LLM Prompt Integrity: Governing the Gateway to Generative AI is a practitioner's guide to securing the most powerful—and most fragile—part of today's AI systems: the prompt field. In modern organisations, large language models no longer sit at the edges of workflows; they now drive legal research, financial analysis, customer support, and internal decision-making through a simple text box that quietly acts as a high-risk API. This book argues that treating prompts as casual chat has created an integrity gap—and that the real question is no longer "Did the model hallucinate?" but "What are we willing to accept in the first place?". Ashish Chugh introduces Prompt Integrity (PI) as a first-class discipline focused on input trust, defining clear pillars for securing LLM applications: Data Integrity (can the surrounding context and RAG sources be trusted?), Structural Integrity (is the prompt's shape, schema, and token budget sound?), and Behavioral Integrity (what is this prompt trying to achieve, and is that compatible with policy, ethics, and law?). Through a running example—a Legal Research Copilot that sits on top of sensitive case law and internal documents—the book shows how to embed PI into real systems using integrity pipelines, prompt abstraction layers (PALs), validator agents, taint tags, PII masking, and zero-trust prompt routing. You'll learn how to: --Design architectural Integrity Pipelines (pre-processing, sanitisation, validation, post-execution cross-check) that sit in front of any model, not inside it. --Replace ad-hoc prompt strings with structured prompt languages, schemas, and entity-aware validation tied to your systems of record. --Detect and defend against direct prompt injection, RAG poisoning, multimodal injection, and exfiltration attacks using canonicalization, taint tracking, semantic fingerprinting, and integrity scoring. --Build governance around prompts: lifecycle logging from keystroke to output, RBAC on system prompts and corpora, incident response for prompt-poisoning events, and future-facing patterns such as signed intent objects and ledger-backed attestations. Written for AI/ML engineers, security teams, product owners, and technical leaders in regulated domains such as law, finance, and healthcare, this book assumes you already know how to call an LLM API and care about running it in front of clients, regulators, and internal audit without losing sleep.
