Interest in generative artificial intelligence (AI) has peaked, along with broader concerns about artificial intelligence, as evidenced by an open letter calling for a halt to AI research. But how real are the threats of AI? What threats does generative AI pose, especially from a cybersecurity perspective?
Common AI Threats – Understanding AI and Applying It Properly
AI is already driving change in many industries, and its increasing sophistication suggests the potential for great disruption. This is a prospect that fuels the fear of workers being replaced. For example, we have already seen this starting to roll out in content creation using generative AI.
AI is here and many of its use cases are being discovered over time, new technologies are being used, and the industry needs to understand AI better to find ways to use it appropriately. is needed.
Fear of replacement is nothing new. Such concerns became apparent with the advent of assembly lines and the introduction of robots in manufacturing. However, to be fair, there are fundamental differences between AI and previous innovations. It is an innate ability to adapt. This introduces an element of unpredictability that makes many people uncomfortable.
As generative AI becomes more sophisticated, it becomes more difficult to separate humans from AI. Current iterations of generative AI have already demonstrated their ability to pass the Turing test, which measures an AI’s ability to trick humans into believing it is human.
What do you do when you can’t distinguish between humans and man-made things? How can you trust your identities, data, or communications? It must be authenticated, approved and continuously verified.
How and at what pace AI will evolve remains to be seen, but in the meantime there are some current and potential cybersecurity implications to consider.
Scaling cyberattacks
A few years ago, we were introduced to AI-generated art, and many artists were clamoring for it. However, some believed that AI could help artists create more by doing iterative work. For example, illustrators can use AI to repeat patterns they create and quickly fill in the rest of the illustration. This same principle can be applied by malicious actors expanding their cyberattacks.
Most hacks are done manually. In other words, large-scale cyberattacks require a large number of people. Attackers can use her AI to reduce the tedious and time-consuming elements of hacking, such as gathering data about targets. Being one of the biggest cybersecurity threats, state actors are more likely to own the resources to invest in advanced AI to scale up cyber intrusions. This allows attackers to attack more targets, potentially increasing the likelihood of discovering and exploiting vulnerabilities.
Villains and generative AI
Users can ask generative AI to create malicious code or phishing scams, but developers claim generative AI does not respond to malicious queries. Still, malicious actors may be able to find indirect means of deriving code from generative AI. Generative AI developers must continually revisit their parameters to ensure new vulnerabilities are not being exploited. It is the dynamic nature of AI that requires constant vigilance.
Threat actors can also exploit generative AI to exploit human error, which significantly impacts security vulnerabilities. These villains can use AI to exploit people through social engineering. Social engineering refers to a range of malicious activities that use psychological manipulation through human interaction to force security breaches. The superior natural language processing capabilities of generative AI can be very effective in streamlining such social engineering efforts.
AI is a Tool: Defending Against Generative AI
While many are quick to jump to potential risk-generating AI poses, it is equally important to recognize the human element that is so closely tied to AI. Cyber defenders can use this tool as a defensive mechanism. start attacking.
One of the main takeaways from Verizon’s Data Breach Investigations Report (DBIR) is the human involvement in cybersecurity breaches, whether through the use of stolen credentials, phishing, or basic human error. The important role played by the element. People are becoming susceptible to social engineering tactics that generative AI directed by threat actors can implement at scale. This ability to scale sophisticated digital fraud can put citizens, consumers, and businesses at increasing risk. This threat is only exacerbated by evolving workplace arrangements that make managing login credentials more complex as employees move between work and home, and between work and personal devices.
The threat of pervasive threats reinforces the claims of Zero Trust, which adopts a “never trust, always verify” approach to cybersecurity. This is a model that acknowledges the reality that security threats can come from anywhere, including within your organization. A Zero Trust approach not only requires strict authentication of users, but also applies the same degree of discrimination to applications and infrastructure such as supply chains, clouds, switches and routers.
Building Zero Trust architectures and enforcement techniques is a daunting task, but AI can greatly simplify the process. In other words, a technology that could create a wide range of threats could also streamline implementation of the drastic security her protocol needed to stem such attacks.
AI out of the box
In reality, AI cannot be reversed. AI is a tool, and like any tool, it can be used productively or destructively. We need to anticipate how bad actors will use technology and use it to our advantage.
