AI Failure Path Mapping: From Component Monitoring to Systemic Resilience

Your IT team didn’t raise any warnings so why have you ended up with a regulatory investigation into you’re new AI-based system?

Introducing AI Failure Path Mapping (AI-FPM)

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AI-FPM Methodology

The AI Dashboard is Green - yet AI Outcomes Fail.

At the beginning of 2026 the PRA issued a unified warning across International Banks, UK Deposit Takers, and Insurance supervision: AI amplifies risk.

Their message was consistent across all three letters:

1. AI is Critical for Growth (The Opportunity).

2. AI Amplifies "Boring" Risks (The Threat).

3. Data Governance is a Pre-requisite (The Constraint).

But look at the image. This is the "Risk Blind Spot" that threatens that growth.

The dashboard stays green. The outcome fails.

You can have a model that is mathematically sound but operationally dangerous. MLOps tools monitor the model; they don't see the silent failure path - stale data leading to bad credit decisions, bias violations, regulatory breaches.

Here's the problem: Traditional monitoring misses this entirely.

We have introduced AI Failure Path Mapping (AI-FPM) to tackle this scenario.

An AI Failure Path

The regulators defined "WHAT" is needed: Robust capabilities to detect, respond, and recover from AI failures.

AI-FPM delivers "HOW": A 7-step process from Mapping to Recovery Playbooks - aligned directly with PRA SS1/23, Consumer Duty, EU AI Act, and ISO 42001.

Don't let invisible failures become visible breaches.

Actionable Remediation Plans

Operational Playbooks