The fourth industrial revolution is here, and experts anticipate organizations will continue to embrace artificial intelligence (AI) and machine learning (ML) technologies. A forecast by IDC indicates spending on AI/ML will reach $35.8 billion this year and hit $79.2 billion by 2022. Though the principles of the technology have been around for decades, the more recent mass adoption of cloud computing and the flood of big data has made the concept a reality.
The result? Companies based around software-as-a-service are best positioned to take advantage of AI/ML because cloud and data are second nature to them.
In the past five years alone, AI/ML went from technology that showed lots of promise to one that delivers on that promise because of the convergence of easy access to inexpensive cloud computing and the integration of large data sets. AI and ML have already begun to see acceleration for cybersecurity uses. Dealing with mountains of data that only continues to grow, machines that analyze data bring immense value to security teams: They can operate 24/7 and humans can't.
For your cybersecurity team to effectively launch AI/ML, be sure these three requirements are in place:
1. Data: If AI/ML is a rocket, data is the fuel. AI/ML requires massive amounts of data to help it train models that can do classifications and predictions with high accuracy. Generally, the more data that goes through the AI/ML system, the better the outcome.
2. Data science and data engineering: Data scientists and ..
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