By 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)
- Specialist subagents reason over classical telemetry — LLMs augment, don't replace, SIEM/XDR
- Shared agent state across institutions — privacy-preserving federation enables collective defense
- Bounded human-in-the-loop autonomy — Main Agent coordinates, humans validate critical actions
- 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