Operating knowledge · Source-aware publishing
Evidence for making AI operational.
Evidence-led guides to enterprise AI adoption, AI automation, security, privacy, governance and production operation. Every article states its boundary, preserves its approved wording and separates operating guidance from unsupported customer claims.
Publish the architecture, not a pile of keywords.
These clusters connect business automation with enterprise adoption, trust and production operation. Each article answers one real decision and links to the surrounding system.
AI automation
Definitions, architecture choices, examples, cost and ROI frameworks for leaders who need a controlled production result rather than a tool demonstration.
↗02 · Trust through specificityEnterprise AI
System-level guidance for security, privacy, infrastructure, GDPR and governance—bounded to exact data and action paths instead of blanket claims.
↗AI Production Security Checklist: Two Release Gates
Use two practical security gates to decide when an AI system may process real data and when it may run recurring, action-capable production work.
↗AI Agent Write Access: A Safe Permission Model
Learn how to separate AI agent read, draft, and write authority with scoped tools, deterministic policy, authenticated approval, and replay-safe execution.
↗RAG Access Control: Enforce Permissions at Retrieval
Design permission-aware RAG with caller identity, source authorization, filtered retrieval, evidence checks, safe caching, and production access tests.
↗Private AI Security: Why Hosting Is Only One Control
Assess private AI security across identity, retrieval, applications, tools, releases, and operation before treating hosting ownership as protection.
↗AI Environment Isolation: Lessons From a Production Incident
Learn how separate data, runtimes, credentials, releases, side effects, target assertions, backups, and tripwires protect AI production environments.
↗Secure Enterprise AI Solutions: What Buyers Should Compare
Compare secure enterprise AI solutions by business result, data, integrations, permissions, testing, evidence, operation, and total responsibility.
↗25 AI Vendor Security Questions for Enterprise Buyers
Use 25 AI vendor security questions to assess data paths, providers, access, retrieval, model controls, actions, evidence, and production operation.
↗EU AI Act 2026: What Businesses Must Do Now
Understand the EU AI Act's current deadlines, classify one AI system, assign provider and deployer roles, and build an evidence-based action plan.
↗EU AI Act Compliance Checklist for Enterprise Systems
Use a seven-stage EU AI Act compliance checklist covering scope, roles, classification, immediate duties, suppliers, release evidence, and monitoring.
↗EU AI Act High-Risk Systems: A Classification Guide
Learn how intended purpose, Article 6, Annex I, and Annex III determine high-risk AI classification, and why human approval is a control, not an exemption.
↗EU AI Act Provider vs Deployer: Assign the Right Role
Compare EU AI Act provider and deployer roles using intended purpose, branding, development, release control, operation, and Article 25 changes.
↗EU AI Act AI Literacy: A Practical Article 4 Plan
Build role-based EU AI Act literacy measures for users, approvers, system owners, and technical administrators using practical failure cases.
↗AI System Inventory for EU AI Act Readiness
Build an AI system inventory covering purpose, roles, data, models, retrieval, actions, classification, evidence, versions, and review triggers.
↗EU AI Act Risk Classification: A Decision Tree
Classify an AI system through scope, prohibited practices, high-risk uses, Article 50 transparency, GPAI duties, roles, and review triggers.
↗EU AI Act vs GDPR: Two Reviews, One System Map
Compare the EU AI Act and GDPR across scope, roles, purpose, data, risk assessments, providers, logs, rights, and shared system evidence.
↗EU AI Act Prohibited Practices: An Article 5 Screen
Screen proposed AI uses against Article 5 using the practice, affected people, data, objective, effect, exceptions, and enforceable system limits.
↗EU AI Act Article 50: Transparency Rules Explained
Apply Article 50 across direct AI interactions, machine-readable marking, deepfakes, public-interest text, human review, and accessible disclosure.
↗EU AI Act Human Oversight: Design a Real Control
Design EU AI Act human oversight around identity, competence, context, authority, timing, evidence, automation bias, intervention, and safe stopping.
↗EU AI Act Logging Requirements for Production Systems
Design AI Act logs across requests, sources, models, policy, approvals, execution, outcomes, retention, privacy, investigation, and recovery.
↗EU AI Act Technical Documentation: Build the System File
Build EU AI Act technical documentation across purpose, design, data, performance, risk, controls, release evidence, operation, and change history.
↗RAG and the EU AI Act: Data, Access and Evidence
Assess RAG under the EU AI Act across classification, data roles, source scope, authorization, indexing, ranking, model context, provenance, and change.
↗EU AI Act and AI Agents: Classify Purpose, Control Action
Assess AI agents under the EU AI Act by intended purpose, role, tool authority, deterministic policy, human oversight, execution evidence, and change.
↗25 EU AI Act Questions to Ask an AI Vendor
Ask AI vendors 25 questions about roles, classification, models, data, documentation, transparency, oversight, logs, incidents, changes, and exit.
↗GPAI Code of Practice: What Enterprise Buyers Need
Understand the GPAI Code's scope, chapters, signatory evidence, provider questions, downstream limits, model-route records, and change triggers.
↗EU AI Act Substantial Modification: A Change Review
Review AI system changes across purpose, people, models, data, authority, performance, branding, evidence, contracts, and provider responsibility.
↗What Is AI Automation? A Production Definition
AI automation explained for business leaders: what it is, how it differs from ordinary automation, when to use it and what production readiness requires.
↗AI Agents vs Workflows: Choose by Decision Freedom
Compare AI agents and workflows by autonomy, variability, testability, authority and production risk—and choose the smallest architecture that fits the result.
↗AI Automation Examples That Belong in Production
Five realistic AI automation examples with sources, controls, approval boundaries, fallback and measures—from document intake to account operations.
↗AI Automation ROI: A Baseline-to-Operation Framework
Calculate AI automation ROI without invented productivity claims: establish a baseline, count full operating cost, measure accepted outcomes and account for risk.
↗AI Automation Costs: What a Production Budget Must Include
Understand AI automation costs across design, integration, data, acceptance, adoption, model usage, monitoring, support and controlled change.
↗AI and GDPR Compliance: A System-by-System Framework
A system-level GDPR framework for AI: purpose, role, lawful basis, minimisation, transparency, rights, providers, security and accountability.
↗AI Data Privacy: Map the Full Context Path
A production guide to AI data privacy across prompts, retrieval, model providers, tools, logs, outputs, human review, retention and deletion.
↗AI Governance: From Policy to Production Decisions
An operational AI governance model for portfolio selection, system ownership, risk classification, acceptance, release evidence, monitoring and retirement.
↗Secure Enterprise AI: A Production System Framework
Learn how to assess secure enterprise AI across data, identities, retrieval, models, tools, actions, testing, releases, and ongoing operation.
↗Private AI Infrastructure: Define the Right Boundary
Compare private AI infrastructure patterns across data, network, compute, models, tenancy, and geography, with a practical requirement framework.
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