AI Security Risks and Threats

Top 7 AI Security Risks and Threats

AI has the potential to revolutionize many industries, including cybersecurity. However, the power of AI also comes with significant security risks.

#1. Data Breaches

AI models are trained on large volumes of data. This data includes labeled instances of the types of events that the AI is designed to detect. For example, AI trained to identify threats in network traffic needs training data that contains examples of both normal and malicious traffic. These collections of training data can contain sensitive information about an organization’s customers and the business. Storing and using this data to train AI runs the risk that it will be breached by an attacker.

#2. Adversarial Attacks

AI systems are trained to develop models that are useful to achieve particular goals. For example, an AI system may be taught to differentiate between benign files and potential malware in network traffic.

Cyberattackers may attempt to train their own AI systems to learn the models of the defensive ones. This may allow the attackers to identify means to slip attacks past the AI system by discovering gaps in its model.

#3. Data Manipulation and Data Poisoning

Data manipulation and poisoning attacks target the labeled data used to train AI models. The attackers will introduce additional, mislabeled instances into this collection of data. The goal of these attacks is to train the AI’s model incorrectly. If the training dataset has attack traffic labeled as benign, then the AI model will not recognize those attacks. This presents the attacker with the opportunity to slip past the AI system once it has been deployed.

#4. Bias and Discrimination

An AI model is only as good as its training data. AI models are trained by presenting them with many labeled inputs and allowing them to build models that produce the desired outputs. The problem with this approach is that biased training data produces biased AI models. The most common example of this is the fact that facial recognition systems are predominantly trained on images of people from certain demographic groups. Often, these systems have a much higher error rate for people outside of the demographic groups represented by its training dataset.

#5. Lack of Transparency

AI is well suited to identifying patterns, trends, and relationships within data. After training the AI, its model will reflect these trends and be capable of making decisions and identifications based on these patterns. However, the models used by AI systems are not transparent or interpretable. This makes it infeasible to determine whether the AI’s model contains biases or errors, such as those introduced by a corrupted training dataset.

#6. Automated Malware Generation

ChatGPT and similar tools have already demonstrated a certain level of proficiency in programming. While code written by GenAI may have errors, it can expedite the code development process and develop sophisticated apps. GenAI tools have protections against writing malware; however, these guardrails often have loopholes that allow them to be bypassed. GenAI can allow less sophisticated threat actors to develop advanced malware, and its capabilities will only grow in the future.

#7. Model Supply Chain Attacks

Training an AI model is a complex challenge. An organization needs to collect a large corpus of labeled data and use it to train an AI model. This requires access to both data and expertise in machine learning and data science. As a result, many organizations will use AI models developed and trained by third parties. However, this introduces the risk that attackers will target the organizations developing the model, injecting malicious training data or taking other steps to corrupt the model.

Zero Trust Networks with the Infinity Platform

The introduction of LLMs and GenAI makes implementing a zero-trust security architecture more important than ever. At the same time, the capabilities of these systems also dramatically complicate zero-trust implementations and make ZTAI essential.
Implementing ZTAI can be complex and requires granular visibility and control over all actions performed by LLM-integrated applications within an organization’s environment. Check Point’s Infinity Platform offers the tools that your organization needs to implement effective zero-trust in the age of AI, including GenAI security for safe GenAI adoption in the enterprise and integrated AI capabilities for advanced threat prevention and response.