Privacy / ZDR
PII never leaves the perimeter: masking and Zero Data Retention
Read the chapter →Grigoriy Dobryakov
Course · Enterprise AI Governance Architecture
AI governance is the set of rules and mechanisms that decide what an AI system in a company may and may not do, and how to make sure it actually holds to that. This course is about the engineering side: not a forty-page policy but a control plane — the layer between the business and the models that all traffic passes through and that governance cannot be bypassed technically.
Each chapter takes a business or regulatory goal and drives it down to an architectural pattern and an engineering implementation: what to deploy, where it breaks, and how it maps to the EU AI Act, ISO/IEC 42001 and OWASP. The running case is Kovcheg, a bank AI assistant (RAG plus an agent that takes actions), deliberately high-risk so it touches every plane at once.
The course runs down a stack of control planes: one request passes through the layers top to bottom — from data masking and access control through guardrails to audit and evaluation. Start with the manifesto.
PII never leaves the perimeter: masking and Zero Data Retention
Read the chapter →RAG returns only what the user is allowed to see
Read the chapter →Injections, jailbreaks and hallucinations don't get through
Read the chapter →Every AI decision is reproducible and provable
Read the chapter →Cost under control, the system holds its SLA
Read the chapter →EU AI Act, ISO 42001 and content marking
Read the chapter →The model neither degrades nor discriminates
Read the chapter →Vetted components and a kill-switch for agents
Read the chapter →Agent autonomy bounded by the action's risk class
Read the chapter →Controls assembled into a managed system (AIMS)
Read the chapter →Designing the control plane for your system: privacy, access, guardrails, audit, EU AI Act / ISO 42001 compliance — as working architecture, not a policy PDF.
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