Globally, generative AI solutions appear to have taken center stage above all the new technologies that have recently been incorporated into large industries. World leaders and experts are growing increasingly concerned about more direct risks, even as the general public frequently talks about the wider societal impacts, such as the dissemination of false information online and how AI could alter the availability of jobs.
US intelligence officials have warned that government legislation may not keep up with the recent and rapid advances in artificial intelligence. Meanwhile, research funded by Microsoft indicates that 87% of UK-based firms are vulnerable to cyberattacks using AI. The threat landscape has changed as a result of this circumstance. What particular malevolent applications of general AI are experts worried about, though?
Gen AI’s Threats
The reality is a little more worrying for many enterprises, even if the general public may see generative AI as an exciting tool to develop content and streamline procedures. Malicious actors can—and frequently have—infiltrate formerly impregnable systems due to their capacity to create convincing fabrications and handle massive volumes of data on their own.
General AI models can scan and analyze entire corporate computer systems, finding and taking advantage of the most common attack vectors and flaws, when used maliciously. The main issue is that a lot of modern algorithms can accomplish these tasks quickly and accurately, which exposes users to serious security threats.
● Social engineering: Artificial intelligence (AI) models are being used more frequently to generate fictitious credentials, audio files, and video clips that deceive targets into disclosing personal information and passwords. Asking keyholders for access could be the easiest way to acquire access to high-risk systems, as studies show that only 73% of individuals can recognize AI-generated speech and that many are unable to accurately identify phony photos.
● Physical attacks: A significant number of physical systems are currently controlled by internet-connected devices, with up to 86% of industrial firms reportedly having implemented Industrial Internet of Things (IIoT) solutions in recent years. The FBI has alerted lawmakers to actual attempts to use gen AI models to compromise IIoT systems in order to take control of physical systems and destroy vital infrastructure. These attacks have demonstrated the ability of hackers to cause physical equipment damage in simulated scenarios, resulting in malfunctions, explosions, and fires.
● Data breaches – Recent statistics indicate that users enter personally identifiable information into generative AI tools in as many as 55% of data loss protection situations, a reflection of the public’s pervasive misuse of this technology. This risk increases significantly when hackers purposefully use brute-force assaults and social engineering techniques to get over established cybersecurity safeguards in order to access and expose sensitive data using general artificial intelligence (gen AI) models.
● Technology theft – It is a significant problem for numerous organizations, as there is a possibility that their own artificial intelligence models may be hacked and used against them. This is a hazard that has been realized recently due to internal system breaches. The acquisition of AI models created by national intelligence agencies by malevolent actors would greatly enhance their capabilities and present a serious risk to national security.
Defending Against Threats From Gen AI
As cyber-physical attacks become more frequent and a predictable hazard for most major industry firms, the focus needs to shift to intelligent defensive systems and methods. In order to effectively safeguard critical systems from advanced threats, stakeholders need to evaluate current physical and cyber security solutions while considering AI-powered approaches.
In order to mitigate the risk that artificial intelligence (AI) poses to an organization’s operations in the years to come, leaders and security teams need to figure out dependable ways to make the most of this technology. 53% of firms, according to research released in 2023, see the connection between cybersecurity concerns and gen AI, but just 38% are thought to be actively addressing these dangers. So what is a defensive application for generative AI?
Threat Identification and Response
Because generation AI models are able to adapt to changes in threat signifiers and continuously analyze fresh data, businesses can proactively use these solutions to defend core systems from sophisticated attacks. Gen AI systems can automatically analyze past data to find unusual behaviors that can indicate new dangers. Then, in order to consistently handle changing threats in real time, systems can quickly modify critical operating parameters.
Organizations can guarantee that prompt action is taken to contain and neutralize threats by setting up Gen AI models to continuously monitor network activity for indications of anomalous behavior. The idea is especially crucial for businesses seeking a certain level of physical and cybersecurity convergence, since it allows for the containment of attacks before hackers can get past security measures connected to IIoT installations and physical security systems.
Generation of Vulnerability Patches
Malicious actors frequently use Gen AI models that analyze an organization’s internal systems in order to find weaknesses that can be exploited. By utilizing their own generation AI capabilities, teams may proactively protect against this kind of attack. It is possible to configure these systems to produce suitable virtual patches on their own for recently discovered vulnerabilities.
To test patches in controlled conditions, models can leverage both external and internal datasets. This guarantees that optimization and application of repairs can occur autonomously, without disrupting vital activities, jeopardizing physical systems, or resulting in needless downtime.
Enhanced Credential Security
Businesses can use gen AI models to create fake credentials for testing and optimization. For instance, they can use AI-generated biometric data—such as fingerprint patterns and facial recognition—to train internal systems to recognize forged credentials. This same idea can be used for text-based strategies, assisting managers in teaching staff members how to recognize the telltale signs of AI-generated social engineering tactics.
For many years, governments and businesses around the world have been increasingly concerned about cyberattacks. However, as generative AI continues to improve, it is anticipated that these dangers will only get more complex. Since 90% of worldwide enterprises think that cyberattacks constitute a threat to physical security solutions, this risk is only made worse by the growing prevalence of merged physical and cybersecurity systems.
Teams need to be ready to take advantage of these technologies in order for leaders and security people to consistently reduce the harmful effects of general artificial intelligence (gen AI) on the cyber-physical threat landscape. By utilizing the ability of general artificial intelligence (AI) to continually monitor, address, and take action against cyber threat behavior, organizations can dependable defend important assets against sophisticated attacks.
