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Generative AI & data: Potential in cybersecurity if the risks can be curtailed


Artificial intelligence (AI) in 2023 feels a bit like déjà vu to me. Back in 2001, as I was just entering the venture industry, I remember the typical VC reaction to a start-up pitch was, “Can’t Microsoft replicate your product with 20 people and a few months of effort, given the resources they have?” Today, any time a new company is pitching its product that uses AI to do ‘X,’ the VC industry asks, “Can’t ChatGPT do that?”

Twenty-two years later, Microsoft is at the table once again. This time they’re making a $13 billion bet by partnering with OpenAI and bringing to market new products like Security Copilot to make sense of the threat landscape using the recently launched text-generating GPT-4 (more on that below). But just as Microsoft did not inhibit the success of thousands of software start-ups in the early 2000s, I do not expect Microsoft or any vendor to own this new AI-enabled market. 

However, the market explosion and hype around AI across the business and investment spectrum over the past few months has led people to ask: what are we to make of it all? And more specifically, how do CIOs, CSOs, and cybersecurity teams learn to deal with technology that may pose serious security and privacy risks?

The good, the bad, and the scary

I look at the good, the bad, and the scary of this recent Microsoft announcement. What’s incredible about ChatGPT and its offspring is that it brings an accessible level of functionality to the masses. It’s versatile, easy to use, and usually produces solid results.

Traditionally, organizations have needed sophisticated, trained analysts to sort through, analyze, and run processes for their security data. This required knowledge of particular query languages and configurations relevant to each product, like Splunk, Elastic, Palo Alto/Demisto, and QRadar. It was a difficult task, and the available talent pool was never enough.   

That difficulty in SIEM (Security Information and Event Management) and SOAR (Security Orchestration, Automation, and Response) still exists today. SIEM helps enterprises collect and analyze security-related data from servers, applications, and network devices. The data is analyzed to identify potential security threats, alert security teams to suspicious activity, and provide insights into a company’s security defenses. SIEM systems typically use advanced analytics to identify patterns, anomalies, and other indicators of potential threats.


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