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While businesses benefit from AI tools, many have discovered that various technologies take different approaches to data protection. Some analytics tools and databases have built-in data protection, but that doesn’t always meet the required level of security. Also, compliance and security teams struggle to align data protection with these tools. Lines of business want to use AI tools that best meet their specific needs, and a one-size-fits-all application doesn't cut it for entire enterprise. Businesses need to coalesce their many AI tools with a unifying system that manages the complexities of security.
Enterprises tepidly approach AI because they’re afraid of not complying with regulations safeguarding the privacy of data. It’s a tough balancing act: Maximizing AI applications that personalize customer experiences, improve operations across the enterprise, and anticipate industry trends—all while staying mindful of data privacy. Enterprises can’t grant liberal access to data, nor can they take months to determine which data needs to be anonymized for analytics. Effective, enterprise-wide security has to shape data analytics if businesses are to extract value from data and protect its most sensitive elements.
Digital transformation has brought with it the ability for businesses to generate, organize, and activate data more seamlessly than ever before. We see data as a superpower that businesses can use to drive advanced analytics and AI.
When digital transformation comes knocking, it wants to take your sensitive data with it—to the cloud. Today’s enterprises know they can increase analytics agility by leveraging hybrid- and multi-cloud services.
Businesses can monetize the troves of data they have accumulated, transforming operational data into a valuable asset. For many, the ability to share privacy-preserved data and analysis could have world-impacting implications.
