Governing Intelligence: Technology

By Artem Kartusov and Rick Hayes

AI governance does not become real when a board approves a policy. It becomes real when the codebase enforces it.
Most organisations now have AI principles, risk frameworks, steering committees, and approval processes. Yet the systems that matter most are still built one pull request at a time - by engineers deciding how data is retrieved, how agents call tools, how permissions are scoped, how logs are written, and what happens when something goes wrong.
Governing Intelligence: Technology is a practical guide for engineering, platform, security, and technology leadership teams responsible for turning AI governance from policy into architecture.
This book is not about writing better prompts. It is not a theoretical ethics text. It is an implementation-level playbook for building AI systems that are secure, observable, auditable, and governable in production.
Across thirteen focused chapters, the book shows how to translate governance commitments into technical controls: policy-enforcement gateways, scoped identities, access-controlled retrieval, vendor due diligence, prompt-injection defences, agent permissioning, evaluation pipelines, audit logs, incident runbooks, CI/CD gates, and internal AI platforms that make the governed path the default path.
The central argument is simple: a guardrail that lives only in a policy document is not a guardrail - it is a hope. A real guardrail lives in the platform, the gateway, the schema, the permission broker, the deployment pipeline, or the audit log.
Written for CIOs, CTOs, platform leaders, security engineers, ML engineers, SREs, architects, and technical governance teams, this volume assumes technical fluency and focuses on the practical decisions organisations face as AI systems move from pilots to production.
Readers will learn how to: Map AI governance obligations to engineering ownershipDesign a reference architecture for governed AI systemsVet AI vendors and models before production useProtect sensitive data across prompts, retrieval, outputs, and logsScope identities and tool permissions for models, agents, and workflowsDefend against direct and indirect prompt injectionClassify agent actions by reversibility, impact, and approval requirementsBuild golden test sets, red-team routines, and AI-specific evaluation gatesMonitor drift, cost, usage, anomalous behaviour, and audit evidencePrepare incident-response runbooks for AI-specific failuresTreat prompts, model versions, and tool manifests as deployable artefactsBuild internal AI platforms where guardrails are inherited by defaultThis is the third volume in the Governing Intelligence series. Nations focuses on public policy and regulation. Boards focuses on directors and enterprise accountability. Technology translates those expectations into the systems engineering practices that make them enforceable. Finance completes the series by examining AI risk, value, reporting, audit, and capital allocation.
If your organisation is moving AI from experimentation into real workflows, this book is for the people who must make sure the system does not merely work - but works safely, accountably, and with evidence.
Because engineers do not govern AI through policy. Engineers govern AI through architecture.

• Format: Paperback
• Language: English
• ISBN: B0HHSKTRW5
• ISBN13: 9798171362256
• Release Date: September 2026
• Publisher: Independently Published
• Length: 76 Pages
• Weight: 0.25 lbs.
• Dimensions: 0.2" x 6.0" x 9.0"