17 AI in policy practice
This chapter covers
- Why AI systems must not act as authorization decision-makers
- How AI assists policy authoring and design without becoming authoritative
- Using AI in runtime analysis to compare design-time intent with observed behavior
- Enforcing authorization before retrieval in RAG systems
- How architecture enables accountability and governance in AI-enabled systems
As AI systems become part of daily life, they are increasingly embedded in workflows that handle sensitive data and consequential actions. They summarize records, generate recommendations, prepare responses, and sometimes initiate operations on behalf of employees or customers. That puts AI systems close to authorization questions: what data should be included, what actions may be taken, and under whose authority the system is acting. As a result, many teams are tempted to delegate authorization decisions to AI. That idea seems reasonable, but it’s usually a mistake.
Authorization exists to enforce the intent behind access decisions. It relies on determinism, explainability, auditability, and predictable failure modes. AI systems excel at many tasks, such as interpretation, synthesis, and exploration. When these strengths are applied appropriately, AI can significantly enhance the design, understanding, testing, and governance of policies without becoming the component responsible for access determination.