Model Risk Management for Generative Systems

Finance & Banking • ~8 min read • Updated Jul 5, 2025

Context

Generative AI models such as LLMs introduce new categories of model risk that traditional Model Risk Management (MRM) frameworks weren’t designed to address. These systems can produce plausible but incorrect outputs, adapt unpredictably to new inputs, and change behavior with updates or retraining. Without adapted controls, institutions risk compliance breaches, reputational harm, and operational disruption.

Core Framework

Modernizing MRM for generative systems requires:

  1. Hazard Identification: Hallucinations, bias propagation, prompt injection vulnerabilities, and sensitive data leakage.
  2. Testing Protocols: Scenario-based stress tests, red-teaming for adversarial prompts, and quantitative quality benchmarks.
  3. Monitoring Regime: Drift detection, output sampling reviews, and near-real-time alerting for anomalies.
  4. Change Control: Formal approvals for fine-tuning, prompt library changes, or API version shifts.

Recommended Actions

  1. Map Model Inventory: Identify all generative models in use and their business-criticality.
  2. Define Risk Tiers: Segment models by potential impact to guide control intensity.
  3. Create Test Harnesses: Automate repeatable evaluation with representative prompts and datasets.
  4. Document Assumptions: Capture training data scope, known limitations, and intended uses.
  5. Establish Retraining Protocols: Require governance sign-off for model updates.
  6. Integrate with Incident Response: Link detection alerts to defined escalation playbooks.

Common Pitfalls

  • Overreliance on Vendor Assurances: Assuming third-party models meet your internal control standards.
  • No Ongoing Testing: Treating validation as a one-time exercise.
  • Weak Change Management: Allowing uncontrolled updates to production models.

Quick Win Checklist

  • Establish a high-risk model registry.
  • Run quarterly adversarial testing.
  • Automate drift detection on key outputs.

Closing

By adapting MRM to generative AI, organizations can harness the value of these models without exposing themselves to unmanaged risks. The key is to embed controls that are lightweight enough for adoption but rigorous enough for compliance and resilience.

Advisory by Straten AI Team

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