husnain-rumi/Threat-Intelligence-Automation-AI

# ⚙️ AI 驱动的威胁情报自动化 Pipeline ## 🎯 项目目标 本项目演示了如何将现代 AI 提取能力与自动化基础设施脚本相结合，以优化安全运营中心 (SOC) 的分诊流程。通过构建一个端到端的 Python pipeline，该系统能够接收原始、非结构化的网络安全事件数据，解析出关键的入侵指标，查询威胁信誉参数，并动态生成可执行的高管简报。 ## 🏗️ 系统 Pipeline 架构图 ``` [Raw / Chaotic Threat Text] │ ▼ [AI Data Extraction Engine] ➔ Extracts clean JSON metrics (IPs, Domains, Malware) │ ▼ [Intel Enrichment Module] ➔ Automatically queries Threat Reputation Database │ ▼ [Executive Report Module] ➔ Compiles metrics into a formatted incident response brief Python Core Implementation The automation script operates in three distinct programmatic modules: The Parsing Layer: Simulates an LLM agent structured to isolate data patterns within narrative logs. The Database Verification Layer: Reaches out to reputation indices to verify active infrastructure classifications. The Assembly Layer: Merges operational findings dynamically into a standardized template string. Operational Outputs When executed, the automation engine successfully processes messy event data and produces a clean, machine-parsed readout, immediately concluding with recommended mitigation procedures: Key Architecture Takeaways Automation Over Manual Overhead: Automating Tier-1 analysis workflows shrinks threat identification loops from hours down to milliseconds, allowing teams to isolate host clusters instantly. Unstructured Data Parsing: Leveraging semantic models allows organizations to reliably parse chaotic text streams (such as OSINT blogs or email threads) into structured data arrays for immediate programmatic ingest. ```

标签：C2, DLL 劫持, PB级数据处理, Python, SOC分析, 大语言模型, 威胁情报, 安全运维, 开发者工具, 无后门, 自动化Pipeline, 逆向工具