Security Building Blocks
Secure customer and investor funds from cyber attacks, internal collusion, and human error with a multi-layer technology that combines the latest breakthroughs in MPC cryptography with hardware isolation.
Funds Segregation
ChainPort brings a unique security architecture, funds segregation being the main one, only up to 5% of the assets will be in the hot bridge contract with the remaining up to 98% being on a rebalancing and treasury vaults.
Multi-sig Cold Wallets
All ChainPort wallets are secured by multiple layers of security being Fireblocks and Gnosis multi-sig wallets the main ones. All transactions need to be approved by multiple members of the ChainPort Congress.
FireBlocks MPC Encryption
ChainPort has an active multi-party computation MPC with hardware isolation creating a multi-layer security for store and transfers, powered by Fireblocks, the most popular MPC solution in the world. This eliminates a single point of failure and insulates digital assets from cyber-attacks, internal collusion, and human error.
Multiple Contract Audits
All the ChainPort smart contracts have gone through several audits by some of the most experienced auditing firms in the space, checking and analyzing all the possible attack surfaces. CertiK.org and CyberUnit have audited ChainPort contracts, and more audits will take place soon.
Ongoing Monitoring
CertiK Skynet intelligence engine provides 24/7 monitoring and analysis for the ChainPort smart contracts and maintains our security score. The service also alerts us to any type of security issue on our contracts.
Bug Bounty Program
ChainPort will soon present a bounty program to its community and users for finding bugs or errors in various parts of the product.
Insurance Program
Coming Soon
ChainPort will soon present an insurance plan to users interested in it and indemnify them for a part to full of their assets that are stored in the bridge in the unlikely event of assets loss.
Machine Learning Models
Coming Soon
ChainPort is building an automated system based on machine learning, monitoring and analyzing user behaviors and anomalies, combined with an alert and prevention system.
