AI & Privacy Risks

AI otherwise known as Artifical Intelligence is the development of computer systems that can perform tasks, and make their own decisions that usually reguire human intelligence. Where it involves the AI to create algorithms and models that would allow for the machine to learn from past experiences, learn data, recognize patterns, and adapt on the fly to new situations it is faced with (Malwarebytes, 2026). Overall, AI is like teaching computers to think and learn like humans, where it really gives the computer its own free will to some extents that are implemented by the developer of the AI.

Some risks with AI being more popular in recent times, has led to tactics such as Brute force, denial of service (DoS), and social engineering attacks that when used by humans aren't that much of threat (Malwarebytes, 2026). Yet, when you task an AI to do these tasks they will actively do everything they can to accomplish that task, by finding all the vulnerabilities that would normally take humans a long amount of time, where AI can speed up the process which puts a major risk on companies that are trying to ensure their data is kept secure. These AI tools that have been more popular as of lately, are becoming cheaper, and more accessible for everyday users to use, which further supports the reason for companies to be worried about that tool to be used against them.

1. Data Poisoning

• For the first attack listed, attackers input incorrect data in the dataset used to train the AI, which leads to corrupted data that can modify AI functionality and create false choices or predictions. New or modified false data points can then be added to the dataset, where it makes it impossible for the AI process to learn correctly (SentinelOne, 2026).

2. Model Inversion

• This attack seeks to recover the training data used for creating an AI. Where if a successful extraction has taken place then it leads to the data being taken and used for examination of its outputs, which is a severe privacy threat.

3. Adversarial Examples

• This type of example is meant to be misleading, where it's specifially crafted for AI systems, particially the machine learning domain. Where attackers make small, changes to input data that will result in misclassification or misinterpretation of the data that the AI will confuse and cause errors to not be detected.  Where adversarial examples that are used, can evade an AI based security system or manipulate the decision making of the AI.

4. Model Stealing

• For this attack to be successful, it requires a replica of the AI model, where its then used by the attackers to send multiple queries to the target model, and then use its responses to train a replacement model. That then can result in theft of intellectual property and a competitive edge.

5. Privacy Leakage

• For the last security risk is about how the AI model can memorize and leak sensitive information from the training dataset from time to time, it could be when the model is asked certain questions or when it generates outputs (SentinelOne, 2026). This can be a liability because such models tend to generate text based on training data. Where this type of attack must be carefully avoided by auditing AI systems regularly to prevent this from happening.