Federated AI for Collaborative Cyber Threat Detection

We are building a consortium for Horizon Europe CL3 (Civil Security) or French PTCC calls focused on applying federated learning and edge AI to advanced cyber threat detection. Our approach enables security operations centers and network defenders to leverage collective threat intelligence without exposing sensitive network data or attack patterns.

What we bring

• Manta Platform: Production-ready orchestration for distributed AI workloads across security infrastructure
• Cascade Detection Architecture: Multi-layer threat analysis reducing data transfer by 90% while maintaining detection accuracy
• Federated Learning Expertise: Proven algorithms for collaborative model training without sharing operational data
• Edge-First Processing: Local threat detection with sub-second inference at network endpoints

Current consortium strength

• AI Platform Provider (Manta): Edge orchestration and federated learning infrastructure for security operations
• Research Connection: Collaboration pathways with INRIA security researchers

What we're seeking

• Network Security Vendors: NDR/IDS providers with existing probe deployments
• SOC Operators/MSSPs: Security operations centers with diverse threat landscapes
• Threat Intelligence Companies: Organizations with specialized detection algorithms
• Research Institutions: Cybersecurity research groups with anomaly detection expertise
• Critical Infrastructure Operators: Energy, telecom, or transport operators needing sovereign security

Target Applications

• Collaborative anomaly detection across distributed network probes
• Federated learning for zero-day threat detection without sharing attack signatures
• Cascade AI processing: edge filtering → regional aggregation → global intelligence
• Privacy-preserving threat intelligence sharing between organizations
• Real-time attack pattern recognition across heterogeneous environments

Expected Impact

• Reduce false positive rates by 40% through ensemble learning
• Cut security data transfer costs by 80-90% via edge processing
• Enable sub-2-minute threat detection across distributed infrastructure
• Create European sovereign threat intelligence capability
• Accelerate model convergence 3x through federated weight sharing

The Cybersecurity Challenge

Modern cyber threats evolve faster than traditional signature-based detection can adapt. Security teams process petabytes of network data while struggling with 70% false positive rates that overwhelm analysts. Meanwhile, organizations cannot share threat data due to confidentiality concerns, limiting collective defense capabilities.

Our federated approach revolutionizes threat detection through cascade AI processing: local probes perform initial analysis, sending only high-confidence alerts upstream. Organizations benefit from collective threat intelligence through federated learning—improving detection accuracy without ever exposing their network data or attack patterns.

This architecture is particularly critical for European strategic autonomy, reducing dependence on non-EU threat intelligence platforms while ensuring operational data never leaves organizational boundaries.

Interested in joining our consortium? We're looking for partners who can provide real-world security challenges and complementary expertise in threat detection, network analysis, or security operations. Together, we can demonstrate how federated AI transforms cybersecurity while preserving operational confidentiality.

Contact us to discuss your use case and role in this innovative European collaboration.