How AI can help secure the software supply chain [Q&A]

Securing the software supply chain presents many challenges. To make the process easier OX Security recently launched OX-GPT, a ChatGPT integration aimed specifically at improving software supply chain security.

We spoke to Neatsun Ziv, co-founder and CEO of OX Security, to discuss how AI can present developers with customized fix recommendations and cut and paste code fixes, allowing for quick remediation of critical security issues across the software supply chain.

BN: What's the most promising application of using AI for cybersecurity?

NZ: Generative AI has dramatically changed the cybersecurity landscape -- for both sides. Hackers have already begun using AI models to find and exploit vulnerabilities, develop malware and craft phishing emails.

For security teams, one of the most promising applications of AI in cybersecurity is in threat detection and response. AI-powered systems can analyze vast amounts of data in real-time, identify patterns, and detect anomalies that may indicate potential security breaches or malicious activities. This can help organizations respond more effectively to emerging threats, prevent attacks, and minimize the impact of security incidents. Additionally, AI can assist in automating routine tasks such as vulnerability scanning and patch management, freeing up human analysts to focus on more complex security challenges.

BN: Do you have concerns about using ChatGPT to improve application security?

NZ: While it's important to be aware of potential security risks and challenges associated with using AI models, it doesn't necessarily mean you should be overly concerned. AI models like ChatGPT undergo extensive testing and development to ensure their security and reliability. OpenAI, the organization behind ChatGPT, has taken steps to improve the safety and security of their models.

However, it's always wise to exercise caution and implement best practices when integrating AI models into your applications. This may include measures such as input validation, output filtering, and monitoring for any potential issues or vulnerabilities. Keeping up with security updates and advancements in AI technology can also help you stay informed and proactive in addressing security concerns.

Ultimately, the decision to use AI models in your application should consider the potential benefits and risks while aligning with your specific security requirements and objectives.

BN: What's the most problematic or dangerous applications in which you see malicious actors using LLMs?

NZ: One of the most problematic and dangerous applications where malicious actors can potentially exploit large language models (LLMs) is in generating convincing fake content, such as deepfakes or misinformation. LLMs have the ability to generate highly realistic text, which can be used to create fake news articles, phishing emails, or social media posts that are difficult to distinguish from legitimate ones. This poses significant risks to individuals, organizations, and society as a whole, as false information can be rapidly spread, leading to misinformation campaigns, reputational damage, or even manipulation of public opinion.

Additionally, LLMs can be utilized to automate and scale malicious activities, such as spamming, phishing attacks, or social engineering attempts. Malicious actors can leverage the language generation capabilities of LLMs to craft tailored and convincing messages to deceive unsuspecting individuals. Research has indicated that AI-generated phishing emails have higher open rates than manually crafted phishing emails, which makes sense given that AI is capable of processing larger datasets more rapidly than humans.

Addressing these challenges requires a combination of technological advancements, user awareness, and responsible deployment of LLMs. Ongoing research and development aim to enhance model transparency, develop techniques to detect and mitigate the impact of fake content, and establish guidelines for responsible use of LLMs to mitigate potential harm.

BN: Do you think that usage of AI in cybersecurity will be able to stay ahead of malicious actors' usage of it?

NZ: Yes, the usage of AI can significantly help organizations stay ahead of malicious actors in the ever-evolving landscape of cybersecurity. AI technologies can be leveraged to enhance threat detection, response, and prevention capabilities. Here are a few ways AI can contribute:

• Threat Intelligence: AI can analyze vast amounts of data from various sources, including security logs, network traffic, and threat feeds, to identify patterns and trends. This enables organizations to proactively identify emerging threats and vulnerabilities.
• Anomaly Detection: AI-powered systems can continuously monitor network behavior, user activity, and system logs to detect unusual or suspicious patterns that may indicate a security breach. This helps in early detection and response to potential threats.
• Automated Response: AI can automate certain security processes and responses, such as blocking or quarantining suspicious network traffic, mitigating distributed denial of service (DDoS) attacks, or isolating compromised systems. This allows organizations to respond swiftly and effectively to mitigate risks.
• User Behavior Analytics: AI can analyze user behavior patterns to establish baselines and detect anomalies that may indicate insider threats or unauthorized access attempts. This helps in identifying potential internal risks and preventing unauthorized activities.
• Adaptive Security: AI systems can learn from past incidents and adapt security measures accordingly. They can analyze attack patterns, identify vulnerabilities, and recommend or implement security patches or configurations to strengthen defenses.

While AI can be a valuable tool in combating cyber threats, it's important to note that security is a constant cat-and-mouse game. Malicious actors also evolve their techniques, and AI models themselves can be targeted. Hence, a multi-layered security approach that combines AI with human expertise and ongoing vigilance is crucial for staying ahead of malicious actors.

BN: What made you decide to pursue developing OX-GPT?

NZ: One of the biggest challenges in application security is developer adoption. Among other benefits, integrating GenAI into our platform allows us to create the best possible user experience. This means developer adoption, which ultimately means better security.

Until now, security teams often needed a lot of tools to secure their applications, resulting in too many alerts and fragmented workflows. Flooded by alerts, developers often become frustrated and overwhelmed. After wasting enough time chasing false positives, they lose trust in these tools and no longer see the value in the activities that security asks them to perform.

In order to be effective, application security needs to be user-centric. OX-GPT provides a best in class, developer-first experience. Developers receive context for the specific issues they are facing, including how the code in question could be exploited by hackers, the possible impact of such an attack and potential damage to the organization. It provides cut and paste code, crafted to secure and fix a specific issue, along with an explanation of why the fix works. Users remain in control of their code, with faster and easier identification and remediation of risks.

In the future, we will be able to provide bespoke messaging for each developer -- optimized for time to remediation.

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