AI Risk Heatmap Framework

Visualize AI risk exposure with dynamic heatmaps that drive governance, prioritization, and remediation.

Operational Workflows

Define a comprehensive risk taxonomy for AI covering legal, ethical, o

Align the model with enterprise risk management frameworks and responsible AI policies.

Score risks using factors like likelihood, impact, control effectiveness, detection, and velocity.

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Operational Workflows

Integrate heatmaps into risk review meetings, remediation planning, and escalation processes.

Automate data ingestion from model inventories, incident logs, compliance systems, and control testing.

Frequently asked questions

How are link heatmaps updated with strategic decisions?

Link heatmap updates to strategic decisions such as funding, policy changes, or control investments.

What is Continuous Improvement referring to?

Continuous Improvement – This refers to a process of ongoing evaluation and refinement designed to optimize performance and address emerging challenges.

How should review scoring assumptions and data sources be managed?

To ensure the accuracy and relevance of your heatmap, it’s crucial to regularly review your scoring assumptions, the underlying data sources used for calculations, and the design of any visualizations – this allows you to adapt to evolving risks and maintain a robust analytical framework.

Where can I find information on capturing lessons learned from incidents?

Capturing lessons learned effectively involves gathering insights from incident reports, audits, and feedback from stakeholders. This information is then used to continuously improve the heatmap program’s design and effectiveness, ensuring that future analyses are more informed and accurate.

▶ Try it live

Everything above runs in your browser — open Heat Conduction and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Heat Conduction simulation