AI Applied Radar: AI Applied To Risk Management

Report
Corporate Risk Leaders
Mahum Khawar
Mahum Khawar
Katelyn Johnson
Katelyn Johnson
29 Sep, 2025

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Executive Summary

This report delivers a comprehensive assessment of AI-augmented use cases for risk management, enabling risk managers to assess each use case’s trustworthiness, business value and operational viability. The AI Applied Radar analysis evaluates a spectrum of AI-driven solutions, using a methodology grounded in expert interviews, global surveys and technical review. This report maps each use case across mainstream, pilot and emerging phases of market adoption. Mainstream deployments encompass compliance assistants, third-party risk scoring and triage, and automatic policy and controls mapping. Pilots target real-time alerts, novel threat analysis and process intelligent task completion. Emerging concepts, such as machine learning (ML) combined with physics-based modelling for faster climate risk assessments and supplier performance prediction models, remain developer-focused. The Radar provides actionable insights for risk managers and vendors seeking scalable, high-impact AI adoption.
Introducing the AI Applied Radar analysis
Key questions answered by the AI Applied Radar analysis
AI Applied Radar analysis aligns with risk management technology buyers’ demands for practical and scalable AI solutions
AI Applied Radar for risk management
Defining the market for emerging, pilot-phase and mainstream AI technologies for risk management
Methodology overview
Identifying the three critical pillars of compelling AI use cases
Assessing the market adoption phase of AI use cases
Determining the tech availability for AI use cases
AI Applied Radar: risk management
Figure 1. Summary of AI models, data sources and privacy, and quality control measures used within risk software 
Figure 2. The AI Applied Radar for risk management
Figure 3. AI Applied Radar use case groupings for risk management
Figure 4. Description of mainstream AI-augmented use cases
Figure 5. Trustworthiness at scale, operational viability and business impact for mainstream use cases
Figure 6. Description of pilot AI-augmented use cases
Figure 7. Trustworthiness at scale, operational viability and business impact for pilot use cases
Figure 8. Description of emerging AI-augmented use cases
Figure 9. Trustworthiness at scale, operational viability and business impact for emerging use cases

About the Authors

Mahum Khawar

Mahum Khawar

Analyst

Mahum is an Analyst at Verdantix, specializing in AI integrations within risk management software and operational resilience. She advises technology buyers and software vendor...

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Katelyn Johnson

Katelyn Johnson

Senior Manager

Katelyn is a Senior Manager at Verdantix, specializing in enterprise risk management and external risk and resilience. She helps executives navigate today’s evolving ris...

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