{ "item_title" : "From Data to Authority", "item_author" : [" Sheeraz Ali "], "item_description" : "Your data has lineage. Your AI has citations. But can your system explain why an answer should be trusted?A database contains the latest record. A search index returns an older version. An AI assistant confidently summarizes information that has already been superseded. Every component may be functioning as designed, yet the system produces contradictory answers.The problem is not simply data quality, synchronization, or retrieval accuracy. It is authority: deciding what information a system is justified in relying on, for which purpose, under which conditions, and at what time.From Data to Authority presents a practical architecture for making those decisions explicit, traceable, and testable across software, data platforms, and AI systems.Rather than introducing another governance platform or demanding a complete technology overhaul, the book develops a disciplined approach to connecting evidence, contextual authority, publication rights, reproducible releases, and runtime behavior.Across 34 chapters in eight parts, discover how to: Turn evidence into governed authority. Distinguish sources, observations, supported claims, and deterministic calculations. Establish clear rules for identity, scope, time, conflicting evidence, and uncertainty.Separate truth from permission. Understand why accurate information may still be unsuitable for publication, redistribution, commercial use, or AI consumption, and how rights and obligations survive transformation.Build verifiable information releases. Design canonical state, deterministic projections, reproducible builds, integrity checks, release activation, correction, and revocation without allowing downstream representations to become competing sources of truth.Make better architectural and economic decisions. Evaluate build-time versus request-time processing, server-thin/client-heavy systems, static distribution, bounded client execution, and offline operation while accounting for costs and operational tradeoffs.Govern RAG and AI agents. Prevent retrieval indexes and generated answers from silently replacing authoritative state. Separate an agent's ability to read and reason from its permission to execute consequential actions.Prove that controls actually work. Use diagnostics, scenarios, acceptance tests, failure exercises, and evidence-based assurance to challenge architectural assumptions before relying on them.From principles to practical decisionsThe book culminates in the Governed Authority Decision Pack, a structured, ten-section approach to documenting the evidence, ownership, rights, release contracts, runtime boundaries, economics, tests, and implementation decisions behind a governed information system.Equally important, it explains when ordinary databases, APIs, and conventional application architecture are already sufficient, helping teams avoid unnecessary complexity.Who should read this book?Software architects, CTOs, technical founders, platform and data engineers, AI architects, and governance or assurance professionals responsible for systems where inaccurate, outdated, conflicting, or unauthorized information can have meaningful consequences.Whether you are building a public information platform, a knowledge system, an AI assistant, or an agent-enabled application, From Data to Authority provides a systematic way to ask the questions that matter before treating an answer as trustworthy.Because knowing where an answer came from is not the same as knowing whether your system should rely on it.CoreVecta Labs Systems Library - Book 2A standalone companion to From Intent to Revenue.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/9/79/816/156/9798161564684_b.jpg", "price_data" : { "retail_price" : "49.99", "online_price" : "49.99", "our_price" : "49.99", "club_price" : "49.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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