Ozempic's increasing popularity often raises the question - Is it possible for a class of drugs (like GLP-1s) to solve our obesity epidemic in the US? This medication has been all over the news and on social media lately for its revolutionary benefits to help support weight loss and mitigate health issues for those struggling with diabetes. It has also been marketed and promoted as a possible cure-all for the obesity epidemic in the US. 

Roughly around the same time, the buzz around Generative AI has picked up steam, primarily driven by OpenAI and its use of large language models (LLMs). One of the use cases I frequently encounter is the applicability of GenAI for cybersecurity (operations, risk/compliance, etc). The thought process is something along these lines:

• Our teams are over-stretched, and the attackers are growing in number and sophistication
• Our attack surface has increased via digitization and interconnectedness of our businesses
• Some of our work has been commoditized, so I can outsource, but I also require intelligent decision-making for threats, risks, and activities unique to our business. 

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Proponents of GenAI talk about all the valuable benefits to organizations, similar to many of the improved health benefits that Ozpemic has provided to individuals who struggle with insulin resistance and diabetes. However, as with Ozempic and the GLP-1s class of drugs, challenges and risks can be associated with using GenAI for your cybersecurity needs.

Ozempic Hype: What's the deal?

When it was initially introduced to the pharmaceutical market, Ozempic was created to support patients who struggle with Type 2 diabetes and insulin resistance. It is designed to help lower blood sugar levels and combat insulin resistance challenges. As a side effect, it can promote weight loss by reducing appetite and slowing food movement through the stomach.

Ozempic has provided many benefits to patients prescribed to help with weight loss and blood sugar levels. However, it’s gained popularity over the past year for its significant off-label use as a weight loss Aid. It has also been endorsed by celebrities, influencers, and other public figures for its efficacy in weight loss.  The effectiveness of the medication in promoting weight loss has led to a surge in demand. This demand has increased the price of GLP drugs and led to shortages. As a result, this has created challenges for patients who rely on it for diabetes management, given its off-label use for weight loss has strained medication supply chains. There are also many side effects (Note: I’m not a doctor, nor do I play one on TV).

What is the buzz about GenAI?

GenAI and Machine Learning are not innately new technologies. These innovations have a longer history than the buzz surrounding it may lead you to believe. The release of OpenAI’s ChatGPT in November of 2022 has generated buzz surrounding these technologies and their capabilities. It has pioneered rapid advancements, diverse applicability, and many new startups touting their use of GenAI to disrupt countless industries.
GenAI promises to revolutionize many industries that are less prone to change and are typically mired in complexity and red tape.  For example, GenAI can help with diagnostics, drug discovery, and patient treatment planning in healthcare. It uses GenAI algorithms to analyze medical images and more accurately detect diseases like cancer, understand the unique impact on a patient's genetic profile, and chart a course for recovery and rehabilitation. 

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Potential Benefits of GenAI in Cybersecurity

1. GenAI enhances automation and efficiency: In its most basic form, GenAI can automate repetitive tasks for teams, allowing organizations to reprioritize their focus on more complex and innovative activities. Eventually, a GenAI solution should be able to take on more complicated tasks, which can lead to increased productivity and cost reductions for businesses globally. 

2. Improving data & risk insights: GenAI has proven capable of analyzing large amounts of data rapidly and accurately. It can support teams to uncover patterns, trends, and insights within their data sets that may have previously proven challenging. GenAI's support can provide security and identity teams with the ability to identify anomalies and potential threats with better efficacy than traditional methods may have previously missed. This helps drive more informed decision-making for leadership and their teams. 

3. Advancing cybersecurity initiatives: A widespread use case for GenAI in cybersecurity has been its ability to support detection and response activities by learning from past events and adapting to emerging threats. For example,  identifying insider threats by analyzing user behavior patterns and implementing monitoring controls for suspicious activity based on user type, persona, location etc. 

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Challenges and Risks to GenAI in Cybersecurity

1. Integration and compatibility misalignment: Adopting GenAI within cybersecurity requires organizations to integrate more complex systems that process large amounts of data into their infrastructure. This can be challenging to deploy and integrate within infrastructures that still utilize legacy technologies that GenAI models are not designed to work with effectively. This can create challenges for organizations wanting to align their internal processes and infrastructure to use many GenAI technologies on the market today.

2. Data quality and bias concerns: Many GenAI models primarily rely on large datasets for their functionality. If the data the model is presented with is inaccurate, biased, or incomplete, this can produce incorrect or misleading results. Further, this can lead to more false positives and unreliable security projections in the threat detection capabilities of security teams. Finally, overreliance on GenAI can also lead to oversight of traditional security measures and other critical aspects of cybersecurity. 

3. Security vulnerabilities and risks: A good number of GenAI (primarily LLMs) have source code that is publicly accessible. This allows cybercriminals to exploit vulnerabilities more successfully. Further, malicious attackers can leverage GenAI products to enhance their attack methods by creating more sophisticated GenAI-powered malware, deep fakes, phishing, and social engineering attacks. We are still early in identifying vulnerabilities and risks associated with using GenAI solutions and GenAI solutions being used against us.

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Is GenAI the Ultimate Solution to Cybersecurity?

Integrating GenAI, LLM, and ML models within your cybersecurity processes can help reduce workloads and costs for your security teams. Despite its numerous advantages, the jury is still out on the potential risk vs. reward of GenAI usage. As organizations increase the adoption of GenAI while considering all the guardrails needed, there is also the human aspect to consider. Does this help us or hurt us in the long run? The fundamental question of humans becoming lazy, dependent, and unproductive is another topic for another blog post.

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Ozempic and GenAI are not magic bullets or cure-alls for a healthy and productive society. We need foundations - good exercise and diet for people,  governance, and knowledge sharing for systems.  Does GenAI provide exponential benefits for every single problem? Probably not. In the same way, not everyone needs to be on Ozempic to solve their health issues. There are many shades of grey, but I am confident that well-informed consumers will make well-informed decisions and not buy into the hype.