AI-driven Cybersecurity & Threat Intelligence
This project uses AI and data-driven methods to detect, prioritize, and explain cyber threats— transforming raw logs, signals, and OSINT data into actionable intelligence for security teams, SOCs, and automated defence systems.
Project Overview
The AI-driven Cybersecurity & Threat Intelligence initiative focuses on turning noisy, high-volume security data into clear, prioritized insights. It brings together logs, network telemetry, endpoint events, and external intelligence to help teams spot real attacks faster and automate parts of incident response.
Current focus tracks:
- Intelligent detection & correlation across endpoints, network, identities, and cloud.
- Threat scoring & prioritisation based on risk, context, and business impact.
- AI-assisted investigation to summarize signals, propose hypotheses, and suggest actions.
Objectives
- Reduce alert fatigue by grouping, enriching, and scoring events automatically.
- Improve detection of subtle, low-and-slow attacks using behavioural and statistical models.
- Give analysts better context with entity timelines, summaries, and attack path views.
- Enable safer automation via playbooks, recommendations, and human-in-the-loop workflows.
Tech Stack & Methods
The project combines classic security analytics, machine learning, and LLM-based reasoning:
- Data: SIEM logs, EDR/EPP telemetry, network flows, identity events, OSINT/feeds.
- Models: Anomaly detection, clustering, supervised classifiers, and graph-based analytics over entities (users, hosts, IPs).
- LLM & reasoning: Natural language summaries of incidents, hypothesis generation, and threat-hunting assistants.
- Integrations: Connectors for existing SIEM/SOAR tools and cloud security platforms.
Real-world Applications
- Detection of account takeover, lateral movement, and data exfiltration patterns.
- Prioritized alert queues that show which hosts, users, or apps need attention first.
- AI-generated investigation notes, impact summaries, and incident reports for SOC teams.
- Attack surface monitoring—watching misconfigurations, exposed services, and weak points.
Security & Governance
The design emphasises least-privilege access, strong audit trails, and explainable models so that security teams can trust and verify the system’s outputs while meeting compliance needs.
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