AI in Cybersecurity: Risks and Vulnerabilities

AI in cybersecurity is a double-edged sword. While AI reinforces our cyber defense, it has vulnerabilities and may even be offensive. Learn more about AI-related security issues.

Nowadays, AI technologies are harnessing across almost all industries. AI can generate computer code, streamline business processes, and boost cyber defense capabilities. Specifically, AI in cybersecurity streamlines labor-intense manual tasks such as scanning massive volumes of data and detecting potential threats. AI can also analyze user and application behavior for malware and attack indicators on a protected system. However, there is a double-edged sword: while AI reinforces our working capability and cyber defense, it still has vulnerabilities and may be offensive. According to a recent survey conducted by Salesforce, 71% of 500 senior IT leaders believe that generative AI is likely to bring forth fresh security data risks. Such a large percentage of AI skeptics should be a wake-up call for information security specialists and AI developers.

AI vulnerabilities refer to weaknesses or risks associated with the deployment and use of AI systems. The offensive AI, in turn, involves leveraging AI capabilities to conduct cyber attacks, exploit vulnerabilities, or develop sophisticated offensive tools.

Continue reading to learn about both, the vulnerabilities and offenses of the AI.

A Double-Edged Sword: Vulnerabilities of AI in Cybersecurity

AI could suffer a data breach

AI could be breached itself! Let’s just mention the ChatGTP data breach caused by a bug in an open-source library soon after it was released. Officials from OpenAI, ChatGPT’s parent company, said the user’s first and last name, email address, payment address, credit card numbers, and credit card expiration date were disclosed. After the incident, the company was handling the aftermath by notifying impacted users, confirming their emails, and adding additional security measures.

AI could be misused

Intentionally or unintentionally, your employees may upload sensitive data or code to an AI system, which may end up in criminals’ hands. Not long ago, Samsung banned employees from using generative AI tools like ChatGPT after discovering staff uploaded sensitive code to the platform. The company was concerned that data sent to artificial intelligence platforms could be disclosed to other users. Samsung is not the only company restricting the use of AI. U.S. investment bank JPMorgan also restricted the use of ChatGPT for its employees earlier this year. And Amazon has also warned employees not to upload confidential information, including code, to ChatGPT.

AI could be offensive

Finally, AI technologies can help scale cyberattacks via various forms of probing and automation. For instance, AI-powered tools can automate different stages of an attack, such as reconnaissance, vulnerability scanning, and exploitation. By automating these processes, attackers can target a larger number of systems and carry out attacks more rapidly. This is one of the reasons why companies are adopting a “zero trust” approach, where defenses continuously examine and scrutinize network traffic and applications to ensure they are not malicious. We have already written about zero trust in one of our blogs Building a Suuccesul Zeto-Trust Strategy.

The cases above not only diminish trust in AI’s reliability but also rises multiple information security concerns. Let’s figure out the approaches to offensive AI and how it affects information security.

Approaches to Offensive AI in Cybersecurity

Offensive AI is widely used for powering and scaling cyberattacks. Microsoft Chief Scientific Officer Eric Horvitz described a few approaches to offensive AI in his statement.

Basic automation

Just like defenders utilize AI to streamline their processes, adversaries can also leverage AI to enhance their advantages. The automation of attacks is nothing new in cybersecurity. Many malware and ransomware variations have employed simple logic to identify and adjust to different operating environments over the past five years. For instance, attacking software has checked time zones to align with local working hours to evade detection or carry out specific actions tailored to the target system. Additionally, automated bots have propagated across social media platforms. These early instances represent elementary forms of AI that encode and utilize the attacker’s specialized knowledge. However, significant advancements in AI technology have made it plausible for malicious software to become more adaptable, covert, and intrusive.

Authentication-based attacks

AI in cybersecurity can be utilized in authentication-based attacks, such as creating synthetic voiceprints to bypass an authentication system. Horvitz noted convincing demonstrations of voice impersonations aimed at tricking authentication systems were showcased during the Capture the Flag (CTF) cybersecurity competition held at the 2018 DEF CON conference. These demonstrations highlighted the potential risks posed by AI-powered techniques in undermining authentication systems.

AI-powered social engineering

Humans are the weakest cyber security links. We already wrote about it in our blog post Reinforcing the Weakest Cybersecurity Link with Access Controls. Criminals can use AI to exploit this persistent vulnerability. For instance, criminals can utilize AI methods to generate highly personalized phishing attacks that are capable of deceiving even the most security-conscious individuals. The AI tool can learn from publicly available data such as online profiles, connections, post content, and online activities of targeted individuals thereby optimizing the timing and content of messages to maximize clickthrough rates. The large-scale neural language models can even automatically craft emails with remarkable success rates surpassing human-written messages.

Prevention and Mitigation Measures to Offensive AI in Cybersecurity

Given the complicated cybersecurity landscape and AI’s important place in it, IT specialists must pay specific attention to AI-powered cyber offensives. AI developers must prioritize AI systems with strong security measures by integrating robust safeguards like data encryption, secure access controls, and continuous threat monitoring. By designing AI systems with security as a primary consideration, organizations can decrease the chances of cyber-attacks and safeguard sensitive information. Regular security assessments and testing can help identify vulnerabilities and ensure that the safeguards remain effective against evolving threats. Ultimately, emphasizing the development of secure AI systems ensures that the advantages of this technology are fully realized without compromising safety or privacy. Some of the other measures for preventing and mitigating AI-powered information security incidents include:

Implementing Strong Access Controls: AI systems should be protected by stringent access controls to prevent unauthorized entry. This includes multi-factor authentication, strong passwords, and role-based access controls.
Adversarial Training: Adversarial training involves deliberately exposing AI systems to malicious inputs to help them learn how to detect and respond to adversarial attacks. This approach prevents attackers from circumventing the system’s security measures.
Regular Auditing: Regularly auditing AI systems help identify potential vulnerabilities and areas that need improvement. This can include code reviews, penetration testing, and vulnerability assessments.
Transparent Decision-Making: As AI systems become more complex, understanding their decision-making process can be challenging. Prioritizing transparency in AI systems by establishing clear decision-making processes that can be audited and reviewed is important.

Conclusion

AI technologies have become ubiquitous across industries. However, some challenges related to AI exist. While it boosts productivity and defense capabilities, it also presents vulnerabilities and offensive potential. Data breaches, misuse of AI systems, and offensive AI attacks are potential challenges that must be addressed. Implementing robust security measures, conducting regular audits, and promoting transparent decision-making can help mitigate these risks and ensure the safe and effective use of AI technology.

Discover more exciting topics with our blog, and feel free to contact the Planet 9 team if you have any questions. We’ll be happy to assist!

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