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Language model

As explained in Authorization in Action

A large language model is a machine-learning model trained on extensive text data to process and generate language.

A language model processes context and generates language. In policy work, large language model (LLM) systems include chat interfaces, copilots, retrieval-augmented generation (RAG), and agentic applications. section 17.0

Language models can explain policies, summarize rules, explore hypothetical access situations, and suggest policy changes. They remain advisory: policy engines, logs, replay tools, and human reviewers determine authoritative outcomes. section 17.0 section 17.2.4

Analyzing suspicious access

A deterministic rule flags support engineers accessing tenant data outside approved escalation windows. A replay tool verifies the historical decision, while the language model groups cases, identifies affected tenants and workflows, and drafts an incident narrative for review. section 17.2.4

How Authorization in Action covers it

How deep it goes
Explains advisory uses and authority boundaries, but stops short of model internals or generative-model implementation.
Examples
Uses policy explanation, hypothetical access exploration, flagged-event grouping, tenant identification, and incident-narrative drafting.
What you can do after
Diagnose access patterns with language-model summaries while preserving authoritative findings in deterministic checks and human review.
How it connects
Connects language models with authorization, policy engines, policy replay, audit logs, retrieval-augmented generation, and human review.
Cover of Authorization in Action
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