Hi, I'm Abu Saeid

I am a dedicated **SOC Analyst and Cybersecurity Researcher** with a strong focus on bridging operational security with advanced analytical methods. My primary experience lies in Level 1/Level 2 Security Operations, specializing in **incident triage, threat hunting**, and the development of robust **SIEM (Wazuh/Elastic)** architectures. I translate threat intelligence into actionable detection rules, constantly tuning and hardening defense posture against emerging TTPs.

My core value lies in **automation**. I leverage Python and SOAR-lite methodologies to automate repetitive tasks, significantly reducing Mean Time to Detection (MTTD) and Mean Time to Respond (MTTR). This includes developing playbooks for automated enrichment, containment, and, critically, implementing **AI/ML models** for anomaly detection to overcome rule-based detection fatigue.

I am passionate about **Purple Teaming**, advocating for a collaborative approach where red-team insights directly inform and validate blue-team detections. This continuous feedback loop ensures defensive controls are measurable and effective. My academic work further explores the intersection of AI and security: Research Thesis: NeuroCrypt — Privacy-Preserving Federated Learning Framework for Secure BCI Data Sharing