AI Production Readiness Checklist
A practical checklist for leaders who need to prove an AI system can survive pressure before it goes live. A high-performing AI model is not the same thing as a secure AI system.That is where many organizations are getting ahead of themselves.They look at benchmark results, demo performance, pilot feedback, and early user adoption and assume the system is ready for production.But accuracy only tells you how the system behaves when the environment behaves.It does not tell you what happens when someone tries to bend the system.That is the gap this checklist is designed to close.AI systems do not only fail when they give a bad answer. They fail when untrusted input influences trusted behavior. They fail when retrieved content is treated like authority. They fail when a prompt becomes a control. They fail when a model can call tools without proper approval gates. They fail when no one can reconstruct how a bad output became a business action.If your AI system touches customer data, security operations, legal language, regulated workflows, code, financial processes, executive decision support, or business automation, you need more than confidence.You need evidence.The AI Production Readiness Evidence Checklist gives leaders and technical teams a practical way to evaluate whether an AI system is ready to go live.It helps you ask:Can hostile input cross a trust boundary?Can retrieved content override policy?Can the model select tools it should not use?Can a low-privilege user expose higher-privilege information?Can the system act outside the user’s authority?Can the organization reconstruct what happened if the system fails?This checklist is designed for serious teams trying to move fast without creating unmanaged AI risk.What You GetInside the checklist, you will get a practical production-readiness framework covering:AI use-case risk tieringA simple way to separate low-risk, medium-risk, and high-risk AI systems so governance does not become a one-size-fits-all bottleneck.Architecture evidenceThe system diagram and technical evidence leaders should ask for before an AI system goes live.Data source and retrieval trust mappingA practical way to distinguish trusted content from untrusted content so retrieval does not become a hidden failure path.Context assembly reviewQuestions that expose whether user input, retrieved content, memory, tool outputs, and system instructions are being blended into one unsafe prompt envelope.Tool permission matrixA way to evaluate whether the model or agent has too much authority across APIs, databases, code, records, approvals, or external actions.Adversarial test report guidanceA practical checklist for testing whether hostile input can manipulate retrieval, memory, tool use, permissions, outputs, or downstream workflow behavior.Known failure registerA format for documenting failure modes, severity, business impact, owners, compensating controls, and residual risk decisions.Human approval and escalation modelA way to define when AI can act, when it can only recommend, when a human must approve, and who owns escalation.Logging and investigation planThe evidence needed to reconstruct the path from input to retrieval to context to model output to tool call to final action.Runtime monitoring planWhat to monitor after launch so the organization can detect abuse, abnormal tool selection, suspicious prompts, retrieval anomalies, and unsafe behavior changes.Production release sign-offA leadership-ready sign-off structure for approving AI systems against their risk tier before they enter production.Who This Is ForThis checklist is built for:CISOs responsible for AI security and enterprise risk.CAIOs building AI governance and operating models.CIOs accountable for technology delivery and business integration.Product leaders shipping AI-enabled capabilities.Engineering leaders building AI systems with retrieval, tools, memory, or workflow automation.Security architects reviewing AI applications before production.GRC leaders trying to turn AI policy into evidence.Business executives who need to approve AI use cases without relying on demo confidence.If you are involved in approving, securing, governing, building, or deploying AI systems, this checklist gives you a practical standard for production readiness.
Get it → ghancock.gumroad.com