ML Case-study Interview Question: LLM-Driven Automation for Generating Secure Enterprise Incident Summaries

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Case-Study question

A major enterprise that operates globally faces a large volume of security and privacy incidents each day. They must create incident reports and summaries for multiple stakeholders, ranging from technical leads to senior executives. These reports must include root cause, impact, and remediation steps. The enterprise wants to integrate Large Language Models into its internal incident response workflow to generate these summaries more efficiently. Propose a detailed plan covering data ingestion, prompt engineering, infrastructure design, accuracy and privacy safeguards, and quality control steps. Include how you would minimize hallucinations or factual inaccuracies, ensure protection of sensitive data, and maintain compliance with global regulatory requirements. Explain your solution in a way that any Senior Data Scientist at a FANG Company could implement and validate.

Proposed solution

Overview of the approach

Start by standardizing how incident data is collected and structured. Store logs, code snippets, and metadata in a centralized system. Clean and label the data so the model only receives text relevant to the incident. Use placeholders for code or lengthy logs to preserve context and save tokens for important facts. Feed this structured data into the Large Language Model through a carefully designed prompt.

Prompt engineering

Ask the model to focus on the most recent incident facts and to follow your organization’s writing style guidelines. Inject examples of high-quality summaries. Maintain tags like

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