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Artificial Intelligence (AI) in 2023 sounds a bit like déjà vu to me. Back in 2001, as I was entering the venture industry, I remember the typical VC response to a start-up pitch was, “Can’t Microsoft replicate your product with 20 people and a few months of effort?” , Given their resources? Today, any time a new company introduces its product that uses AI to do ‘X’, the VC industry asks, “Can’t ChatGPT do it?”
Twenty-two years later, Microsoft is at the table once again. This time they brought $13 billion to market by partnering with OpenAI and bringing new products like Security CoPilot to understand the threat landscape using the recently launched text-generating GPT-4 (more on that below). Are betting. But just as Microsoft didn’t disrupt the success of thousands of software start-ups in the early 2000s, I don’t expect Microsoft or any vendor to own this new AI-enabled market.
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However, the explosion of the market over the past few months and the hype around AI across the business and investment spectrum has led people to ask: what are we doing with all this? And more specifically, how do CIOs, CSOs, and cybersecurity teams deal with technology that can pose serious security and privacy risks?
the good, the bad and the scary
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I look at the good, the bad and the scary aspects of this recent Microsoft announcement. What’s incredible about ChatGPT and its progeny 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 required sophisticated, trained analysts to sort, analyze, and run processes for their security data. Knowledge of specialized query languages and configuration relevant to each product such as Splunk, Elastic, Palo Alto/Demisto and Curradar is required. It was a difficult task, and the available talent was never enough.
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This difficulty still exists today in SIEM (Security Information and Event Management) and SOAR (Security Orchestration, Automation and Response). SIEM helps enterprises collect and analyze security-related data from servers, applications, and network devices. Data is analyzed to identify potential security threats, alert security teams to suspicious activity, and provide insight into a company’s security protections. SIEM systems typically use advanced analytics to identify patterns, anomalies, and other indicators of potential threats.
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