Despite the security concerns surrounding
Posted: Thu Feb 06, 2025 3:47 am
“These models are very expensive to train, maintain, and perform forensic analysis on, so they bring a lot of uncertainty,” says Joe Regensburger, vice president of research at Immuta. “We don’t know what kind of impact they’ll have or how big of an impact they’ll have.”
GenAI, 85% of data scientists surveyed by Immuta believe they can overcome any challenges associated with using the technology. Moreover, two-thirds of respondents are confident in their ability to maintain data privacy in the age of AI.
“In the era of cloud computing and AI, data security and governance issues are becoming increasingly complex,” says Sanjeev Mohan, director of SanjMo. “It’s simply not possible to use legacy approaches to manage data security across hundreds of information products.”
Three Main Ethical Issues of AI. Source: Immuta
Three Main Ethical Issues of AI. Source: Immuta
According to the survey, while GenAI increases risks, data scientists are turning to GenAI to provide new tools and methods to automate their privacy and security work.
Specifically, 13% of cameroon mobile database hope AI will help with phishing detection and security awareness training, 12% expect AI to help with incident response, and 10% say it can help with threat modeling and red teaming to simulate real-world attacks. Data augmentation and obfuscation, auditing and reporting, and streamlining security operations center (SOC) collaboration and operations are also potential areas for AI application.
“AI and machine learning can automate processes and quickly analyze massive amounts of data to improve threat detection, as well as use advanced encryption techniques to protect data,” says Matt Diantonio, vice president of product management at Immuta.
Ultimately, he said, it’s clear that advances in AI are changing the nature of data security and privacy work. Companies must work to stay on top of the rapidly evolving nature of threats and opportunities.
“As organizations advance in AI, it is critical to mitigate data risks to prevent sensitive data from being used inadvertently or maliciously in AI models,” says Diantonio. “To do this, it is critical to implement a robust security and governance strategy for AI-generated data streams and inferences.”
GenAI, 85% of data scientists surveyed by Immuta believe they can overcome any challenges associated with using the technology. Moreover, two-thirds of respondents are confident in their ability to maintain data privacy in the age of AI.
“In the era of cloud computing and AI, data security and governance issues are becoming increasingly complex,” says Sanjeev Mohan, director of SanjMo. “It’s simply not possible to use legacy approaches to manage data security across hundreds of information products.”
Three Main Ethical Issues of AI. Source: Immuta
Three Main Ethical Issues of AI. Source: Immuta
According to the survey, while GenAI increases risks, data scientists are turning to GenAI to provide new tools and methods to automate their privacy and security work.
Specifically, 13% of cameroon mobile database hope AI will help with phishing detection and security awareness training, 12% expect AI to help with incident response, and 10% say it can help with threat modeling and red teaming to simulate real-world attacks. Data augmentation and obfuscation, auditing and reporting, and streamlining security operations center (SOC) collaboration and operations are also potential areas for AI application.
“AI and machine learning can automate processes and quickly analyze massive amounts of data to improve threat detection, as well as use advanced encryption techniques to protect data,” says Matt Diantonio, vice president of product management at Immuta.
Ultimately, he said, it’s clear that advances in AI are changing the nature of data security and privacy work. Companies must work to stay on top of the rapidly evolving nature of threats and opportunities.
“As organizations advance in AI, it is critical to mitigate data risks to prevent sensitive data from being used inadvertently or maliciously in AI models,” says Diantonio. “To do this, it is critical to implement a robust security and governance strategy for AI-generated data streams and inferences.”