Protects the investments and competitive advantage provided by your AI models.
AI-based solutions influence us and our society - often without our awareness - making decisions that can change the course of our lives. Despite the level of trust that we place in ML algorithms, these systems can be exploited. We present a taxonomy of attacks on ML and show how they work.
In an increasingly digital world, it is crucial to secure the AI model to protect sensitive data, maintain trust, and prevent potential misuse. Unlike traditional cyberattacks targeting software vulnerabilities or human interactions, AI-specific attacks exploit the complexity and challenges inherent in AI models themselves. Various characteristics of AI-specific attacks include data poisoning, model inversion, and other AI-specific vulnerabilities, making them a specialized concern within cybersecurity. Today, we observe the following methods for manipulating the AI model:
Machine Learning Detection and Response (MLDR) is the first cybersecurity solution of its kind that monitors, detects and responds to attacks targeting AI models. The patent-pending technology provides a non-invasive, software-based platform that monitors the inputs and outputs of your machine learning algorithms for anomalous activity consistent with adversarial ML attack techniques. Response actions are immediate with a flexible response framework to protect your ML.
Protects the investments and competitive advantage provided by your AI models.
Preserves the effectiveness and integrity of each version of the AI models.
Strengthens and secures the integrity of AI models.
Provides insight into the risks and attacks threatening your models and where they are most likely to occur.
Utilizes MITRE ATLAS tactics and techniques to detect adversarial machine learning.
Increases the returns on AI projects and converts more models into production.
