is responsible for implementing a control, who is accountable for its success, and who needs to be consulted or informed.

This matrix establishes clear lines of authority. The Product Owner, for instance, is accountable for defining the organization's risk appetite for generative AI outputs—deciding what level of factual inaccuracy or inappropriate content is acceptable. The ML Engineer is then responsible for implementing the technical filters to enforce that policy, while the Security Officer is accountable for auditing their effectiveness.

By mapping a framework like AISAM directly to the LLM lifecycle and assigning clear ownership for each control, an organization can build a robust, auditable, and secure AI system. Governance becomes a proactive, embedded function rather than a reactive, compliance-driven exercise.

This structured approach ensures security and accountability are built in from the start.