{"id":33570,"date":"2023-10-03T04:00:00","date_gmt":"2023-10-03T11:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=33570"},"modified":"2023-10-02T18:53:07","modified_gmt":"2023-10-03T01:53:07","slug":"seven-reasons-why-data-security-needs-ai","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/10\/03\/seven-reasons-why-data-security-needs-ai\/","title":{"rendered":"Seven Reasons why Data Security Needs AI"},"content":{"rendered":"\n

Today data holds the key to business success. Enterprises are embracing the power of data to improve marketing effectiveness, identify new revenue opportunities, personalize customer experiences, and improve operational efficiency.   However, the increasing complexity of the data landscape is making it a huge challenge to provide users and applications with fast access required, while ensuring regulatory compliance. <\/p>\n\n\n\n

Here are seven trends that suggest why more intelligent and automated solutions are needed to enable data governance.<\/p>\n\n\n\n

    \n
  1. Data Federation <\/strong>\u2013 The process of decentralizing or democratizing data is a quantum leap in enterprise data architecture that opens the door to numerous ways to maximize business outcomes. However, the ability to query data from different sources into one single virtual format requires improved data quality as well as data accessibility. Enterprises need a flexible data security architecture where data governance standards are defined centrally, but domain teams have the autonomy and resources to execute them locally.<\/li>\n\n\n\n
  2. The Rise of the Data Mesh<\/strong> – Monolithic data infrastructures that handle data consumption, storage, transformation, and output in one central repository are becoming replaced by a data mesh architecture with a wide variety of dispersed data sources, infrastructure, and pipelines. Because there are different methods for securing data in each environment, there is a higher likelihood of errors or inconsistencies managing security authorizations.<\/li>\n\n\n\n
  3. Highly Dynamic Regulatory Environment<\/strong> – The regulatory landscape is becoming increasingly more complex.  International companies are challenged to follow state, federal and international data privacy regulations, where over 130 countries have their own legislation for data privacy.  Data security needs to accommodate the latest versions of all these regulations and adjust policies based on internal security requirements automatically.<\/li>\n\n\n\n
  4. Shifting Data to the Cloud<\/strong> – Businesses all over the world are replacing legacy, on-premises technology with flexible, scalable, and cost-effective computing in the cloud. As organizations increasingly rely on third party providers for collecting, storing, and processing sensitive data, they lose visibility of data assets and can be more vulnerable to cyberattacks and data breaches.<\/li>\n\n\n\n
  5. AI\/ML<\/strong> – AI enables organizations to streamline operations, generate better predictions and insights, and make smarter decisions to drive revenue.  The success of these AI models is increasing the demand for a steady pipeline of data, but safe access requires monitoring multitude of different regulations and internal security policies.<\/li>\n\n\n\n
  6. Data Explosion<\/strong> – The volume of enterprise data is expected to more than double in size between 2022 and 2026, according to John Rydning, research vice president, IDC’s Global DataSphere.  Enterprises need the ability to scale up their security systems to manage the growing volumes of data as well as the increasing number of data consumers including employees, customers, suppliers, partners, ML models, and applications.<\/li>\n\n\n\n
  7. Need for Automated Policy Access Control – <\/strong>The most common method for access control RBAC (rule-based access control), has several limitations.  Permissions can be assigned only to user roles, not to objects and operations. Actions are restricted instead of access to data. Employees also often change roles and companies, making it very difficult to keep permissions up to date.  Policy-based authorizations are more efficient, but they are more complex and need AI to learn how data is used to provide the correct authorizations.  <\/li>\n<\/ol>\n\n\n\n

    With the increasingly complex data landscape, organizations need a strong data governance strategy.\u00a0 Only intelligent automation can keep up with the fast pace of highly dynamic data teams, users, tools, and regulations.\u00a0\u00a0 AI is an indispensable technology because it can monitor, learn, and predict how data will be used to provide the right people with the data they need to fuel data democratization and business success.\u00a0\u00a0\u00a0<\/p>\n\n\n\n

    About the Author<\/strong><\/p>\n\n\n

    \n
    \"\"<\/figure><\/div>\n\n\n

    Noam Biran, Velotix<\/a> VP Product. Noam is a product management executive that has successfully developed a product idea into a thriving business with over $100M in sales. Currently VP of Product at Velotix he is responsible for developing the technical vision and product strategy that supports business goals. Noam previously served as VP of product at the cybersecurity company Hunters and as Director of Product Management at ServiceNow that provides a cloud-based platform to help digitize and unify enterprises.<\/p>\n\n\n\n

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    Today data holds the key to business success. Enterprises are embracing\u00a0the power of data\u00a0to improve marketing effectiveness, identify new revenue opportunities, personalize customer experiences, and improve operational efficiency.\u00a0\u00a0 However, the increasing complexity of the data landscape is making it a huge challenge to provide users and applications with fast access required, while ensuring regulatory compliance.\u00a0In this contributed article, Noam Biran, Velotix VP Product, offers seven trends that suggest why more intelligent and automated solutions are needed to enable data governance.<\/p>\n","protected":false},"author":10531,"featured_media":33572,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,115,182,180,61,67,56,97,1],"tags":[280,341],"acf":[],"yoast_head":"\nSeven Reasons why Data Security Needs AI - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2023\/10\/03\/seven-reasons-why-data-security-needs-ai\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Seven Reasons why Data Security Needs AI - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"Today data holds the key to business success. Enterprises are embracing\u00a0the power of data\u00a0to improve marketing effectiveness, identify new revenue opportunities, personalize customer experiences, and improve operational efficiency.\u00a0\u00a0 However, the increasing complexity of the data landscape is making it a huge challenge to provide users and applications with fast access required, while ensuring regulatory compliance.\u00a0In this contributed article, Noam Biran, Velotix VP Product, offers seven trends that suggest why more intelligent and automated solutions are needed to enable data governance.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/insidebigdata.com\/2023\/10\/03\/seven-reasons-why-data-security-needs-ai\/\" \/>\n<meta property=\"og:site_name\" content=\"insideBIGDATA\" \/>\n<meta property=\"article:publisher\" content=\"http:\/\/www.facebook.com\/insidebigdata\" \/>\n<meta property=\"article:published_time\" content=\"2023-10-03T11:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-10-03T01:53:07+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/10\/Data_Pipeline_shutterstock_9623992_special.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1100\" \/>\n\t<meta property=\"og:image:height\" content=\"550\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Contributor\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@insideBigData\" \/>\n<meta name=\"twitter:site\" content=\"@insideBigData\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Contributor\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/insidebigdata.com\/2023\/10\/03\/seven-reasons-why-data-security-needs-ai\/\",\"url\":\"https:\/\/insidebigdata.com\/2023\/10\/03\/seven-reasons-why-data-security-needs-ai\/\",\"name\":\"Seven Reasons why Data Security Needs AI - insideBIGDATA\",\"isPartOf\":{\"@id\":\"https:\/\/insidebigdata.com\/#website\"},\"datePublished\":\"2023-10-03T11:00:00+00:00\",\"dateModified\":\"2023-10-03T01:53:07+00:00\",\"author\":{\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/35a290930284d4cdbf002d457f3d5d87\"},\"breadcrumb\":{\"@id\":\"https:\/\/insidebigdata.com\/2023\/10\/03\/seven-reasons-why-data-security-needs-ai\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/insidebigdata.com\/2023\/10\/03\/seven-reasons-why-data-security-needs-ai\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/insidebigdata.com\/2023\/10\/03\/seven-reasons-why-data-security-needs-ai\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/insidebigdata.com\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Seven Reasons why Data Security Needs AI\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/insidebigdata.com\/#website\",\"url\":\"https:\/\/insidebigdata.com\/\",\"name\":\"insideBIGDATA\",\"description\":\"Your Source for AI, Data Science, Deep Learning & Machine Learning Strategies\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/insidebigdata.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/35a290930284d4cdbf002d457f3d5d87\",\"name\":\"Contributor\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/36bffd267e38ed3f525205f67270e91b?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/36bffd267e38ed3f525205f67270e91b?s=96&d=mm&r=g\",\"caption\":\"Contributor\"},\"url\":\"https:\/\/insidebigdata.com\/author\/contributor\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Seven Reasons why Data Security Needs AI - insideBIGDATA","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/insidebigdata.com\/2023\/10\/03\/seven-reasons-why-data-security-needs-ai\/","og_locale":"en_US","og_type":"article","og_title":"Seven Reasons why Data Security Needs AI - insideBIGDATA","og_description":"Today data holds the key to business success. 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