In 2022, generative AI has gone mainstream, but what many don’t know is that generative models can also have a dark side, they have given rise to a new breed of cyber attacks. These attacks exploit the defining property of generative models—their ability to generate plausible new examples of some type of data, to synthesize passwords or fingerprints to break authentication, to masquerade malware as harmless software to avoid detection, and much more.
Generative AI will cause a revolution in cybersecurity that will lead to unprecedented changes, this tools can create infinite variations in a very iterative way. Generative AI is a category of machine learning in which computers generate original new content for a given conceptual purpose and context.
At first glance, these cyberattacks may appear quite different, but we believe they adhere to the same attack model, which uses a generative model to recover real-world examples. Identifying the attack pattern allows us to observe other salient qualities, such as a common competitive pattern and similar goals and capabilities. Likewise, the defenses against these cyber attacks are also comparable, such as – restricting access to training data, restricting access to the authentication system, and others. If we know that generative cyberattacks with shared attack patterns also share competitive patterns, defenses, and other characteristics, then many important attributes of a new generative cyberattack can be ascertained simply by classifying its attack pattern.
We are still in the early stages of this revolution and many practices still need to be perfected, we are only at the starting line. There’s still a lot of work to do as we figure out how to apply this new technology to cybersecurity, and there’s a huge opportunity for companies that move quickly into this new space.
Artificial intelligence has become an important tool in the fight against cyber attacks. Using the power of machine learning, AI-based cybersecurity systems can detect and therefore stop attacks with a speed and accuracy that traditional cybersecurity systems cannot match.
Enterprises are rapidly moving toward zero trust—the assumption that everyone who enters an organization is a bad person. An AI-driven approach to cybersecurity provides the scale to cover all relevant machines and network traffic, as well as the adaptability to identify many new threats and vulnerabilities that cybersecurity teams and their software tools have never seen before.
Attackers have learned to automate their attacks and increase their frequency. Because of this, alert fatigue, false positives, the sheer volume of attacks, and the amount of raw data available for analysis make responding an almost impossible task for SOC analysts. Attack sophistication is advancing every day, and we’re seeing a significant increase in attacks that leverage existing scripting capabilities, such as Power Shell and existing network management tools, to spread and move laterally across corporate networks. Cybercriminals hide their attacks in the noise caused by the unmanageable number of alerts and false positives for analysis by security operations.
New viruses, often called zero-day threats, infiltrate data centers before security teams widely distribute updated signatures. The problem security teams face today is that traditional applications cannot update malware signatures fast enough.
Sophisticated malware is modified daily or even hourly. Artificial intelligence can be trained to detect such threats by scanning for suspicious behavior or traffic patterns that conflict with known signatures.
Companies use AI that learns from past attacks and adapts to new threats, making it more effective at detecting and preventing future attacks. In addition, AI-based cybersecurity systems can help prevent attacks by automatically applying countermeasures to block suspicious traffic, quarantine infected systems, and even undo any changes made by attackers. This helps minimize the damage caused by the attack and prevents it from spreading to other parts of the network.
Traditional cyber professionals can no longer effectively defend against the most sophisticated threats as the speed and complexity of attack and defense exceed human capabilities.
Data scientists and other human analysts already in the enterprise can use AI to look objectively at all data and detect threats. Vulnerabilities will emerge, so using artificial intelligence and human data science techniques will help find the needle in the haystack and respond quickly.
As identity-based attacks increase, customized solutions will be the number one requirement for enterprise security centers.
Cybersecurity is everyone’s concern, so companies need to become more transparent and share different types of cybersecurity architectures. Democratizing AI empowers everyone to contribute to solutions. As a result, the collective defense of the ecosystem will respond more quickly to threats.
The ability of AI-based systems to quickly and accurately identify and prevent attacks provides a powerful tool for organizations looking to improve their security posture and defend against cyber threats.
Most large enterprises invest heavily in cybersecurity, yet it is virtually impossible to analyze the thousands of data that flow in and out of an organization through servers and client devices such as computers, tablets, and smartphones.
Typically, companies either inspect data packets or scan and index them for later intelligence, but as the amount of data grows exponentially, the chances of missing a threat are constantly increasing.
And to add to the complexity, hackers have become very talented at using fake credentials to break into data centers. It is no longer enough to simply secure the perimeter, organizations must assume that a breach has occurred and the attacker is already on the network.
To date, no cybersecurity platform includes a comprehensive systematization or taxonomy of machine learning-powered generative cyberattacks.
The taxonomy we began developing at LogSentinel to fill this void is based on the observation that cyberattacks powered by generative machine learning exhibit a repeating set of attack patterns. Taxonomy of generative cyberattacks according to these attack patterns creates a streamlined and scalable systematization that not only helps us identify patterns in seemingly disparate generative cyberattacks, but also helps us prepare for unproven threats and even potential defenses in each attack pattern.
Next-generation cybersecurity products increasingly incorporate artificial intelligence and machine learning technologies. At LogSentinel, we help companies improve the protection of confidential data and secrets. By training AI software on large datasets of cybersecurity, network, and even physical information, cybersecurity solution providers aim to detect and block anomalous behavior even if it exhibits no known “signatures” or patterns. LogSentinel helps companies by providing, in addition to its platform, a dedicated set of consultants, AI engineers, and data scientists.