Cyberaid Ai Security Framework

---
name: cyberaid-ai-security-framework
description: "AI-driven multi-agent cybersecurity framework for financial services. Hybrid system combining LLM subagents with classical SIEM/XDR telemetry, privacy-preserving federation, and quantum-based authentication. Use when designing AI-powered security operations, building multi-agent SOC systems, or creating privacy-preserving collaborative defense platforms."
---

# CyberAId AI Security Framework

## Core Problem

Security Operations Centers (SOCs) are constrained by reasoning capacity, not data or staffing:
- Enterprise SIEMs cover only a fraction of MITRE ATT&CK techniques
- Two-thirds of SOC teams cannot keep pace with alert volumes
- Majority of breaches preceded by alerts that were generated but never investigated

## Architecture: Hybrid Multi-Agent System

### Design Principles (4 Falsifiable Principles)

1. **Specialist subagents reason over classical telemetry** — LLMs augment, don't replace, SIEM/XDR
2. **Shared agent state across institutions** — privacy-preserving federation enables collective defense
3. **Bounded human-in-the-loop autonomy** — Main Agent coordinates, humans validate critical actions
4. **Regulatory alignment** — all findings map to relevant compliance regimes and survive audit

### Component Structure

```
Main Agent (coordination layer)
├── Reporting capability (audit-ready outputs)
├── Specialist Subagent 1: Threat Detection
├── Specialist Subagent 2: Incident Response
├── Specialist Subagent 3: Compliance Mapping
└── Specialist Subagent 4: Adversarial Validation
    └── Shared runtime with bounded autonomy
```

### Capability Packs

Extendable modules:
- **Quantum-based authentication** — post-quantum cryptographic protocols
- **Digital twins for adversarial validation** — simulated attack scenarios
- **eBPF-based kernel telemetry** — deep system visibility
- **Privacy-preserving federation** — cross-institution threat sharing

## Use Cases

### 1. Client Impersonation Detection
- Monitor for social engineering patterns
- Correlate with communication channels
- Alert on behavioral anomalies

### 2. Anti-Money Laundering (AML)
- Pattern matching across payment flows
- Real-time transaction risk scoring
- Regulatory reporting automation

### 3. Retail Banking Incident Response
- Automated triage of security alerts
- Playbook execution with human oversight
- Post-incident report generation

### 4. High-Frequency Trading Resilience
- Detect manipulation patterns
- Validate trading algorithm integrity
- Real-time anomaly detection

## Skill-Based Agent Adaptation

Most promising research direction: each deployment contributes to continuously refined collective defense through skill-based agent adaptation.

## Activation Keywords
- AI cybersecurity framework
- multi-agent SOC
- AI-driven security operations
- collaborative defense
- SIEM LLM integration
- financial cybersecurity
- privacy-preserving security federation
- CyberAId
- 网络安全AI框架
- AI安全运营中心

## References
- arXiv:2605.01892 - CyberAId: AI-Driven Cybersecurity for Financial Service Providers (Fatouros, Makridis, Soldatos)
- MITRE ATT&CK framework
- eBPF kernel telemetry