Layer 1 – AI-Powered Analysis

The first layer is an automated audit layer powered by artificial intelligence. It is designed for speed, consistency, and breadth of coverage:

Data Privacy & Encryption: Sensitive audit input and output are encrypted using modern standards (libsodium, OpenSSL) and enhanced with techniques like symmetric/asymmetric encryption and homomorphic encryption.
Data Preprocessing: AI also prepares and normalizes the input for deeper analysis by cleaning and segmenting critical logic, ensuring faster and more accurate detection.
Automated Code Analysis: AI parses and interprets smart contract code, configuration files, and system behavior using machine learning (ML), deep learning, and natural language processing (NLP).
Threat Detection & Pattern Recognition: Leveraging large datasets of known exploits and attack signatures, the AI can recognize common vulnerabilities such as reentrancy, overflow, underflow, access control flaws, and logic errors.
Simulated Penetration Testing: AI simulates attacks like DDoS, SQL injection, XSS, and reentrancy exploits using tools such as Metasploit and Nessus, allowing for realistic threat modeling.

This layer ensures that every audit begins with a broad, consistent, and efficient security scan—ready to be verified and enhanced by human insight.

Last updated 27 days ago

The first layer is an automated audit layer powered by artificial intelligence. It is designed for speed, consistency, and breadth of coverage:

Data Privacy & Encryption: Sensitive audit input and output are encrypted using modern standards (libsodium, OpenSSL) and enhanced with techniques like symmetric/asymmetric encryption and homomorphic encryption.
Data Preprocessing: AI also prepares and normalizes the input for deeper analysis by cleaning and segmenting critical logic, ensuring faster and more accurate detection.
Automated Code Analysis: AI parses and interprets smart contract code, configuration files, and system behavior using machine learning (ML), deep learning, and natural language processing (NLP).
Threat Detection & Pattern Recognition: Leveraging large datasets of known exploits and attack signatures, the AI can recognize common vulnerabilities such as reentrancy, overflow, underflow, access control flaws, and logic errors.
Simulated Penetration Testing: AI simulates attacks like DDoS, SQL injection, XSS, and reentrancy exploits using tools such as Metasploit and Nessus, allowing for realistic threat modeling.

This layer ensures that every audit begins with a broad, consistent, and efficient security scan—ready to be verified and enhanced by human insight.

Last updated 27 days ago