In the context of Cybersecurity Month, it is pertinent to address an innovative topic that has gained increasing importance in the current industry: artificial intelligence (AI). Its popularity has skyrocketed in recent years due to the release of tools that astonish the world, capable of generating code, music, images, text, video, and more, all with just a simple instruction.
One question that comes to mind is whether these developments are secure, as every technology offering benefits also presents opportunities for cyber attackers who explore new ways to achieve their goals. Therefore, the discussion on cybersecurity applied to artificial intelligence becomes crucial amidst the wave of innovation AI brings.
The Rise of AI and Its Vulnerabilities
AI is transforming the way we perform various tasks and has the potential to facilitate numerous aspects and sectors, including medical, business, and industrial sectors, among others. Many people are already using AI tools and even delegating critical tasks to them. However, despite the undeniable benefits of AI, it also comes with vulnerabilities.
Common AI Attack Methods
Various attack methods related to AI developments have been detected. Among the most common are data manipulation attacks, where attackers introduce malicious data into the system’s training phase to modify its learning and influence the results, creating unreliable and biased models with potentially malicious intentions.
Another method involves modifying or extracting input data, which can be imperceptible in machine learning models. For example, removing a few pixels from an image to prevent recognition. This type of attack, known as Adversarial Machine Learning, aims to make models fail and produce incorrect predictions or decisions.
A known and already alerted method is the Poisoning Attack, which attempts to corrupt the AI model by manipulating data during its training phase. This is known as a backdoor attack, as it requires close access to the development process. Such attacks are difficult to detect because they aim to perform specific malicious actions while appearing normal in functionality.
The Need for AI Cybersecurity
These examples illustrate why it is necessary to address cybersecurity in the context of artificial intelligence and the implications of a possible attack on its developments and tools. As AI becomes increasingly integrated into our lives and extends to critical tasks like autonomous vehicles, it is vital to consider the associated risks.
Another essential aspect requiring attention is privacy and data protection. It is crucial to be cautious in handling large amounts of information, as unauthorized use can have significant implications for organizations.
Measures to Enhance AI Cybersecurity
On a positive note, these attack methods are already considered in the NIST framework, which includes best practices for evaluating AI and machine learning models to prevent such attacks.
Despite cybersecurity challenges, measures can be taken to reduce risks in an organization. At Cybolt, we have experts who can advise you on securely using available tools or from the moment your organization considers AI development by implementing DevSecOps practices.
Our cybersecurity approach is based on the NIST framework, which has launched a project that includes AI security risks. This allows us to establish best practices such as model encryption, reinforcement of access control models, and implementing anomaly detection and management strategies for AI.
The convergence between cybersecurity and artificial intelligence is essential to ensure protection and reduce risks or prepare for an attack. Additionally, we must raise awareness about the conscious use of AI and consider its potential to violate data privacy rights.
We know this is a significant challenge, but we are convinced that together we can advance with safe AI developments.
Happy Cybersecurity Month, for more spaces of trust!
[1] Adversarial Machine Learning: An Introduction to Attacks on ML Models [2] Data Poisoning: A Threat to Machine Learning Models
