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Paperback. Zustand: new. Paperback. You ran the AI security audit. You have the model inventory, the non-human identity count, the vendor map, and the data flows. The audit committee accepted it. Then you walked back to your desk and realized the environment it describes changes every week. Someone deployed an agent on Tuesday. A vendor activated an AI feature inside a product you already own on Wednesday and did not tell you.Nothing in the audit was wrong. An audit is an instrument for describing a state, and what a security leader is accountable for is a rate of change.An audit produces a photograph. What you are accountable for is a moving object.This is the book about what happens next.The AI IT Security Implementation & Strategy is Volume VI of The Operating Discipline for AI Library and the second volume of its security pillar. It converts the audit's evidence base into a running program across the four domains a security leader owns: security governance and risk management, security operations, third-party and supply chain risk, and data protection and privacy.Each domain receives three chapters that ask the same three questions in the same order. What has AI broken here, working from the artifacts the audit produced rather than from theory. How does the same technology rebuild the capability, because nothing operating at human speed can supervise decisions made at machine speed. And what does the endgame look like, closing with a one-year target operating model that names an owner, a cadence, a quarterly milestone, and a budget line.Inside, you will build: The Human Supervision Matrix, which defines machine autonomy per action class rather than per tool, so the policy survives the next product release instead of being reopened by it.The Red-Button Protocol, which converts suspension from a paragraph in a policy document into a rehearsed capability with a named executor, a dependency map, and a measured time to confirmed stop.The Agent Authority Chain, tracing delegation through six elements from human principal to agent to sub-agent to tool call, so that "who authorized this" has an answer that survives an inquiry.Adaptive AI Trust Scores, replacing the annual vendor questionnaire with a continuously maintained measure that carries a direction, so a degrading vendor is visible before the renewal conversation rather than during it.The AI Decision Provenance Chain, reconstructing what data went in, what processed it, what the decision was, and what could have altered it.AI Security Debt, a countable register of accepted risk that carries an age dimension, because a three-year-old exception and a three-month-old exception are not the same governance failure.The AI Security Scorecard, the quarterly board instrument, with the metric definitions and trend fields already structured.A closing chapter sequences the first ninety days, because four domains launched at once with one team and one budget become zero programs by month four. The appendix supplies twenty-two working instruments as populatable worksheets, and a framework alignment section maps the guidance to the NIST AI Risk Management Framework, ISO/IEC 42001, the EU AI Act, the OWASP top ten lists, MITRE ATLAS, and Google SAIF.Written for chief information security officers, security directors, and security managers. Every page assumes the reader already knows what a control is, what an identity is, and what happens when neither one is inventoried.Somewhere ahead of you, on a date you do not control, someone will ask you to prove your AI exposure is governed. A regulator. An enterprise customer's security team. An insurance carrier at renewal. Or your own audit committee.The audit tells you wh Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Zustand: New.

Zustand: New.

Zustand: New.

Zustand: New.

Zustand: New.

Hardcover. Zustand: new. Hardcover. You ran the AI security audit. You have the model inventory, the non-human identity count, the vendor map, and the data flows. The audit committee accepted it. Then you walked back to your desk and realized the environment it describes changes every week. Someone deployed an agent on Tuesday. A vendor activated an AI feature inside a product you already own on Wednesday and did not tell you.Nothing in the audit was wrong. An audit is an instrument for describing a state, and what a security leader is accountable for is a rate of change.An audit produces a photograph. What you are accountable for is a moving object.This is the book about what happens next.The AI IT Security Implementation & Strategy is Volume VI of The Operating Discipline for AI Library and the second volume of its security pillar. It converts the audit's evidence base into a running program across the four domains a security leader owns: security governance and risk management, security operations, third-party and supply chain risk, and data protection and privacy.Each domain receives three chapters that ask the same three questions in the same order. What has AI broken here, working from the artifacts the audit produced rather than from theory. How does the same technology rebuild the capability, because nothing operating at human speed can supervise decisions made at machine speed. And what does the endgame look like, closing with a one-year target operating model that names an owner, a cadence, a quarterly milestone, and a budget line.Inside, you will build: The Human Supervision Matrix, which defines machine autonomy per action class rather than per tool, so the policy survives the next product release instead of being reopened by it.The Red-Button Protocol, which converts suspension from a paragraph in a policy document into a rehearsed capability with a named executor, a dependency map, and a measured time to confirmed stop.The Agent Authority Chain, tracing delegation through six elements from human principal to agent to sub-agent to tool call, so that "who authorized this" has an answer that survives an inquiry.Adaptive AI Trust Scores, replacing the annual vendor questionnaire with a continuously maintained measure that carries a direction, so a degrading vendor is visible before the renewal conversation rather than during it.The AI Decision Provenance Chain, reconstructing what data went in, what processed it, what the decision was, and what could have altered it.AI Security Debt, a countable register of accepted risk that carries an age dimension, because a three-year-old exception and a three-month-old exception are not the same governance failure.The AI Security Scorecard, the quarterly board instrument, with the metric definitions and trend fields already structured.A closing chapter sequences the first ninety days, because four domains launched at once with one team and one budget become zero programs by month four. The appendix supplies twenty-two working instruments as populatable worksheets, and a framework alignment section maps the guidance to the NIST AI Risk Management Framework, ISO/IEC 42001, the EU AI Act, the OWASP top ten lists, MITRE ATLAS, and Google SAIF.Written for chief information security officers, security directors, and security managers. Every page assumes the reader already knows what a control is, what an identity is, and what happens when neither one is inventoried.Somewhere ahead of you, on a date you do not control, someone will ask you to prove your AI exposure is governed. A regulator. An enterprise customer's security team. An insurance carrier at renewal. Or your own audit committee.The audit tells you wh Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Zustand: New.

Zustand: New.

Paperback. Zustand: new. Paperback. You ran the AI security audit. You have the model inventory, the non-human identity count, the vendor map, and the data flows. The audit committee accepted it. Then you walked back to your desk and realized the environment it describes changes every week. Someone deployed an agent on Tuesday. A vendor activated an AI feature inside a product you already own on Wednesday and did not tell you.Nothing in the audit was wrong. An audit is an instrument for describing a state, and what a security leader is accountable for is a rate of change.An audit produces a photograph. What you are accountable for is a moving object.This is the book about what happens next.The AI IT Security Implementation & Strategy is Volume VI of The Operating Discipline for AI Library and the second volume of its security pillar. It converts the audit's evidence base into a running program across the four domains a security leader owns: security governance and risk management, security operations, third-party and supply chain risk, and data protection and privacy.Each domain receives three chapters that ask the same three questions in the same order. What has AI broken here, working from the artifacts the audit produced rather than from theory. How does the same technology rebuild the capability, because nothing operating at human speed can supervise decisions made at machine speed. And what does the endgame look like, closing with a one-year target operating model that names an owner, a cadence, a quarterly milestone, and a budget line.Inside, you will build: The Human Supervision Matrix, which defines machine autonomy per action class rather than per tool, so the policy survives the next product release instead of being reopened by it.The Red-Button Protocol, which converts suspension from a paragraph in a policy document into a rehearsed capability with a named executor, a dependency map, and a measured time to confirmed stop.The Agent Authority Chain, tracing delegation through six elements from human principal to agent to sub-agent to tool call, so that "who authorized this" has an answer that survives an inquiry.Adaptive AI Trust Scores, replacing the annual vendor questionnaire with a continuously maintained measure that carries a direction, so a degrading vendor is visible before the renewal conversation rather than during it.The AI Decision Provenance Chain, reconstructing what data went in, what processed it, what the decision was, and what could have altered it.AI Security Debt, a countable register of accepted risk that carries an age dimension, because a three-year-old exception and a three-month-old exception are not the same governance failure.The AI Security Scorecard, the quarterly board instrument, with the metric definitions and trend fields already structured.A closing chapter sequences the first ninety days, because four domains launched at once with one team and one budget become zero programs by month four. The appendix supplies twenty-two working instruments as populatable worksheets, and a framework alignment section maps the guidance to the NIST AI Risk Management Framework, ISO/IEC 42001, the EU AI Act, the OWASP top ten lists, MITRE ATLAS, and Google SAIF.Written for chief information security officers, security directors, and security managers. Every page assumes the reader already knows what a control is, what an identity is, and what happens when neither one is inventoried.Somewhere ahead of you, on a date you do not control, someone will ask you to prove your AI exposure is governed. A regulator. An enterprise customer's security team. An insurance carrier at renewal. Or your own audit committee.The audit Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Zustand: New.

Zustand: New.

Paperback. Zustand: new. Paperback. You ran the AI security audit. You have the model inventory, the non-human identity count, the vendor map, and the data flows. The audit committee accepted it. Then you walked back to your desk and realized the environment it describes changes every week. Someone deployed an agent on Tuesday. A vendor activated an AI feature inside a product you already own on Wednesday and did not tell you.Nothing in the audit was wrong. An audit is an instrument for describing a state, and what a security leader is accountable for is a rate of change.An audit produces a photograph. What you are accountable for is a moving object.This is the book about what happens next.The AI IT Security Implementation & Strategy is Volume VI of The Operating Discipline for AI Library and the second volume of its security pillar. It converts the audit's evidence base into a running program across the four domains a security leader owns: security governance and risk management, security operations, third-party and supply chain risk, and data protection and privacy.Each domain receives three chapters that ask the same three questions in the same order. What has AI broken here, working from the artifacts the audit produced rather than from theory. How does the same technology rebuild the capability, because nothing operating at human speed can supervise decisions made at machine speed. And what does the endgame look like, closing with a one-year target operating model that names an owner, a cadence, a quarterly milestone, and a budget line.Inside, you will build: The Human Supervision Matrix, which defines machine autonomy per action class rather than per tool, so the policy survives the next product release instead of being reopened by it.The Red-Button Protocol, which converts suspension from a paragraph in a policy document into a rehearsed capability with a named executor, a dependency map, and a measured time to confirmed stop.The Agent Authority Chain, tracing delegation through six elements from human principal to agent to sub-agent to tool call, so that "who authorized this" has an answer that survives an inquiry.Adaptive AI Trust Scores, replacing the annual vendor questionnaire with a continuously maintained measure that carries a direction, so a degrading vendor is visible before the renewal conversation rather than during it.The AI Decision Provenance Chain, reconstructing what data went in, what processed it, what the decision was, and what could have altered it.AI Security Debt, a countable register of accepted risk that carries an age dimension, because a three-year-old exception and a three-month-old exception are not the same governance failure.The AI Security Scorecard, the quarterly board instrument, with the metric definitions and trend fields already structured.A closing chapter sequences the first ninety days, because four domains launched at once with one team and one budget become zero programs by month four. The appendix supplies twenty-two working instruments as populatable worksheets, and a framework alignment section maps the guidance to the NIST AI Risk Management Framework, ISO/IEC 42001, the EU AI Act, the OWASP top ten lists, MITRE ATLAS, and Google SAIF.Written for chief information security officers, security directors, and security managers. Every page assumes the reader already knows what a control is, what an identity is, and what happens when neither one is inventoried.Somewhere ahead of you, on a date you do not control, someone will ask you to prove your AI exposure is governed. A regulator. An enterprise customer's security team. An insurance carrier at renewal. Or your own audit committee.The audit Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Hardcover. Zustand: new. Hardcover. You ran the AI security audit. You have the model inventory, the non-human identity count, the vendor map, and the data flows. The audit committee accepted it. Then you walked back to your desk and realized the environment it describes changes every week. Someone deployed an agent on Tuesday. A vendor activated an AI feature inside a product you already own on Wednesday and did not tell you.Nothing in the audit was wrong. An audit is an instrument for describing a state, and what a security leader is accountable for is a rate of change.An audit produces a photograph. What you are accountable for is a moving object.This is the book about what happens next.The AI IT Security Implementation & Strategy is Volume VI of The Operating Discipline for AI Library and the second volume of its security pillar. It converts the audit's evidence base into a running program across the four domains a security leader owns: security governance and risk management, security operations, third-party and supply chain risk, and data protection and privacy.Each domain receives three chapters that ask the same three questions in the same order. What has AI broken here, working from the artifacts the audit produced rather than from theory. How does the same technology rebuild the capability, because nothing operating at human speed can supervise decisions made at machine speed. And what does the endgame look like, closing with a one-year target operating model that names an owner, a cadence, a quarterly milestone, and a budget line.Inside, you will build: The Human Supervision Matrix, which defines machine autonomy per action class rather than per tool, so the policy survives the next product release instead of being reopened by it.The Red-Button Protocol, which converts suspension from a paragraph in a policy document into a rehearsed capability with a named executor, a dependency map, and a measured time to confirmed stop.The Agent Authority Chain, tracing delegation through six elements from human principal to agent to sub-agent to tool call, so that "who authorized this" has an answer that survives an inquiry.Adaptive AI Trust Scores, replacing the annual vendor questionnaire with a continuously maintained measure that carries a direction, so a degrading vendor is visible before the renewal conversation rather than during it.The AI Decision Provenance Chain, reconstructing what data went in, what processed it, what the decision was, and what could have altered it.AI Security Debt, a countable register of accepted risk that carries an age dimension, because a three-year-old exception and a three-month-old exception are not the same governance failure.The AI Security Scorecard, the quarterly board instrument, with the metric definitions and trend fields already structured.A closing chapter sequences the first ninety days, because four domains launched at once with one team and one budget become zero programs by month four. The appendix supplies twenty-two working instruments as populatable worksheets, and a framework alignment section maps the guidance to the NIST AI Risk Management Framework, ISO/IEC 42001, the EU AI Act, the OWASP top ten lists, MITRE ATLAS, and Google SAIF.Written for chief information security officers, security directors, and security managers. Every page assumes the reader already knows what a control is, what an identity is, and what happens when neither one is inventoried.Somewhere ahead of you, on a date you do not control, someone will ask you to prove your AI exposure is governed. A regulator. An enterprise customer's security team. An insurance carrier at renewal. Or your own audit committee.The audit Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Zustand: new. Hardcover. You ran the AI security audit. You have the model inventory, the non-human identity count, the vendor map, and the data flows. The audit committee accepted it. Then you walked back to your desk and realized the environment it describes changes every week. Someone deployed an agent on Tuesday. A vendor activated an AI feature inside a product you already own on Wednesday and did not tell you.Nothing in the audit was wrong. An audit is an instrument for describing a state, and what a security leader is accountable for is a rate of change.An audit produces a photograph. What you are accountable for is a moving object.This is the book about what happens next.The AI IT Security Implementation & Strategy is Volume VI of The Operating Discipline for AI Library and the second volume of its security pillar. It converts the audit's evidence base into a running program across the four domains a security leader owns: security governance and risk management, security operations, third-party and supply chain risk, and data protection and privacy.Each domain receives three chapters that ask the same three questions in the same order. What has AI broken here, working from the artifacts the audit produced rather than from theory. How does the same technology rebuild the capability, because nothing operating at human speed can supervise decisions made at machine speed. And what does the endgame look like, closing with a one-year target operating model that names an owner, a cadence, a quarterly milestone, and a budget line.Inside, you will build: The Human Supervision Matrix, which defines machine autonomy per action class rather than per tool, so the policy survives the next product release instead of being reopened by it.The Red-Button Protocol, which converts suspension from a paragraph in a policy document into a rehearsed capability with a named executor, a dependency map, and a measured time to confirmed stop.The Agent Authority Chain, tracing delegation through six elements from human principal to agent to sub-agent to tool call, so that "who authorized this" has an answer that survives an inquiry.Adaptive AI Trust Scores, replacing the annual vendor questionnaire with a continuously maintained measure that carries a direction, so a degrading vendor is visible before the renewal conversation rather than during it.The AI Decision Provenance Chain, reconstructing what data went in, what processed it, what the decision was, and what could have altered it.AI Security Debt, a countable register of accepted risk that carries an age dimension, because a three-year-old exception and a three-month-old exception are not the same governance failure.The AI Security Scorecard, the quarterly board instrument, with the metric definitions and trend fields already structured.A closing chapter sequences the first ninety days, because four domains launched at once with one team and one budget become zero programs by month four. The appendix supplies twenty-two working instruments as populatable worksheets, and a framework alignment section maps the guidance to the NIST AI Risk Management Framework, ISO/IEC 42001, the EU AI Act, the OWASP top ten lists, MITRE ATLAS, and Google SAIF.Written for chief information security officers, security directors, and security managers. Every page assumes the reader already knows what a control is, what an identity is, and what happens when neither one is inventoried.Somewhere ahead of you, on a date you do not control, someone will ask you to prove your AI exposure is governed. A regulator. An enterprise customer's security team. An insurance carrier at renewal. Or your own audit committee.The audit Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Zustand: new. Paperback. Nobody knows if the AI in your business is working. Including you.It's drafting your emails, supporting your decisions, and shaping the work that goes to your clients. It's running inside your tools, your vendors' platforms, and your employees' personal accounts. None of it was governed the way you govern hiring, finance, or compliance. You've been signing off on AI for months without a defensible way to know whether it's helping the business or quietly costing you money, accuracy, and trust.That gap is what The AI Readiness & Performance Assessment(TM) closes.This is not a primer on AI tools. It is not a list of use cases to try. It is the structured, evidence-based discipline that tells you whether AI can actually perform inside your business, before you spend another dollar widening a problem you haven't measured.Inside, Stephen R. Jordan walks executives through: Why this book exists.Executives are being asked to defend AI decisions they were never given a framework to make. When the board asks what AI is producing, when the regulator asks how it is governed, when the acquirer asks for evidence of operating discipline, when the lawyer asks who approved what, the answers cannot be improvised. They have to be documented.This book gives you the documentation method.Who it is for.Owners, presidents, CFOs, and COOs running organizations between 20 and 1,000 employees. Executives who would rather measure first, then scale. Leaders who want operating discipline applied to AI the same way it is applied to every other material function in the business.About the author.Stephen R. Jordan is the founder of SRJ Consulting & Services LLC, an AI governance and operational discipline advisory firm serving mid-market to multinational organizations. His career spans senior roles at Citi, Intel, McAfee, and Optiv. Dallas-Fort Worth-based, works nationwide.The AI Readiness & Performance Assessment(TM) is Volume II of The Operating Discipline for AI Library(TM), the operator-led nine-volume series organized into two pillars: AI Business Services(TM) (Volumes I through IV) and AI Risk Governance & Security(TM) (Volumes V through IX).If you would rather know before you scale, start here. Volume II of The Operating Discipline for AI Library. A structured method for executives to test whether AI is producing measurable value before scaling, with a 90-day roadmap. For owners, presidents, CFOs, and COOs. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Zustand: new. Paperback. Governance identifies what must change. The optimization work changes it.Most AI governance work ends at the risk register. The findings get documented, the target state gets approved, and then nothing moves. Projected labor savings stay projected. Usage climbs, dashboards glow, and the CFO still cannot defend the ROI number on the slide.The AI Efficiency and Process Optimization closes that gap. Volume IV takes the outputs governance produces, including risk registers, approved target states, and corrective action items, and converts them into work the business can actually run: implementation projects, workflow redesigns, technical safeguards, contract remediation, and measurable business improvements. A verification step confirms that projected labor savings were genuinely captured, not estimated on a slide.The book installs a complete measurement discipline. The Workflow Reality Map documents how workflows actually run, including the manual patches and shadow steps nobody wrote down. The AI Process Fit Test gives every AI use case one of four answers: expand, refine, pause, or remove. Four performance indicators, an efficiency scorecard, and an ROI formula produce the number that still holds up after every assumption is challenged, because it counts the hidden costs of review burden and rework alongside the benefits. A ninety-day installation plan runs on named owners and existing meetings, not on consultants living in the conference room. A closing chapter connects documented AI discipline to business valuation, for owners preparing the business for a lender, investor, or acquirer conversation.Written in plain English for owners, presidents, CFOs, and COOs at organizations between 20 and 1,000 employees. No technical background is required. No machine-learning vocabulary is assumed. AI performance is treated the way every other operating function gets treated: baselined, measured, scored, and accountable to the profit and loss statement.Volume IV is the closing volume of Pillar I of The Operating Discipline for AI Library, a nine-volume canon on running AI as a permanent business function, following The AI Business Enablement Audit, The AI Readiness and Performance Assessment, and The AI Risk and Governance Review. Most AI governance work ends at the risk register. Volume IV converts governance findings into implementation projects, workflow redesigns, and captured labor savings. For owners, presidents, CFOs, and COOs proving the AI investment paid off. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Paperback. Zustand: new. Paperback. Somewhere ahead of you, someone with authority will ask you to prove your AI is governed.It could be an acquirer in diligence, a regulator opening an inquiry, an auditor in fieldwork, or your own board in a moment that does not feel quiet. The question is the same in every version. The companies that can answer it produce a signed document. The companies that cannot improvise a narrative.The AI Risk & Governance Review(TM) closes that gap. It is the structured, evidence-based operating discipline that produces governance you can hand across the table, sized for the business you actually run, before someone asks for what you don't have.Volume III of The Operating Discipline for AI Library(TM) is written for owners, presidents, CFOs, COOs, general counsels, and boards running organizations between 20 and 1,000 employees. It is written for executives who would rather defend their AI before someone challenges it.Inside, Stephen R. Jordan walks leaders through: The four AI risk categories every framework maps to: data, decisions, vendors, and complianceA five-level AI Data Exposure Model(TM) and a four-tier Decision Influence Matrix(TM) sized to act in weeks, not quartersThe 6-Step Review Process(TM) that produces a signed governance dossier for every material AI use caseThe AI Governance Framework Crosswalk(TM) the line-by-line mapping between the operating artifacts and ISO/IEC 42001, the NIST AI Risk Management Framework, the EU AI Act, SR 11-7, NYC Local Law 144, the newer EU and international instruments (DORA, NIS2, the Revised Product Liability Directive, the Council of Europe Framework Convention on AI), and the sector rules in healthcare, financial services, insurance, and educationThree new operating logs that satisfy ISO 42001 Clauses 9 and 10: the AI Internal Audit Workpaper Log(TM), the AI Management Review Minutes Log(TM), and the AI Corrective Action Register(TM)The AI Board Reporting Package(TM) that turns quarterly minutes into defensible director oversightThe AI Incident Response Framework(TM) for when an AI use case produces an outcome that is wrong, harmful, or under inquiryA 90-Day Governance Launch Plan(TM) that runs on named owners and existing meetingsVolume III sits inside Pillar I: AI Business Services(TM), alongside Volume I (The AI Business Enablement Audit(TM)) and Volume II (The AI Readiness & Performance Assessment(TM)). Each volume stands on its own. Read together, they form an installed operating discipline for AI as a permanent business function. Somewhere ahead of you, someone with authority will ask you to prove your AI is governed. This is the operating discipline that produces the signed document you hand across the table before they ask. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Zustand: new. Paperback. Your security lifecycle is running. The queue keeps growing anyway.That is the paradox behind Secure by Design in the Age of AI: The Product Security Operating Model for the AI Era, Volume VII in The Operating Discipline for AI Library. Product security teams that added an AI addendum to a lifecycle they trusted are watching pull requests pile up while nobody skips a single gate. Stephen R. Jordan names the cause the Dual-Impedance Problem: assisted and agentic development roughly doubles the rate of change while shipping a new kind of risk, models, prompts, retrieval, tools, agents, memory, that a reviewer trained on code was never built to catch. Adding reviewers does not fix kind. Adding AI-specific steps does not fix rate. And AI-assisted code review, the fix most teams are buying right now, catches a minority of what a trained human flags and slows the queue further when it is used as a gate.This book installs the five-part operating model that fixes both mismatches at once. An attack surface ledger gives every model, tool, retrieval source, and agent one owned row with a validation date. A feature tier map sorts every AI feature by data reach, authority, autonomy, exposure, and consequence, so scarce human judgment goes where the blast radius is. A two-lane assurance model runs a deterministic machine lane that blocks every change, a human lane reserved for what a machine cannot judge, and an advisory band that never gates. A ship decision record names the executive who accepted the risk, with evidence current at the release timestamp. And a post-release response ladder runs the product from its first outside vulnerability report through certification, acquisition, and retirement.Ten supporting instruments round it out: a maturity scorecard, a capacity metric that tells you whether your review team can hold, a component admission standard for models and datasets, a protocol for the vulnerability that has no code fix, a one-page agreement that fits security inside a sprint, and an evidence pack that answers your enterprise customer, your auditor, your regulator, and your acquirer from one source.Written for the CISO and the director or vice president of product security at any organization that ships AI-enabled products. Every chapter installs one instrument, states its owner and cadence, and closes with the board question you must be able to answer, the evidence that answers it, the failure pattern to watch for, and a thirty-day move you can start this week. No vendor is named anywhere. Every claim carries a citation. The closing chapters sequence your first ninety days so your first fully documented release ships in week twelve.Your customers have already read Volume VI and learned what evidence to demand. This is how you have the answer ready before they ask.11 / 4000 bytes A five-part operating model for shipping AI-enabled products securely: an attack surface ledger, a feature tier map, two-lane assurance, a named ship decision, and a post-release response ladder. Written for CISOs and product security leaders. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…