Governance for Large Language Models and Generative AI

Large Language Models (LLMs) and Generative Artificial Intelligence (AI) introduce unique governance challenges beyond traditional Machine Learning (ML). Key concerns include prompt injection attacks that bypass safety filters, Retrieval Augmented Generation (RAG) systems surfacing unvetted or sensitive content, provenance of generated text (was this human written or model output?), and the inability to log raw prompts containing Personally Identifiable Information (PII) without violating General Data Protection Regulation (GDPR). Production Large Language Model (LLM) governance requires logging prompts (with redaction), contexts retrieved from knowledge bases, safety classifier decisions, and outputs before and after post processing. At 5,000 Queries Per Second (QPS) with average prompt plus completion size of 2 kilobytes, this generates 864 gigabytes per day, requiring the same tiered storage strategies as prediction journals. Prompt and output logging must balance auditability and privacy. Raw prompts may contain sensitive user data (medical history, financial information). One approach is to log prompt templates and parameter hashes rather than full text, reproduce by rehydrating from secure vaults on audit demand. Another is to run Personally Identifiable Information (PII) detection and redact before logging. Microsoft and OpenAI emphasize safety evaluations as release gates. Before any Large Language Model (LLM) deployment, run red team test suites covering prompt injection, jailbreaks, toxic output generation, and factual consistency. Store test results in the model card. For Retrieval Augmented Generation (RAG), log provenance of retrieved chunks (document ID, chunk offset, retrieval score) so outputs can be traced to source material. This enables content takedown. If a source document is later deemed inappropriate, lineage identifies all outputs that referenced it. Safety classifiers act as guardrails. Input classifiers check prompts for policy violations (hate speech, requests for harmful instructions). Output classifiers scan completions before returning to users. For high risk domains (healthcare advice, financial guidance), require human review before publishing, with a Service Level Agreement (SLA) of 2 hours. Log classifier decisions (prompt_id, classifier_version, score, threshold, decision) in the audit trail. When a classifier is updated, A/B test against the previous version to ensure false positive rates do not spike. A 10 percent increase in false positives can degrade user experience significantly. Meta invests in scaled fairness and safety tooling that evaluates Large Language Model (LLM) outputs across demographics and languages, detecting differential error rates. Online learning in Large Language Models (LLMs) (for instance, fine tuning on user feedback) requires governance controls. Adversaries can inject malicious feedback to poison the model. Use update rate limits (maximum 1 percent parameter change per day), canary buffers that hold updates for manual review, and separate staging environments where updates are tested before production promotion. Maintain an immutable record of update deltas and the feedback that triggered them. For systems serving European Union (EU) users under the AI Act, high risk applications require conformity assessments and ongoing monitoring reports submitted to regulators.

Healthcare chatbot logs {"prompt_template": "symptom_query", "params_hash": "hmac:abc123", "retrieved_docs": ["mayo_clinic_id:4521", "webmd_id:9912"], "safety_input": "pass", "safety_output": {"medical_advice_score": 0.85, "threshold": 0.9, "decision": "allow"}, "completion_hash": "sha256:def456"}, redacts patient name from prompt before logging

Microsoft Responsible AI review for customer service Large Language Model (LLM) includes red team testing with 5,000 adversarial prompts (jailbreaks, bias probes), documents results showing 99.2 percent safety classifier recall on toxic outputs, includes this in model card before production approval

Retrieval Augmented Generation (RAG) system for legal research logs retrieved case citations (case_id, jurisdiction, relevance_score), when a precedent is later overturned, lineage identifies 1,247 outputs that cited it, system sends notifications to users who received those outputs for review