Generative AI and Blockchain Exploitation Among Trend Micro’s Top 5 Cybersecurity Challenges in 2024
Trend Micro identified the top five cybersecurity challenges organizations’ cyber defenses will face in 2024 that need attention.
American cybersecurity company Trend Micro identified five key security threat trends for 2024, including cloud-native worms, data poisoning of ML/AI systems, supply chain attacks, generative AI-boosted social engineering scams and blockchain exploitation.
According to the cybersecurity company, the rapid progress in generative AI has opened doors to novel cyber threats. Ransomware continues its upward trajectory, with cybercriminals increasingly targeting data exfiltration. Simultaneously, supply chains are facing heightened risks from malicious actors.
For instance, British cybersecurity solutions provider Sophos unveiled two reports shedding light on the integration of AI in cybercrime. In the first report, titled “The Dark Side of AI: Large-Scale Scam Campaigns Made Possible by Generative AI,” Sophos reveals how scammers could exploit technology such as ChatGPT to perpetrate widespread fraud in the future, all while requiring minimal technical expertise.
There is no doubt that the potential for malicious actors to leverage generative AI for large-scale scams is at an all-time high. Trend Micro identified five cybersecurity challenges that organizations’ cyber defenses will face in 2024 and need attention.
Cloud-Native Worms Targeting Cloud Environments
Cloud-native worms targeting cloud environments leverage automation, a critical tool for cybercriminals, to propagate rapidly and wreak havoc within cloud systems. Given the inherent vulnerabilities and misconfigurations often present in cloud environments, these attacks are anticipated to escalate in frequency and severity. The interconnected nature of cloud infrastructure provides an ideal breeding ground for such worms to spread, making containment a challenging task for cybersecurity professionals.
Data Poisoning of Machine Learning and AI Systems
Data poisoning represents a potent threat as cybercriminals exploit training data vulnerabilities to compromise AI model integrity. This manipulation opens avenues for exfiltration, extortion, and sabotage, significantly amplifying the potential impact of cyber-attacks. By infiltrating and corrupting the training data, threat actors can subvert the functionality of ML and AI systems, posing severe risks to organizations reliant on these technologies for critical decision-making processes.
Supply Chain Attacks on CI/CD Systems
Supply chain attacks targeting Continuous Integration/Continuous Delivery (CI/CD) systems have emerged as an attractive strategy for cybercriminals due to their extensive access to a broad range of potential victims. By compromising software development supply chains, attackers can inject malicious code that undermines the entire IT infrastructure, affecting numerous interconnected parties.
Generative AI-Boosted Social Engineering Scams
Generative AI-boosted social engineering scams introduce a new dimension to traditional phishing schemes by leveraging advanced AI algorithms to create compelling fake messages. Crafted with remarkable authenticity, these messages increase the likelihood of successful social engineering attacks, such as business email compromise (BEC). As generative AI technology advances, the potential for producing sophisticated audio/video deepfakes further amplifies the threat landscape, posing challenges for cybersecurity professionals identifying and mitigating such attacks.
Blockchain exploitation, while not yet pervasive, presents a growing concern as private blockchains gain traction for managing internal financial transactions. Attackers may exploit vulnerabilities within these systems to gain unauthorized access and execute extortion schemes. As organizations increasingly adopt private blockchains for their purported security benefits, they must remain vigilant against potential vulnerabilities and implement robust security measures to safeguard sensitive financial data from exploitation by malicious actors.
With the increasing sophistication of cyberthreats, organizations should focus on protection at all points of the attack life cycle and implement multidimensional security built on good threat intelligence.
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