Securing ML Systems Requires Understanding ML Technology

The key to ML Security is to understand the unique types of attacks possible and then to have the technology to differentiate attacks from legitimate use.

We understand the major vulnerabilities of ML Systems

  • Input Extraction Attacks
  • Training Data Extraction Attacks
  • Model Extraction Attacks
  • Input Manipulation Attacks
  • Training Data Manipulation Attacks
  • Model Manipulation Attacks

We analyze ML System interactions to protect the system

  • Zero Trust Framework
  • Contextual Analysis of Input
  • Contextual Analysis of Output
  • Conversation Logic Analysis
  • Legitimate Use vs Threat
  • Allow, Block, Escalate or Track

ML Security Technology in Action

Scanta’s VA Shield is the first product implementing our advanced ML Security Technology into a solution to protect Virtual Assistant Chatbots from Machine Learning attacks.