CyAIber builds focused AI tools that help you understand risk in AI-driven environments — surfacing meaningful signals, securing LLM interactions, and reducing analysis fatigue.
Built from real-world needs, not hype decks.
Early conversations have been with security and data teams exploring how to secure LLM deployments, monitor on-chain risk, and operationalize ML insights. Pilot programs available for early adopters.
Help teams ship LLM-powered features safely with lightweight guardrails that catch risks before they hit production.
Help protocols, treasuries and infra providers reason about on-chain risk before capital moves.
Use platforms like BigQuery to build models that feed product, GTM and risk decisions.
Live or near-live builds that can be evaluated as-is, forked internally or evolved into something tailored to your stack.
A lightweight gateway that scans prompts and responses for prompt-injection, data-exfil patterns, jailbreak attempts and other risky behaviour before they hit your model APIs.
Built with Python & Streamlit, ships with a growing set of OWASP-inspired checks and support for multiple model providers (Together, OpenAI, DeepSeek, Anthropic). Designed to sit in front of your existing LLM stack.
A hybrid ML framework for scoring transactions across EVM, UTXO and high-throughput chains, aimed at detecting exploit-style behaviour before blocks finalise.
Uses chain data (e.g. BigQuery), engineered features from traces, and models like XGBoost + Isolation Forest to flag patterns related to re-entrancy, flash loans, bridge abuse and other common attack paths.
An end-to-end pipeline that turns raw product, CRM and event data into segments, churn-risk scores and next-best actions – all living natively inside BigQuery.
Cleans and joins data, trains models, and exposes results as queryable tables for GTM, risk and product teams. Optional LLM layer for summary views and account briefs.
Active development phase. Priority given to design partners willing to provide feedback on early builds.
CyAIber started as a security content initiative exploring cybersecurity, OSINT, blockchain, and emerging tech. With the acceleration of AI, it evolved into an R&D-focused effort to build practical AI-powered security tools and share the learning with the community.
Exploring LLM security, on-chain risk detection, and data intelligence platforms. Best fit for teams that want strong controls without killing velocity.
Early conversations have been with security, data or product leaders who already feel the pressure of shipping AI-driven features and financial rails safely.
Share what you're building, where security or risk is keeping you up, and what a "win" would look like.