AI Production Readiness :

What this framework is — and is not

This is LYFYE's operational readiness framework for evaluating a deployed or deployable AI/software system. It is informed by production engineering practice and external risk guidance, but it is not a NIST assessment, an OWASP certification, a penetration test, a compliance audit, or proof that a system is secure. It identifies the evidence a team should obtain before widening exposure.

Group 1: Identity & access

Authentication, authorization, and tenant isolation decide who gets into the system and what they can do after they arrive. A polished interface is not evidence of authorization. Production evidence comes from exercising the deployed boundaries directly, including lower-privileged and cross-tenant attempts where applicable.

• Authentication & sessions: test sign-in, refresh/expiry, long-lived sessions, and sign-out against the deployed environment.
• Authorization boundaries: call protected server routes directly as a user who should not be allowed to perform the action.
• Tenant & customer isolation: prove a foreign tenant or customer cannot cross the data boundary; mark this domain not applicable only when the architecture truly has no tenant boundary.

Group 2: Data & security

Production systems need explicit control over secrets, hostile/unexpected input, and recovery. Generative AI adds additional untrusted surfaces because prompt content, retrieved context, model output, tool arguments, and external files can all cross trust boundaries.

• Secrets & sensitive data: inspect source history and shipped client bundles, rotate exposed material, and establish deliberate secret storage.
• Input validation & attack surface: schema-bound public endpoints and handle model input/output as data that can be malformed or adversarial.
• Data & recovery: run an actual restore drill. A backup configuration that has never been restored is evidence of a plan, not evidence of recovery.

Group 3: AI reliability & economics

Model providers, network calls, and generated output introduce failure modes that conventional happy-path demos hide. Production readiness requires bounded failure behavior, spend controls, and observability that reaches a human operator.

• AI failure handling: simulate timeout, provider errors, and malformed responses; confirm each path reaches an honest, usable terminal state.
• AI cost & usage controls: test whether rate and spend ceilings stop usage rather than merely logging that it happened.
• Error visibility: inject a safe test error and confirm the monitoring/alert path works end to end.

Group 4: Operations & commercial controls

A system can be technically impressive and still be commercially or operationally fragile. Paid entitlements must survive direct API calls, environments must be separable and reversible, and ownership must outlive the original developer's memory.

• Billing & entitlements: where paid plans exist, deliberately exceed a plan boundary through the server/API path and confirm it is enforced.
• Deployment & environments: trace each environment's database and credentials, document deploy provenance, and rehearse rollback.
• Operational ownership: have someone other than the original author operate the system from the runbook and record where the documentation fails.

Group 5: Assurance & governance

Tests protect behavior as the system changes. Approval and audit records protect consequential decisions when AI can influence or initiate real-world action. The appropriate level depends on the consequence; the boundary should be deliberate rather than accidental.

• Tests & regression protection: cover the money and trust paths first — identity, writes, model pipeline, entitlements, and handoffs — and prove the suite fails when behavior is intentionally broken.
• Approvals & audit trail: choose one consequential action and reconstruct who or what triggered it, what was approved, and when from durable records alone.

Why unknown is a first-class result

Teams often answer production questions with 'I think so' because another person owns the database, identity provider, billing system, deployment pipeline, or model budget. Treating uncertainty as a pass produces false confidence; treating it automatically as a defect confuses missing evidence with a known failure. The correct result is verification required, with the shortest technical test that can settle it.

Relationship to NIST and OWASP guidance

NIST AI RMF 1.0 is a voluntary, cross-sector framework for managing AI risk and is currently being revised; NIST's Generative AI Profile is a companion resource for generative-AI risks. OWASP's GenAI Security Project publishes current application-security guidance, including the 2026 LLM Top 10. LYFYE's fourteen domains are not a substitute for either body of work. They are a compact operating check designed to surface evidence and hand the team concrete verification actions before broader production exposure.

How to use the result

Start with known blockers, then settle unknowns that could change the launch decision. Keep product opportunity separate from implementation readiness. A useful prototype may deserve more investment while still requiring identity, cost, recovery, observability, or operating controls before its user base widens.