From 0 - 100 in a few minutes. How to leverage GenAI fo Risk Intelligence

The quarterly security analysis used to mean two weeks of painstaking research across industry reports, attack databases, and security advisories.

We'd compile data from diverse sources, manually categorize incidents, and gradually assemble a picture of the threat landscape—all while newer attacks occurred.

From Research Maze to Precision Template

Today, I begin with a structured prompt template for ChatGPT that transforms this process. The template directs the system to search security publications, CISA advisories, and vendor threat reports, extracting standardized data on attack vectors, industry impacts, and mitigation strategies. Each data point maintains source links for verification.

What happens next still feels like analytical alchemy:

In the next few minutes, the system delivers a comprehensive dataset of recent attacks categorized by method, industry, and impact. I spend 15 minutes validating key findings against primary sources, confirming accuracy.

A second prompt analyzes patterns: Which sectors face increasing threats?

How are attack vectors evolving? What correlations exist between technologies and vulnerabilities? Questions that previously required days of analysis now generate insights in minutes.

Visualizing the data

The structured data transfers to Claude with visualization prompts. Within minutes, interactive dashboards emerge showing attack distribution heat maps, vector evolution timelines, and risk matrices that translate technical findings into business implications.

Quantify the risks

For risk quantification, the final template applies analytical frameworks to generate evidence-based assessments. This replaces subjective "high/medium/low" ratings with data-driven scores derived from actual attack frequencies and documented impacts.

Technical Reality, Human Judgment

This methodology represents substantial advancement but requires critical application. The system aggregates publicly available information but cannot independently verify factual accuracy.

The most effective implementations combine AI-driven data processing with human analytical judgment—technology excels at pattern identification while security professionals provide contextual interpretation.

The transformation isn't about replacing analysts but elevating their function from mechanical data processing to high-value interpretation and response planning. What previously consumed 80+ hours now requires under 60 minutes of direct engagement, with dramatically improved analytical depth.

The result: security teams shifting from "what happened" to "what it means" and ultimately to "what we should do about it"—all within a single afternoon rather than weeks of fragmented research.

Have a great weekend!

Marco

P.S.: Want to Transform Your Security Risk Management?

My Security Risk Management AI Course delivers these exact templates and workflows (and many more).

Only 8 spots remain for the March cohort.

Secure your spot now:

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