Srj consulting and services publishing (121 resultados)

Condición: New.

Condición: New.

Condición: New.

Condición: New.

Condición: New.

Condición: New.

Condición: New.

Paperback. Condición: 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. …

Condición: New.

Condición: New.

Condición: New.

Condición: New.

Condición: New.

Hardcover. Condición: 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. …

Condición: New.

Condición: New.

Condición: New.

Condición: New.

Paperback. Condición: 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. …

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

Paperback. Condición: 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.…

Hardcover. Condición: 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. Condición: 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. …

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

PAP. Condición: 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. Condición: 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. Condición: 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. Condición: 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. Condición: new. Paperback. You built a real security program. Patch cycles on schedule, zero trust implemented correctly, incident response tested under pressure. Then AI agents, MCP servers, embedded vendor features, and autonomous tools walked into your environment faster than your operating model could absorb them. That is not a failure of effort. It is a failure of speed. Now a question is forming, from a regulator, an audit committee, an insurer, or an enterprise customer expanding its security questionnaire: can you prove your AI exposure is known, controlled, and governed? Every dashboard shows green, and none of them are lying. Every control functions. But none of those tools were built to watch what AI agents do inside your environment, because they were designed before AI agents entered it at scale. This is the Green Dashboard Fallacy. It is not a detection failure, it is an assumption failure. The greatest AI security risk facing your organization is not that you are undefended. It is that leadership believes the existing program is already watching. The audit produces six artifacts built to survive outside scrutiny: an AI exposure map across the six CISO domains, a non-human identity inventory and agent boundary matrix, a tested AI Red Button procedure, an AI vendor tier map and data flow map, a regulatory crosswalk, and a four-page board pack. These are not concepts to understand. They are documents to hand over. Three instruments carry the method. The Visibility Triangle sorts every gap into visible, suspected, or undetectable. The Six-Domain Operating View structures the work across governance, security operations, architecture, application security, third-party risk, and data protection. The Defensible AI Security Baseline sets a dated, scored standard across seven areas with a named owner for each, which turns a one-time audit into a program. Seven binding frameworks run underneath: NIST AI RMF, ISO/IEC 42001, the OWASP LLM and Agentic Top 10, OWASP AIVSS, MITRE ATLAS, HITRUST AI, and Google SAIF. The thesis is stated plainly. AI agents must be audited as privileged, non-human actors inside your control environment. Once an agent can retrieve data, invoke tools, write records, or trigger workflows, it is an operational actor with an identity, a privilege scope, and a blast radius. No products are recommended and the book does not end in a pitch. Volume V of The Operating Discipline for AI Library, and the opening volume of Pillar II, AI Risk Governance and Security. The timeline belongs to whoever moves first. Every dashboard is green, and none of them were built to watch your AI. A CISO audit method that produces six artifacts, a scored baseline, and a four-page board pack: proof your AI exposure is known, controlled, and governed. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…