Artificial Intelligence and Machine Learning (AI/ML) are strategic technologies for every data driven organization and therefore securing it is essential to the business. Developing a robust cybersecurity plan can often be timely and expensive. But when it comes to artificial intelligence and machine learning (AI/ML) systems, the game changes.
AI/ML systems hold the same opportunities for exploitation and misconfigurations as any other technology, but it also has its own unique risks. As more enterprises focus on major AI-powered digital transformations, those risks only grow in vulnerability.
There are 2 critical assets in AI. The first is data, big data. This is how AI can learn/train and infer its predictions and insights. The second asset is the data model itself. The model is the result of training the algorithm with big data and this is a competitive edge of every company. Data driven companies should design a solution to secure both big data and data models to protect their AI projects.
Since artificial intelligence and machine learning systems require higher volumes of complex data, there are many ways in which AI/ML systems can be exploited. While there are mechanisms in place for detecting and thwarting attacks that organizations can leverage, the question remains, how to leverage existing principles for confidential computing in order to develop secure AI applications.
If you take a simple machine learning model where test points are drawn from the same distribution as training data, then hypothetically the ML system should work to make correct predictions. But in real-world scenarios, far greater ambiguity can arise. A simple example of this can be that the ML system makes a low-confidence or incorrect prediction.
An emerging space within AI is the need to share and access more big data from semi trusting parties in order to achieve better models and insights. A good example is multiple healthcare providers sharing images and their interpretation in order to create an AI model to detect anomalies on its own. The more images the better algorithm. This scenario requires the model to access data from all healthcare providers, but assure that images cannot be accessed by each individual healthcare provider to another.
While these are just a few examples of the many ways hackers can target ML systems, there are plenty of opportunities for an attack at every step in the ML process. From poisoning training data to making inferences about confidential training data to learning how to perturb an input, there are a multitude of attack vectors that can lead to the exploitation of AI/ML systems.
Below are just a few examples of top threats facing AI/ML systems in 2021.
Top Security Threats for AI/ML Applications
One of the most frequent attacks on ML systems is designed to cause high-volume algorithms to make false predictions. This is done by providing the system with malicious inputs. Essentially, this kind of attack is intended to show machines a picture that does not actually exist in the real world, forcing them to make decisions based on unverified data. The impact of such an attack can be catastrophic since its effect can be both lasting and far-reaching, making it a much greater threat than many other ML security risks.
Data Corruption & Poisoning
Since ML systems rely on large sets of data, it’s critical for organizations to ensure their datasets’ integrity and reliability. If not, their AI/ML machines may provide false or malicious predictions through the targeting of datasets. This kind of attack works by corrupting, or “poisoning” that data in a manner that is intended to manipulate the learning system. Businesses can prevent such a scenario through strict PAM policies which minimize the access bad actors have to training data within confidential computing environments.
Transfer Learning Attacks
A majority of ML systems leverage a machine learning model which is pre-trained. The machine’s specifications are designed to fulfill designated purposes through specialized training. This is the window of attack where a transfer learning attack can be fatal to an AI/ML system. For example, if the chosen model is well-known, it isn’t difficult for an adversary to launch attacks that deceive a task-specific ML model. It’s critical for security teams to stay alert for suspicious activity or unanticipated machine learning behaviors which can help identify attacks like these.
Online System Manipulation
The internet plays an important role in the development of AI/ML systems and most machines are connected to the internet when learning, giving adversaries a clear attack vertical. In this scenario, hackers can mislead ML machines by giving systems false inputs or gradually retraining them to provide faulty outputs. Scientists and engineers can prevent this kind of attack in a few ways, including streamlining and securing system operations and maintaining records of data ownership.
Safeguarding the privacy and confidentiality of large volumes of datasets is crucial for researchers. This is especially important when the data is built right into the machine learning model itself. In this scenario, attackers may launch inconspicuous data extraction attacks which place the entire machine learning system at risk. Another attack vertical can also come from smaller sub-symbolic function extraction attacks which require less effort and resources. To protect themselves, organizations must not only safeguard ML systems against data extraction attacks but also set policies in place which would work to prevent function extraction attacks.
In order for organizations to secure their AI applications and machine learning models, they must leverage security solutions that provide hyper-secure confidential computing environments. When confidential computing is combined with the right cybersecurity solutions, such as a resilient Hardware Security Module (HSM), it can provide robust end-to-end data protection in the cloud for AI applications – big and small.