A practical assurance layer for data, models, context, tools, evaluation, security, human control, and production operations—scaled to the risk and responsibility of the system.
DZignn AI Assurance is a delivery framework for identifying what an AI system can access, how it is evaluated, where it can fail, who can override it, and what evidence remains after deployment.
Map source ownership, sensitivity, purpose, access, retention, and the boundaries around client and user information.
Document providers, model versions, selection logic, fallback routes, material limitations, and change exposure.
Control retrieval scope, source quality, citation paths, freshness, and what the system should do when reliable context is missing.
Treat tool use and autonomous actions as permissions—not conveniences—with explicit scope, approvals, and irreversible-action controls.
Define quality targets, regression suites, adversarial cases, grounded-answer checks, failure thresholds, and release criteria.
Observe performance, exceptions, costs, model/provider changes, user feedback, and escalation paths as the system evolves.
The framework is applied proportionally. A low-risk internal assistant should not carry the same process as a system that can alter customer records, trigger transactions, or influence consequential decisions.
Use case, actors, data, decisions, vendors, failure impact, and control requirements.
Permissions, grounding, approvals, fallback, observability, and measurable acceptance criteria.
Representative tasks, adversarial cases, security tests, regression checks, and trace capture.
Known limitations, test outcomes, rollback path, operating ownership, and launch decision.
Quality, incidents, costs, drift indicators, provider changes, user feedback, and remediation.
Untrusted input attempts to change instructions, expose data, or misuse connected tools.
Models or retrieval layers reveal confidential, personal, or cross-tenant information.
An AI system receives more authority than the task requires or acts without suitable approval.
Confident-looking responses are wrong, unsupported, stale, or used outside the intended context.
Model, provider, API, prompt, dataset, or retrieval changes silently alter system behavior.
Loops, tool misuse, high token consumption, latency, or exception cascades create cost or service risk.
DZignn can use recognized guidance as inputs to an engagement-specific control plan. The framework is informed by NIST AI RMF and its Generative AI Profile, AI-management concepts in ISO/IEC 42001, OWASP GenAI security guidance, and applicable transparency obligations such as those in the EU AI Act.
No certification or legal compliance is implied. Applicability depends on the system, role, sector, geography, data, and contract. Formal legal, regulatory, or certification conclusions require the client’s qualified advisers and appropriate independent assessment.
The DZignn AI Agent Foundation includes hooks for retrieval controls, permissions, evaluation, traceability, and fallback so assurance does not have to be added after the system is already complex.