Worked Case Studies & Practical Benchmarks

Real-world illustrative examples demonstrating how small and medium businesses apply our risk matrix to evaluate operational AI deployments.

Case Study 1: E-Commerce Customer Support Chatbot

Moderate Risk (61 / 100)

Scenario: A 45-person online retailer integrates a GPT-4 powered conversational bot into their public website to answer tracking questions and assist with product returns.

  • Privacy Risk (65%): Bot accesses customer names and order history. Requires Zero-Retention API.
  • Hallucination Impact (70%): Incorrect return policy statements create customer disputes.
  • Governance Authority (60%): Automated refund issuance up to $50.
  • Copyright Exposure (20%): Standard product description inputs.
  • HITL Necessity (80%): Escalation path required for complex disputes.

Outcome & Safeguards: Implemented grounding RAG on verified return policies, capped autonomous refunds at $25, and added human agent handoff triggers.

Case Study 2: Legal Firm Contract Clause Extraction

Critical Risk (84 / 100)

Scenario: A boutique legal consultancy uses Claude 3.5 Sonnet to summarize non-disclosure agreements (NDAs) and flag liability caps for enterprise clients.

  • Privacy Risk (95%): Highly confidential client NDAs and financial indemnities.
  • Hallucination Impact (90%): Missed liability clause leads to unhedged client risk.
  • Governance Authority (90%): Legal advice output requires partner sign-off.
  • Copyright Exposure (35%): Proprietary contract template inputs.
  • HITL Necessity (95%): Mandatory attorney verification before client delivery.

Outcome & Safeguards: AI prohibited from autonomous delivery. Formatted as draft highlights for mandatory human lawyer review with local zero-data storage API.

Case Study 3: Software Agency Code Assistant

High Risk (66 / 100)

Scenario: A SaaS development team enables GitHub Copilot for engineers writing client Web APIs and database queries.

  • Privacy Risk (75%): Ingesting proprietary client source code repositories.
  • Hallucination Impact (60%): AI-generated SQL injection or security flaw insertion.
  • Governance Authority (50%): Standard pull request review process.
  • Copyright Exposure (80%): Code model suggestion of copyleft GPL-licensed snippets.
  • HITL Necessity (50%): Senior developer code review mandatory.

Outcome & Safeguards: Enabled commercial license filtering for public code matches and added automated SAST security scanning in CI/CD pipeline.