AI and Cyber Security: The Double-Edged Sword

Sagiss, LLC : Published: May 2, 2024       Updated: June 1, 2024

We asked our team what technologies that are emerging or will emerge in the next few years will have the biggest impact on small businesses.  Lee Borger, one of our account managers, said he’s most intrigued by AI, specifically its impact on cybersecurity.

In this article we will explore his thoughts on AI in cybersecurity, where it can help and where the dangers and risks are. 

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AI is heralded as a revolutionary force in many sectors, and its potential to enhance our digital experiences and productivity is immense. However, like many powerful tools, AI also presents substantial cybersecurity challenges that could potentially escalate the threats we face online.

Impact of generative AI tools in cybersecurity

Generative AI tools were introduced in security just recently with Copilot and other tools like GPT-3 and DALL-E. See the security announcement. As security developers develop generative artificial intelligence in security products, the need for responsible development will be critical. New technology should be regulated in the best interest of privacy and the system should be secure. Accuracy is known to plague current genotypes, and the advancement of the technology can help organizations keep pace with the cyber threat from AI.

Using AI to detect cyber security attacks

Considering AI's ability to detect patterns, cybersecurity anomalies are a clear use case. AI can also identify vulnerabilities by analyzing logs, predicting threats, and reading source code. Behavior anomalous detection provides another example. Machine learning allows a model to determine the normal behavior in a system by identifying cases that do not conform to that behavior. AI enhances threat detection capabilities by leveraging machine learning and deep learning for automated and efficient identification of cybersecurity threats. These can help identify a potential attack, help identify system failure, and identify outlier behaviors in the behavior. Even user behavior that is deemed to be problematic can be identified using machine learning or pattern recognition techniques.

How does AI for cybersecurity work?

The AI technology is designed to analyze large amounts of data across various computer systems and sources to identify patterns of activity within organizations. It is capable of identifying anomalies in occurrences that may warrant investigating if they are found in any case. Ensuring data security is crucial, as AI systems must protect the data they utilize from both technical and organizational threats. A company can never use a user’s data for other companies’ outputs unless they are in compliance. Instead, AI utilizes global threat intelligence derived from numerous organizations. AI algorithms are used to continuously understand the data from evaluating it.

AI-assisted cyber threat intelligence

A security monitoring system can offer real-time alerts and can also improve security in the event an emergency occurs by providing actionable insights for security analysts to further investigate and mitigate cyberattacks. The Cyber Threat Intelligence (CTI) helps to collect data on cyber security incidents. The integration of AI into security tools enhances threat intelligence and visibility across the digital estate. The CTE aims to inform its members about threats to your organization and the intent is to actively prepare team members for possible attacks in your organization. CTI also assists incident response teams with better knowledge of their problem.

Using AI to prevent vulnerabilities

Using AI/Melo for cyber security is important, but the protection against malware is equally crucial. AI assistant systems for pipeline development and test systems become increasingly prevalent in many areas, helping to maintain network security. As with CTI, AI systems can reduce mundane duties by giving people more time for better quality work. AI can also identify vulnerabilities in code reviews, enhancing the overall security posture. Using SAST (static application vulnerability testing) can improve code reviews. The platform SAST exists for some time but its main problem are the numerous false positive results it produces.

Applying AI to cybersecurity

The technology of artificial intelligence has the best possible capability and Cybernetics certainly fits in that category. Machine learning and AI are now used as tools to automate threat detection, assisting security professionals in identifying and mitigating risks faster than traditional software-based methods. AI empowers security teams to respond faster to threats, enabling them to detect hidden patterns and prioritize risks effectively. Nonetheless, cybersecurity poses unique challenges: Self-learning cybersecurity posture management software can solve many of these challenges. Various technologies exist which allow the self training of systems that collect information in the context of your business information systems.

How can leaders help ensure that AI is developed securely?

The Guidelines for Secure AI Systems Development published by the NSA were developed jointly by the CIA and help with the design, development and operation of AI systems. These guidelines help organizations deliver secure results by creating an exhaustive step-by-step guide for developers. Cybersecurity teams play a crucial role in ensuring secure AI development by identifying and responding to threats, automating security tasks, and fostering a culture of continuous learning and professional development. As part of an assessment of security, the stakeholders of an organization are prepared to respond to system failures in order to effectively limit impacts.

AI security use cases

Instead of replacing security experts, AI can assist the security team in their tasks, making a difference in the efficiency of their job. A common application of AI in cybersecurity is:

Integrating AI with human intelligence can lead to better security outcomes by combining the strengths of both.

AI and the Automation of Malicious Code Creation

One of the most significant benefits of AI is its ability to generate complex code based on a simple description of the desired outcome. This capability, though remarkable, is a double-edged sword. While it can dramatically accelerate development and innovation, it also lowers the barrier for bad actors who wish to create malicious software. 

Previously, crafting sophisticated malware required advanced programming skills and deep technical knowledge. Now, with AI platforms, individuals with minimal coding expertise can potentially create harmful programs just by describing what they want the software to do. This shift could lead to an increase in the number and complexity of cyber threats.

The Rise of AI-Powered Phishing Attacks

Another pressing concern is the use of AI in phishing operations. Phishing, the practice of sending fraudulent communications that appear to come from reputable sources, has long been a major threat in cybersecurity. Historically, one of the tell-tale signs of a phishing email has been poor grammar and spelling, which often alerted vigilant users to the email’s malicious nature.

However, with advancements in AI, particularly in large language models, this red flag is no longer reliable. AI can now generate grammatically flawless phishing emails that mimic the style and tone of legitimate communications from trusted sources. 

For instance, a hacker in Russia, without any knowledge of the English language, could use AI to craft a perfect phishing email in English. This not only makes the fraudulent emails more difficult to detect but also enhances their effectiveness, thereby increasing the risk of successful attacks.

Furthermore, AI’s capability to analyze and replicate an individual’s writing style can lead to more sophisticated targeted attacks, such as spear-phishing. Imagine AI systems that can analyze the communication style of a company’s CEO and then use that model to craft an email to a subordinate, requesting sensitive information or financial transfers. These scenarios are not just hypothetical; they represent a real and present danger.

Staying Ahead: The Need for Adaptive Cybersecurity Strategies

Historically, the adage that “when a new technology emerges, the bad guys are often the first to exploit it” holds true. AI seems to be no exception. Cybercriminals are notoriously adaptive and innovative, frequently outpacing the defensive capabilities of those tasked with protecting digital assets. The dynamic nature of AI development means that cybersecurity professionals must be perpetually on their toes, adapting to both the advancements and the new vulnerabilities introduced by AI technologies.

To counteract these threats, there is a crucial need for adaptive and proactive cybersecurity strategies that can empower security teams to evolve at the pace of AI development. User education remains a critical line of defense. Increasing awareness about the capabilities of AI-powered attacks can help individuals and organizations stay vigilant against more sophisticated phishing attempts.

Moreover, the cybersecurity community must invest in AI-driven security solutions that can anticipate and mitigate AI-driven threats. Leveraging AI to enhance threat detection capabilities is essential for identifying and responding to cybersecurity threats efficiently. Leveraging AI to fight AI may seem paradoxical, but it could be the key to developing more resilient information security infrastructures.