Privacy-preserving cross-bank AI threat intelligence — built on the FS-ISAC model, extended to the AI agent layer. One bank's detection improves everyone's defense.
Attackers share techniques. Banks share nothing. The Federation changes that asymmetry.
Whether SaaS or on-premises, no raw data — no prompts, no customer records, no transaction data — ever leaves your environment. Only encrypted, anonymized signal metadata is contributed.
A neutral aggregation hub receives only encrypted model updates. Using federated learning, differential privacy, and secure multi-party computation, it produces an improved global detection model.
Detection rules for attacks you haven't seen are distributed to every participant within minutes. Industry benchmarks show 20–40% improvement in accuracy over single-institution models.
Three established techniques in combination ensure no single party ever sees the full picture
Model updates are aggregated across participants — never raw data. Each bank trains locally; only encrypted gradients are shared.
Statistical noise added to every contribution prevents reverse-engineering of individual bank signals. Mathematical guarantees on privacy.
Aggregation occurs without any single party — including BladeRun — seeing the complete picture. Cryptographic proof of privacy.
Strengthen your AI defenses with collective intelligence — without compromising your data privacy.
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