PrismBlocks provides a platform for managing and optimizing cloud infrastructure costs across multi-cloud environments. The service offers automated governance tools to enforce spending policies and identify underutilized cloud resources. This results in predictable cloud expenditure and improved operational efficiency for engineering teams.
Funding
Funding not disclosed
Founders
Product
Problem
Blockchain protocols are susceptible to vulnerabilities, leading to breaches and financial losses for users and investors. Traditional security measures often fail to detect novel exploits and anomalies in real-time, leaving significant value at risk. The increasing complexity of blockchain transactions exacerbates the challenge of identifying and mitigating potential threats.
Solution
PRISMA offers an AI-powered engine designed to detect and modify vulnerabilities in blockchain protocols, enhancing the security of decentralized applications. The platform employs a data-driven methodology, analyzing extensive blockchain transaction datasets for accurate anomaly detection. By utilizing tree-based ensemble models, PRISMA effectively handles the complexities of blockchain transactions to identify potential fraud and exploits. The system monitors blockchain activity, identifies critical exploits, and provides real-time alerts and mitigation strategies, securing digital assets and preventing financial losses.
Target Audience
The primary target audience includes blockchain developers, decentralized application (dApp) creators, cryptocurrency exchanges, and institutional investors seeking to secure their blockchain assets and infrastructure.
Features
- AI-based engine for vulnerability detection and modification
- Data-driven methodology analyzing extensive blockchain transaction datasets
- Tree-based ensemble models for handling transaction complexities
- ADASYN implementation to combat imbalanced datasets and ensure equitable fraud detection
- Real-time monitoring of blockchain activity for anomaly detection
- Explainable AI focus for transparency and trust in anomaly detection processes