Thinking of using artificial intelligence? Take care with data confidentiality!
The use of platforms based on artificial intelligence and machine learning, such as chatbots and generative artificial intelligence tools, has become increasingly popular in several sectors. More accessible to the general public, these resources can actually assist professionals in their daily routines, automating tasks, facilitating customer service, and even creating entire code snippets to be embedded in commercial products.
However, it is not all roses. Many information security risks can arise when these tools are used carelessly. One of the main threats concerns entering sensitive data on these platforms. Remember that artificial intelligence tools use all the information provided by the users to improve their response capabilities. As such, they can pass this information on to third parties.
Let's imagine a simple example. When interacting with a generative artificial intelligence chatbot, users may end up sharing sensitive information like bank account numbers, passwords, medical data, or sensitive personal and corporate information.
If this data is incorrectly stored or used for platform training, it can be exploited by malicious individuals, resulting in privacy breaches and even fraud.
And when the AI is mine?
Another significant risk lies in data leaking from these tools. If a chatbot or other AI tool belongs to your organization, these resources should ensure that the information collected is stored securely and protected from unauthorized access. It’s common knowledge that data leaks can cause irreparable damage to a company's image, not to mention significant legal and financial consequences.
Lastly, machine learning algorithms can be trained on datasets that reflect societal biases and intolerances. If these algorithms are used in chatbots lacking proper review and mitigation, they may perpetuate bias and discrimination. Careful analysis and constant monitoring of the data used to train these tools are essential to prevent the reproduction of discriminatory biases.
Having basic measures implemented is essential to mitigate these information security risks when using AI and machine learning tools. Best practices include obtaining explicit consent from users before collecting any personal information and removing personally identifiable information from datasets used to train AI models.
Encryption is a major ally in protecting stored and transmitted data, establishing appropriate access controls to ensure that only authorized persons can manipulate the collected data. Furthermore, regular reviews of the algorithms and models used in the tools are necessary to identify and correct possible biases or unwanted behavior.
Training is vital for personnel that use and process data!
Finally, employee training and awareness also play a highly significant role in mitigating such risks. Training for the correct use of these technologies not only includes the transfer of best practices but also facilitates the identification of threats like phishing or other social engineering methods.
