Copilot for Cybersecurity: Leveraging Generative AI for Security Log Analysis

📖 Course Overview

This repository documents key learnings, notes, and resources from the Coursera course “Copilot for Cybersecurity: Leveraging Generative AI for Security Log Analysis.”

The course explores how Microsoft Copilot and other generative AI tools can be applied to cybersecurity workflows, with a focus on security log analysis. By combining AI-driven insights with human expertise, learners gain practical skills to detect, investigate, and respond to threats more effectively.

🎯 Learning Objectives

By the end of this course, you will be able to:

• Understand the role of generative AI in modern cybersecurity operations.
• Apply Copilot for Security to analyze and interpret complex security logs.
• Automate repetitive tasks such as log parsing, anomaly detection, and summarization.
• Enhance threat detection and incident response with AI-assisted workflows.
• Evaluate the strengths and limitations of AI in cybersecurity contexts.

🛠️ Key Topics Covered

• Introduction to Copilot for Security and its integration into SOC workflows.
• Using natural language prompts to query and analyze log data.
• Identifying patterns, anomalies, and potential threats in large datasets.
• Case studies on real-world security incidents and AI-assisted investigations.
• Ethical considerations and responsible AI usage in cybersecurity.

📂 Sample Prompts :

🧑‍💻 Example Prompts for Copilot in Cybersecurity Log Analysis

These prompts are designed to help you experiment with Copilot for Security (or other generative AI tools) when analyzing logs.
You can adapt them to your own environment, log formats, and security use cases.

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🔍 Log Parsing & Summarization

• "Summarize the key events in this Windows Event Log snippet and highlight any unusual activity."
• "Parse this Apache access log and identify the top 5 IP addresses by request volume."
• "From this firewall log, extract all denied connections and group them by source IP."

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🚨 Threat Detection

• "Analyze this log for signs of brute-force login attempts. Provide evidence and reasoning."
• "Identify any suspicious PowerShell commands in this log snippet and explain why they may be risky."
• "Look for indicators of lateral movement in these authentication logs."

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📊 Pattern Recognition

• "Detect anomalies in this 24-hour log dataset. Which events deviate most from the baseline?"
• "Group these log entries by user account and summarize their activity patterns."
• "Identify repeated failed login attempts followed by a successful login from the same IP."

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🛡️ Incident Response Support

• "Generate a timeline of events from this log to support an incident investigation."
• "Correlate these IDS alerts with firewall logs to determine if the attack was successful."
• "Provide a plain-language summary of this log data for a non-technical executive report."

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⚖️ Responsible AI Use

• "Explain the limitations of using AI for detecting threats in this log sample."
• "Highlight where human analyst review is still required after this AI-assisted analysis."

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💡 Tip: When using these prompts, always:

• Provide context (e.g., type of log, timeframe, suspected issue).
• Ask for explanations, not just answers, to understand the AI’s reasoning.
• Validate AI findings with your own expertise and other tools.