Several studies have highlighted the potential dangers of machine learning as a medium for cybercrime: the threats to digital security extend beyond privacy and data theft to include the functional security of people within a fully connected ecosystem. This research was conducted by a large number of experts from universities including Oxford, Cambridge, Stanford, Yale, and Bath.
The emergence of artificial intelligence and related technologies has brought new potential to cybersecurity, but this is actually a double-edged sword; if it falls into the wrong hands, it can become dangerous. The importance of having a perfect cybersecurity strategy or solution has increased over the years. All credit goes to the proliferation of smart devices. Furthermore, with an ever-growing number of endpoints constantly connected to the IoT ecosystem, cybercriminals now have numerous opportunities to infiltrate devices. Due to the massive amounts of data stored, big data breaches can have devastating consequences in terms of system performance and functional safety. This is because large-scale data security vulnerabilities can affect a large number of people, with consequences not only from a reputational perspective but also with enormous legal repercussions. Organizations need to ensure they strike the right balance between data usefulness and privacy. Any unique identifiers of users should be removed before data is stored. This is a security challenge in itself, as deletion may not be sufficient to ensure data remains anonymous in the future.
As organizations store more data, they face challenges with both hardware and software encryption. Users cannot send data encrypted if the cloud needs to perform analytics. One solution is to use Full Homomorphic Encryption (FHE), which allows operations to be performed on encrypted data stored in the cloud. When the data is decoded, the result of operations performed on plaintext data will be the same. Therefore, the cloud will be able to perform operations on encrypted data without knowing the plaintext. Industry experts have analyzed the extensive use of machine learning algorithms by hackers and security professionals. A free-for-all will tighten enterprise defenses and, if handled properly, can also be used for targeted internal attacks, learning from mistakes, and creating an invincible system. The symbiosis of automation in vulnerability searching will prompt cybercriminals to accelerate the execution of viruses and subsequently search for further system weaknesses. For example, cybercriminals can use these capabilities to scan software to find previously unknown vulnerabilities and use them for illicit purposes. Machine learning can provide insights into malicious behavior, supplementing intelligence obtained through other means. It also allows for greater agility and flexibility, as AI-based tools are generally deployed faster and deliver operational results more quickly than other tools. After all, intelligent security systems can overcome any complex threat.
Artificial intelligence has become a highly competitive industry and is expanding rapidly, thanks to investments from high-tech companies and governments around the world. A recent study by Markets and Markets predicts a compound annual growth rate (CAGR) of over 60% in the coming years, with a value exceeding $15 billion. Hardware innovations are also showing strong growth, enhancing the operational capabilities of computers and enabling the execution of more complex models on various GPU platforms.
Most individual AI technologies will be used to automate various robotically controlled tasks that make critical decisions that could negatively impact the entire digital ecosystem. Automated intelligent machines can assess targets most likely to be attacked. Using advanced techniques, AI can hide “cyber infections” by manipulating systems and disabling security components. Once a system is infected, it can be used to spread various computer viruses, including advanced forms of ransomware.
Currently, most cybercrime organizations generate different samples by modifying their source code to use various well-known ransomware familyes. In a similar manner, artificial intelligence technology will be able to create its own custom malware, potentially from scratch by implementing advanced machine learning algorithms. Internet of Things (IoT) devices (such as medical devices) are likely to be a primary focus, as are the resources and data behind them. Cases to consider extend beyond computer viruses to aspects related to robotics and drones. From simple robots used for consumer or industrial activities to drones and future flying taxis for civilian use, these could all become potentially dangerous weapons and be easily remotely controlled.
Many engineers and analysts estimate that the next major AI-powered attack could occur within a few years. The biggest problems remain identity theft, denial-of-service attacks, and password cracking. In an increasingly digital world, such attacks could undermine the power of many and impact the administrative activities of public institutions. AI may be key to spear-phishing attacks, collecting and processing databases to easily link information from different sources and launch optimal attacks. Many attacks can be coordinated with AI, potentially confusing doctors' diagnoses. Malicious AI can be used to coordinate hacking attacks on a theoretically immeasurable scale. Security experts speculate that the greatest danger may come from AI that leverages the immense computing power of machines to create new virus samples. By design, they can analyze current virus weaknesses and generate sophisticated forms. The first drawback may be the high computational cost. However, these will inevitably decrease over time. Many organizations have begun to combat AI-powered malware and prepare for potential future attacks, and cybersecurity experts are understanding the risks, but with the theoretically limitless potential of AI, management becomes complex and seriously jeopardizes IT security. Furthermore, AI promises to support research across various fields, including physics, chemistry, and medicine.
