Ensuring quality and business value in AI
Generative AI Audit Framework
The 9senses GenAI Audit Framework is a structured methodology to assess how well a generative AI system performs against real business use cases - and how safely, responsibly, and reliably it behaves from a user, governance, and risk perspective. The framework combines use-case adherence, business outcomes, and standardized quality dimensions to produce an actionable improvement roadmap.
The framework supports two audit levels: Level 1 (behavioral / black-box evaluation based on observable behavior) and Level 2 (open-book diagnosis covering architecture, retrieval/RAG pipelines, agentic orchestration, governance, compliance, ethics, and value modeling). It applies across the full spectrum of GenAI deployments - customer-facing copilots, internal assistants, document processing systems, and multi-step autonomous agents.
Level 1 Audit
An independent, external review of your GenAI application's observable performance. No internal access to your technical environment required. Delivered within 5 business days.
Level 2 Audit
A tailored deep-dive analysis, for example of technical architecture, retrieval systems, governance, compliance, and business value – recommended when critical issues are identified in Level 1.
Why a GenAI Audit?
Generative AI applications are increasingly embedded in high-stakes workflows across customer service, sales, legal, HR, and operations. When they fail, the consequences can be reputational, regulatory, or financial. A structured GenAI Audit identifies weaknesses before they cause harm and validates that the application delivers its intended results and measurable business value.
The framework assesses this across five areas:
- Intended use: Does the application reliably do what it was designed to do?
- Business outcomes: Does it improve containment, conversion, resolution time, cost efficiency, or satisfaction?
- Risk exposure: Does it hallucinate, leak sensitive data, or fail under adversarial prompts?
- Governance readiness: Are transparency signals, human-in-the-loop controls, escalation paths, and monitoring adequate?
- Regulatory compliance: Does it meet applicable obligations under the EU AI Act, GDPR, and sector-specific rules?
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Level 1: Behavioral (black-box) Audit
Level 1 is a standardized external evaluation based on observable behavior at the time of testing. It benchmarks performance, identifies user-facing weaknesses, and delivers a prioritized set of recommendations – without requiring access to the underlying system.
Core Dimensions & Weights
| Dimension | What is evaluated | Weight |
|---|---|---|
| Response Quality | Relevance to the user's goal, factual correctness, completeness, formatting, hallucination robustness, citation behavior | 50% |
| Speed | Latency for simple and complex requests, streaming behavior, perceived speed under load | 20% |
| User Interface | Clarity, usability, layout, readability, affordance design, accessibility signals | 15% |
| Dialog Quality | Conversation flow, expectation management, tone calibration, multi-turn coherence, escalation behavior | 15% |
Additional dimensions are reviewed as outside-in indicators (not part of the Level 1 composite score): Business Value, Compliance, and Ethics. These inform the qualitative narrative and flag areas requiring deeper Level 2 investigation.
Scoring Methodology
Each dimension is scored on a standardized 1–5 scale and aggregated using the weights above into an overall performance score.
| Score | Meaning |
|---|---|
| 1 | Critical deficiency with high user impact or material risk |
| 2 | Significant weaknesses requiring remediation |
| 3 | Acceptable performance with identifiable limitations |
| 4 | Strong performance with minor gaps |
| 5 | Best-practice level: benchmark-worthy performance |
Level 2: Open-Book Audit (Root-Cause Diagnosis)
Level 2 is an open-book audit that explains why the GenAI system behaves as it does. It reviews the technical and organizational system behind the deployment and produces a targeted optimization plan grounded in root causes - not just symptoms.
Open-Book Scope & Outcomes
| Area | What we review (examples) | Typical outcomes |
|---|---|---|
| Architecture & Orchestration | System boundaries, tool/API usage, routing logic, fallback strategy, agentic loops, trust zones, dependency risks | Architecture risk map, refactoring recommendations, safer routing & fallback design |
| Retrieval / RAG Quality | Chunking strategy, retrieval relevance, grounding behavior, citation logic, context window management, stale content risk | Retrieval tuning plan, grounding improvements, measurable answer-quality lift |
| Prompting & Guardrails | System prompts, policy hierarchy, refusal strategy, tool-use permissions, instruction injection robustness, output filters | Hardened prompts, safer policies, reduced jailbreak & prompt injection risk |
| Security & Data Protection | Access controls, secrets handling, data minimization, leakage scenarios, PII exposure, logging sensitivity | Risk remediation plan, control improvements, safer data flows |
| Governance & Operations | Ownership model, human-in-the-loop design, escalation paths, monitoring, incident response, evaluation cadence, change management | Operating model, monitoring design, continuous improvement loop |
| Compliance & Ethics | Transparency & disclosure obligations, data processing mapping, fairness considerations, EU AI Act risk classification, regulated use cases | Compliance readiness checklist, documentation & policy improvements |
| Business Value Modeling | KPI definitions, baseline vs. target, measurement instrumentation, token/run-cost controls, ROI sensitivity analysis | Value case, KPI dashboard spec, cost controls, prioritized roadmap |
Framework Principles
- Anchor: Intended use and business outcomes serve as the primary anchor for defining what "good" means in each context.
- Scope: Applies across Generative AI applications, including customer-facing assistants, internal copilots, knowledge and retrieval systems, document intelligence systems, multi-agent pipelines, and hybrid human+AI workflows.