Curriculum / Module 07
professional2 hours4 lessonsTimed assessment

GenAI Without Leaks

Data governance, personal information and POPIA

How to get the productivity of generative AI without leaking the personal information that regulation protects. The module shows how information actually escapes — through prompts, outputs, memorisation and training — then teaches the controls: the consumer-versus-enterprise distinction, safe prompting and redaction discipline, and the full POPIA mapping across security safeguards, the operator relationship, automated decisions and cross-border transfers. It is the module that lets an adviser safely use AI for a client report.

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By the end of this module, you can
  • Explain the mechanisms by which personal information leaks into and out of generative-AI systems.
  • Choose and configure tools, and write prompts, so that client and personal data is not exposed or transferred unlawfully.
  • Apply POPIA sections 19, 71 and 72 and the operator concept to a real generative-AI workflow, including cross-border processing.
Skills taught
  • Data-leakage threat modelling
  • Consumer-versus-enterprise tool evaluation
  • Safe prompting and redaction discipline
  • POPIA operator and transborder analysis

Lessons in this module

L1
How information actually leaks: four routes out
Explain the four leakage mechanisms — prompt disclosure, output disclosure, memorisation, and training reuse — and cite the research that established each as real.
30 min
L2
Consumer versus enterprise: the tool decision that decides everything
Evaluate a generative-AI tool for institutional use across the questions that matter — retention, training, location, contract, controls — and state why the consumer/enterprise distinction is a legal boundary, not a pricing tier.
30 min
L3
Safe prompting and redaction: the adviser's client-report workflow
Apply minimisation, redaction and structure disciplines to prompts so that generative tools deliver full productivity on client work without exposing personal information — and execute the module's flagship workflow: the AI-assisted client report.
30 min
L4
The POPIA map: sections 19, 20–22, 71 and 72 over one workflow
Apply POPIA's security-safeguard, operator, breach-notification, automated-decision and transborder provisions to a generative-AI workflow, and produce the compliance record that evidences it.
30 min
Case 07.1
Case file: The report that wrote itself
An applied fact pattern worked against a model resolution, followed by the timed assessment (30 minutes, pass mark 70 percent).
20 min

How it lands across the four desks

Wealth & Advisory

Highest stakes for you: client financial data is exactly what a careless prompt exposes. You leave with a workflow that produces the client report with AI speed and zero unlawful disclosure — the module's title promise, kept.

Compliance & Risk

You set the policy: tool approval criteria, the operator-contract checklist, the transborder analysis and the incident posture when a leak happens anyway. This module hands you each artefact.

Fraud & AML

Case data is among the most sensitive information the institution holds — alert narratives, suspicion assessments, subject identities. You learn what may never leave the approved environment and why.

Credit & Underwriting

Application files are dense personal information. You learn safe-use patterns for AI-assisted underwriting work and the leakage routes that turn a drafting shortcut into a notifiable breach.

Key literature · 7 sources

Every module rests on a verified scholarly and institutional evidence base. The full core and further reading lists open with the module.

  • Carlini, N. et al. (2021) 'Extracting Training Data from Large Language Models.' USENIX Security 2021 — verbatim training-data extraction demonstrated.
  • Carlini, N. et al. (2019) 'The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks.' USENIX Security 2019 — memorisation of rare secrets established.
  • Shokri, R. et al. (2017) 'Membership Inference Attacks Against Machine Learning Models.' IEEE S&P 2017 — presence-in-training-data as leakable information.
  • OWASP GenAI Security Project (2025) 'OWASP Top 10 for LLM Applications' — the sensitive-information-disclosure and related risk catalogue.
  • NIST (2024) 'AI RMF: Generative AI Profile' (AI 600-1) — data privacy as a cross-cutting generative-AI risk, with control framing.
  • Protection of Personal Information Act 4 of 2013, ss 19, 20–22, 71, 72 — read in full against this module's workflow mapping.
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