Model Origin Is Not Enough: Build a Provenance Record for Embedded AI
A practical model-provenance record for embedded AI: service path, data rules, permissions, change control, monitoring, evidence, and fallback.
A Cloud Marketplace Is a Buying Channel, Not Your Model-Control Strategy
Industrial AI Security Guidance Is Still Forming. Define the Operating Controls Now.
AI Lowered the Cost of Attack. Your Vulnerability Queue Needs a Business SLA
A practical framework for governing internet-facing vulnerabilities with asset ownership, risk-based remediation deadlines, containment, vendor escalation, monitoring, and recovery.
Your AI Incident Plan Is Not Real Until the Business Rehearses It
A practical AI incident-exercise framework for testing decision rights, containment, vendor escalation, recovery, rollback, communications, and manual fallback.
When an AI Agent Is Blocked, It Should Not Choose Another Route
A practical framework for governing AI agent web access with destination allowlists, stop rules, credential controls, monitoring, escalation, and manual fallback.
Human Oversight for AI Needs a Capacity Plan, Not an Approval Button
A practical framework for staffing AI review queues, setting risk-based approval triggers, managing reviewer load, escalating exceptions, and preserving manual fallback.
When Retail AI Takes Action, Close the Outcome Loop
A practical commerce framework for connecting AI recommendations and approvals to completed transactions, inventory truth, margin results, exceptions, and reversals.
Industrial AI Needs an Execution Boundary, Not Just a Policy Manual
A practical framework for placing permissions, deterministic validation, approval gates, audit logs, stop conditions, rollback, and manual fallback between industrial AI and live operations.
AI Spend Is an Operating Control, Not Just a Finance Report
A practical operating framework for attributing AI usage, controlling variable costs, detecting anomalies, and measuring cost per accepted business outcome.
Your AI Partnership Needs a Responsibility Map, Not Just a Statement of Work
A practical responsibility map for agentic AI partnerships—covering permissions, validation, monitoring, incidents, rollback, and manual fallback.
