Generative AI: Safety, Security, and Exploitations

Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise software categories. Philipp Pointner of Jumio explores the ins and outs of Generative AI– from safety, to enterprise security, to exploitation.

The use of generative AI has propelled technological advancements, revolutionizing tasks like research, report writing, and image creation with accelerated speed and efficiency. While these AI-driven automation tools offer substantial benefits for businesses, they simultaneously open the door to a new wave of fraudulent activities and disinformation. As users are urged to enhance their online skepticism and education of human-centric attack methods, relying on all users to become savvy enough to stop fraud and disinformation isn’t enough. It’s up to businesses to take the lead in this evolving cybersecurity battle to bolster the safety and security of themselves and their customers.

In the face of increasingly complex and AI-driven identity fraud tactics, businesses must prioritize vigilance and implement comprehensive strategies to protect their operations and their customers’ identities.

Let’s explore the common AI-powered attack methods, and the modern strategies, such as robust identity verification, liveness detection, and biometric authentication, that are ensuring the safety and security of businesses and their users.

Generative AI: Safety, Security, and Exploitations

Evolving Generative-AI Attack Methods

Generative AI frameworks have ushered in a new era of sophisticated fraud tactics, empowering scammers to bypass cybersecurity measures and run convincing scams. A prime example is the evolution of phishing emails — once characterized by obvious typos and poor grammar, they now pose a greater threat as generative AI enables fraudsters to craft sophisticated, professional-looking emails that are difficult to identify as fraud. This new level of sophistication includes the use of AI-powered chatbots like ChatGPT to craft realistic messages. In fact, there is now a new product being sold on the dark web called, FraudGPT, which is an LLM trained for the purpose of fraud and scamming. This product does not have the same limitations and filters as ChatGPT, making it especially powerful for attackers, and increasingly risky for organizations and their vulnerable users.

Social engineering tactics have proven quite effective for attackers, with 74 percent of attacks involving the human element in 2023. However, they’re burdensome for businesses, as the average social engineering breach is estimated to cost $4.1 million and can take up to 270 days to identify. With 90 percent of cyber-attacks currently targeting employees, organizations must level up their defense strategies to meet the advancing techniques employed by malicious actors.

Synthetic identity fraud, also referred to as Frankenstein fraud, has also been a consistent challenge for industries, including finance, and AI is accelerating the issue. This technique is a rapidly growing form of identity theft, combining real and fictitious information to create deceptive personas. Criminals typically stitch together a false name, a real date of birth, and a stolen Social Security number or other fraudulent details, making detection more challenging. This type of information can be AI-generated, found in data breaches or purchases from the dark web. Once they attain the necessary information, cyber-criminals construct synthetic identities, complete with fake names and addresses.

Synthetic identity fraud facilitates the practice of bust-out fraud, where fraudsters establish good credit and credit lines before exploiting them to the maximum and disappearing. Detecting these perpetrators proves exceptionally difficult, as the trail often leads only to the individual whose Social Security number was stolen. Deepfakes are another increasingly evolving tactic. These encompass the use of smart technology to make fake videos or images that look realistic — often leveraging AI-generated or real human faces. One study found that 52 percent of people worldwide feel confident in their ability to spot a deepfake video, which reflects over-confidence on the part of consumers, given the reality that deepfakes have reached a level of sophistication that prevents detection by the naked eye.

Additionally, there’s a new twist in hackers’ deepfake tactics. It’s an advanced technique where cyber-criminals blend videos or live streams, placing another individual’s face on the original in real-time. In China, an individual used a face-swapping method during a video call, pretending to be the victim’s friend and tricking them into a $622,000 money transfer. In the corporate world, high-level executives are particularly vulnerable to this type of fraud, as business leaders are now falling victim to tactics like voice cloning. In fact, Zscaler’s CEO recently fell victim to this style of attack, as hackers cloned his voice in an attempt to run scams within the organization.

Who Takes the Blame for Identity Fraud Attacks?

With attack methods rapidly accelerating, businesses must level up their defense postures to spot and defend against advanced cybersecurity threats, especially given that identity fraud presents significant challenges for enterprises. Swift reporting by individuals limits their own financial liability, but the primary responsibility for these losses falls upon organizations.

The fallout from deceptive identity practices results in an erosion of trust in businesses. This loss of trust is particularly pronounced in the financial sector. For instance, in 2019, a major financial institution experienced a cyber-attack impacting over 100 million customer accounts and resulting in a noticeable decline in the institution’s stock value. Consequently, customers tend to migrate to perceived safer alternatives in response to these types of incidents.

Fighting Fire with Fire

In the current digital landscape, establishing and preserving trust in user identities is crucial for organizational safety and operational efficiency. To bolster their defenses, security leaders must consider fighting AI with AI.

AI-enhanced identity verification plays a pivotal role in strengthening security for businesses and users alike. Through modern AI-focused strategies and solutions, security leaders are equipped with systems such as fraud analytics technology, leveraging predictive analytics to analyze data across diverse customer ecosystems. This approach proves instrumental in identifying and thwarting certain sophisticated fraud patterns, including coordinated attacks.

Biometric authentication is also a critical component of deterring identity theft, introducing an additional layer of security that outpaces conventional methods. Advanced liveness detection — algorithms that use neural networks to help defend against fraudsters, identity theft, and spoofing attempts to increase fraud prevention — acts as a robust deterrent against fraudsters attempting to deploy deceptive representations. Face-based biometric security, encompassing facial recognition and other distinctive traits, ensures a heightened level of identity assurance, contributing to a more secure and user-friendly experience.

Solutions in today’s market also include adaptive learning capabilities, which enable continuous refinement in authentication accuracy over time, learning from each user interaction. This is critical, as generative AI-powered attacks evolve each day. They also facilitate user-friendly experiences across devices and compliance with regulatory frameworks within industries such as finance.

Automation of the identity verification process not only combats fraud but also ensures adherence to privacy and industry regulations.

Enhancing The Future of Digital Identity Security

As the sophistication of AI-powered identity fraud tactics continues to escalate, businesses must adopt modern, comprehensive strategies to safeguard their operations and customers’ identities. These strategies include deploying solutions such as biometric authentication, advanced liveness detection, and adaptive learning capabilities, as these tools bolster the safety and security of businesses and their users in today’s continuously evolving digital landscape.