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It could help improve employees’ performance, capacity, and efficiency. Since the technology can synthesize hundreds of millions of data points simultaneously and summarize and classify them, it could be a powerful tool for improving the speed of work.
Generative AI could also help provide meaningful insights and predict outcomes in complex datasets, and then help explain those findings in plain language. Cybersecurity staff could use it to help analyze network activity logs, detect anomalies, and propose remediation.
Generative AI technology could help analyze data collected by drones, satellites, and other sensors for damage and deterioration to public infrastructure in order to help improve maintenance forecasts. Generative AI could help optimize software coding and explain unfamiliar code.
The technology could help promote environmental sustainability by optimizing workloads, as it maximizes energy efficiency, and promotes eco-friendly policies like going paper-free.
Public and private sector agencies recognize that generative AI is a powerful tool that will be affordable, ubiquitous and transformational—but not without risk. Today, generative AI produces outputs primarily based on the vast public data sets commercial solutions were trained on. But agencies that want to use the technology to analyze budgets, boost customer experience with personalized information, or query in-house knowledge bases will have to train their generative AI solutions using their own proprietary information. That raises a host of cybersecurity concerns.
Here are three effective strategies agencies can use to take advantage of generative AI while protecting their sensitive data:
1.Implement a comprehensive data classification strategy.
2. Set policies and educate employees on “shadow AI.”
3.Practice good generative AI governance.
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