Artificial Intelligence is a pervasive, impactful element in our everyday lives, shaping the way we work and experience the world around us.
For example, in the last few years it has enhanced Healthcare, enabling early diagnosis and finding the best treatment plans and even new drugs, according to historical data and medical intelligence.
In the Marketing & Sales sector, AI greatly improved business strategies and performances, providing customers with recommendations according to their predicted behaviors and prioritizing Sales actions based on lead scores and contact factors.
Artificial Intelligence has many different applications in the FinTech industry: from detecting fraudulent and abnormal financial behaviors to monitoring personal finance through Robo-Advisory.
AI for cybersecurity is also one of the fundamental applications today.
Artificial Intelligence: a blessing and a curse for cybersecurity
When it comes to cybersecurity, Artificial intelligence can be both a blessing and a curse.
Similarly to traditional hardware-software systems, AI-powered systems present specific features that can be attacked in non-traditional ways: the so called A.I.A. – Artificial Intelligence Attacks.
Through these attacks, cyber thefts can gain control over the state-of-art AI systems with carefully-perturbed input data samples, aimed to mislead the machine learning algorithm or crash the system.
Machine learning algorithms are impressive at dealing with large volumes of data, but they must be trained properly using well-labeled, accurate training datasets, or they will have to deal with data poisoning, model theft, or evasion attacks.
Data poisoning attacks manipulate the training dataset used to train machine learning algorithms, inducing misclassification to a specific test sample or a subset of the test sample.
An evasion attack happens when a machine learning algorithm is fed with an “adversarial example” — a carefully perturbed input that looks and feels exactly the same as its untampered copy to a human — but that completely throws off the classifier.
According to Gartner, artificial intelligence cyber-attacks will double year over year, but most organizations are still fighting Artificial Intelligence Attacks through general and limited methodologies, such as Application Security Testing (AST): there are no mature tools to identify, monitor and mitigate artificial intelligence attacks.
AIA Guard: The first European solution for assessing Artificial Intelligence Attacks vulnerabilities
Developed by Datrix, AIA Guard (aiaguard.com) is the very first European solution for assessing Artificial Intelligence Attacks vulnerabilities in a GDPR compliant manner. AIA Guard automatically analyzes your entire machine learning workflow with particular attention to data poisoning, inference attacks, model stealing, data leakage and adversarial machine learning.
It can be deployed on your premises or private Cloud and does not require interaction with external sources, it can extend existing modules in order to meet client specific needs. It is a very intuitive and easy to use tool, making sure that key messages and recommendations are delivered efficiently to both technical and non technical end-users.
It first identifies security weaknesses in the source code of the machine learning application, scanning software dependencies for known vulnerabilities.
Secondly, it detects sensitive information, identifying risks for data leaks on sensitive data and personal information (phone numbers, emails, zip code, etc).
It performs evasion analyses focused on textual ML models. These attacks could hijack the model toward a misleading behavior generating adversarial examples that can fool the target classifier.
Through a vulnerability assessment penetration test, it detects vulnerabilities on the exposed services and APIs which could undergo malicious activities.
At the end, AIA Guard provides companies with actionable insights, generating clear reports of all the executed procedures, suggesting corrective actions to enhance security and efficiency.