Benefits of Our AI Vulnerability Assessment & Testing Service
- A thorough & economical assessment
- Testing real AI vulnerabilities and ways to mitigate them
- Offering a prioritized risk identification matrix
- AI Compliance with standard requirements such as IS027001, PCI-DSS, SOC1 and SOC2
- Added protection to your company brand and reputation
Our AI Vulnerability Assessment and Testing Services
These days, AI-based image and video recognition systems surpassed the human in image classification, object identification, and illegal object detection.
Many companies benefit from cloud-based computer vision services without even training or hosting their own AI models. Even state-of-the-art deep learning image and video recognition systems are surprisingly susceptible to different adversarial attacks i.e. through small perturbations to images that remain imperceptible to human. Many of these attacks are black-box attacks where attackers do not even need to know the type, structure, or parameters of the model, which make cloud-based image and video recognition systems quite vulnerable.
We can test robustness and security of your cloud-based or on-prem image and video recognition AI systems with a range of query-based, transfer learning-based and spatial transformation attacks. Our AI-Security scientists are having real-world experience in effectively degrading the performance of many cloud-based image classification services including Amazon, Google, and Microsoft. Our experts run both black-box and white-box testing of your systems and work closely with your engineers to make your image and video recognition AI systems robust, safe and secure.
AI-based text understanding is the backbone of many services, including question answering, chatbots, machine translation, and text classification. Many online recommendation systems including credit card recommendation tools rely significantly on text analysis AI systems.
Despite tremendous popularity and increasing use in security-sensitive applications, text analysis AI systems are vulnerable to many attacks including adversarial text attacks.
Our experts have extensive experience in vulnerability assessment of state-of-the-art text classification and deep learning text understanding (DLTU) systems. Our AI-Security scientists could deceive many real-world DLTU systems including Google Cloud NLP, Microsoft Azure Text Analytics, and Amazon AWS Comprehend. We work closely with our clients to assess security of their systems and help them to offer robust AI-based text classification and understanding systems.
Systems that are using voice and audio such as Google Assistant, Apple Siri, Microsoft Cortana and Amazon Alexa are growing in popularity. Audio recognition AI applications maybe used in safety critical systems such as autonomous vehicles. However, these systems are potentially vulnerable to a range of adversarial cyberattacks.
Our AI-Security scientists can create audio adversarial examples to test safety, security and robustness of your systems and to identify vectors that can be used by attackers to submit unauthorized samples or jam the word detection mechanism and cause denial of service (DoS) attacks.
Many data scientists wrongly believe that generating adversarial samples require full access to the target model. Thus, keeping AI models in the cloud gives them a false sense of security. Many experiments showed that attackers may deceive cloud-based machine learning models without knowing the type, structure and parameters of the model.
Our AI-Security scientists may test your cloud-based AI systems even those that can be only accessed through APIs. We can collect inner information of your machine learning models with limited queries and without any access to the training data, model, or without any other prior knowledge of your system.
Like any other applications, AI and machine learning apps are vulnerable to design and deployment flaws. Some AI software vulnerabilities are systematic and can be exploited in all versions. Many other enterprise vulnerabilities are caused by AI misconfiguration or mistakes in the AI deployment.
Our AI-Security scientist can regularly assess your AI infrastructure and services including your local machine learning engines, cloud-based AI systems, and your AI data pipeline for timely identification of any vulnerabilities in your AI stack. The vulnerability scanning frequency will depend on your requirements and will ensure that you are covered throughout the year.
these are not the only AI vulnerability assessments we provide. As machine learning finds rich applications in other areas such as Internet of Things (IoT) networks, traffic management, spectrum sensing, signal authentication, and etc. our AI-Security scientists create customized AI vulnerability tests accordingly to your needs.