data classification

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Published By: AWS     Published Date: Oct 07, 2019
Imperva, an APN Security Competency Partner, can help protect your application workloads on AWS with the Imperva SaaS Web Application Security platform. The Imperva high-capacity network of globally distributed security services protects websites against all types of DDoS threats, including networklevel Layer 3 and Layer 4 volumetric attacks—such as synchronized (SYN) floods and User Datagram Protocol (UDP) floods—and Layer 7 application-level attacks (including the OWASP Top 10 threats) that attempt to compromise application resources. Harnessing real data about current threats from a global customer base, both the Web Application Firewall (WAF) and DDoS protection, incorporate an advanced client classification system that blocks malicious traffic without interfering with legitimate users. Enterprises can easily create custom security rules in the GUI to enforce their specific security policy. In addition, this versatile solution supports hybrid environments, allowing you to manage th
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AWS
Published By: Verdasys     Published Date: Mar 28, 2012
In this white paper you will learn how data classification can be one of the most cost-effective methods for ensuring the value of data is maximized while still being used securely and how it can be a critical component to successfully govern information at an enterprise.
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information protection, enterprise information protection, data loss prevention software, intellectual property protection, insider threat protection, insider threat protection software, data leakage software, mobile data protection
    
Verdasys
Published By: Intel     Published Date: Dec 13, 2018
In today’s world, advanced vision technologies is shaping the next era of Internet of Things. However, gathering streaming video data is insufficient. It needs to be timely and accessible in near-real time, analyzed, indexed, classified and searchable to inform strategy—while remaining cost-effective. Smart cities and manufacturing are prime examples where complexities and opportunities have been enabled by vision, IoT and AI solutions through automatic meter reading (AMR), image classification and segmentation, automated optical inspection (AOI), defect classification, traffic management solution—just to name a few. Together, ADLINK, Touch Cloud, and Intel provide a turnkey AI engine to assist in data analytics, detection, classification, and prediction for a wide range of use cases across a broad spectrum of sectors. Learn more about how the Touch Cloud AI brings cost savings, operational efficiency and a more reliable, actionable intelligence at the edge with transformative insi
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Intel
Published By: IBM     Published Date: Jul 08, 2016
Read this Forrester whitepaper to learn more about the critical, yet often overlooked, role that data classification and data discovery can play in reducing your organization’s risk and enhancing security.
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ibm, forrester, data discovery, big data, data, analytics, information governance
    
IBM
Published By: McAfee     Published Date: Jan 28, 2013
Re-think your DLP — This white paper discusses how a new DLP model aligns data discovery, classification, policy management, and security controls to your business requirements and processes.
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dlp, business-centric dlp, dlp business rules, discovery, classification, policy management, security controls
    
McAfee
Published By: McAfee EMEA     Published Date: Nov 15, 2017
Machine learning offers the depth, creative problem-solving capabilities, and automation to help security organizations gain significant ground against attackers. It’s a powerful tool for processing massive amounts of data for the purpose of malware classification and analysis, especially for unknown threats. Through supervised learning, human researchers can continually develop new training models that expand the understanding and competency of machine learning systems.
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analytics, security, problem solving, creative, data, researching, malware
    
McAfee EMEA
Published By: SRC,LLC     Published Date: Jun 01, 2009
Today, organizations are collecting data at every level of their business and in volumes that in the past were unimaginable. Data sets are stored in different database systems or in files with distinctive formats, all reflecting business process, application, program software, or information type dependencies. Adding to this complexity is the distribution of these data sets across the enterprise in silos requiring a varied set of tools and/or specialized business rules for data transformation, classification, matching, and integration. Because of the massive amounts of data stored in a variety of representation formats, decision makers strain to derive insights and create business solutions that adequately span and integrate information from these disparate technology islands. Learn more today!
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src, data transformation, classification, business value, geographic business intelligence, geo-bi, etl, extract, transform, load, spatial analysis, operational data stores, ods, spatial data, etl, cdi, point of sale, retail floor space management, rfsm, alteryx
    
SRC,LLC
Published By: AI Business     Published Date: Mar 11, 2019
LOOK TO RELEVANT USE CASES FOR YOUR BUSINESS. While use cases vary across industries, the most common ones fall into these categories and are usually associated with the listed neural network: • Image classification or object detection: convolutional neural network (CNN) • Time-series predictions: long short-term memory (LSTM) • Natural language processing: recurrent neural network (RNN) • Unlabeled data classification and data labeling: autoencoder (AE) • Anomaly detection: autoencoder (AE) • Recommender systems: multilayer perceptron (MLP) Work with your deep learning talent or consultants to identify which use cases best match your organization and desired solutions. Then recreate a successful, already proven method.
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AI Business
Published By: IBM     Published Date: Feb 18, 2009
The basic principles for managing an archiving project presented in this paper can help you initiate and plan an archiving strategy that meets your organization’s requirements.
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ibm integrated data management, peoplesoft, business critical erp applications, daily operations, database archiving, data growth, archiving project, data classification, service level agreements, slas, ibm optim data growth solution
    
IBM
Published By: IBM     Published Date: Feb 18, 2009
This white paper explains how the seven basic principles for managing enterprise application data can help your organization: Establish effective policies for full-lifecycle enterprise data management to control data growth and lower storage costs; Meet service level goals to achieve the timely completion of key business processes for mission-critical applications; Support data retention compliance initiatives and mitigate risk for audits and e-discovery requests; Implement scalable archiving strategies that easily adapt to your ongoing business requirements.
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ibm integrated data management, siebel crm, archiving project, service level goals, data retention compliance, archiving strategies, ibm optim data growth solution, rapid data growth, data classification
    
IBM
Published By: IBM     Published Date: Feb 18, 2009
This white paper explains how the seven basic principles for managing enterprise application data can help your organization: Establish effective policies for full-lifecycle enterprise data management to control data growth and lower storage costs; Meet service level goals to achieve the timely completion of key business processes for mission-critical applications; Support data retention compliance initiatives and mitigate risk for audits and e-discovery requests; Implement scalable archiving strategies that easily adapt to your ongoing business requirements.
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ibm integrated data management, oracle e-business suite, business-critical erp applications, archiving project, service level goals, data retention compliance, archiving strategies, ibm optim data growth solution, rapid data growth, data classification
    
IBM
Published By: IBM     Published Date: Feb 18, 2009
This white paper explains how the seven basic principles for managing enterprise application data can help your organization: Establish effective policies for full-lifecycle enterprise data management to control data growth and lower storage costs; Meet service level goals to achieve the timely completion of key business processes for mission-critical applications; Support data retention compliance initiatives and mitigate risk for audits and e-discovery requests; Implement scalable archiving strategies that easily adapt to your ongoing business requirements.
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ibm integrated data management, jd edwards, business-critical erp applications, archiving project, service level goals, data retention compliance, archiving strategies, ibm optim data growth solution, rapid data growth, data classification
    
IBM
Published By: McAfee     Published Date: Nov 12, 2014
This white paper outlines the best practices for better data loss prevention for healthcare organizations—from data discovery to classification to protection.
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data loss prevention, dlp, healthcare, dlp best practices
    
McAfee
Published By: BlackBerry Cylance     Published Date: Jul 02, 2018
The information security world is rich with information. From reviewing logs to analyzing malware, information is everywhere and in vast quantities, more than the workforce can cover. Artificial intelligence (AI) is a field of study that is adept at applying intelligence to vast amounts of data and deriving meaningful results. In this book, we will cover machine learning techniques in practical situations to improve your ability to thrive in a data driven world. With clustering, we will explore grouping items and identifying anomalies. With classification, we’ll cover how to train a model to distinguish between classes of inputs. In probability, we’ll answer the question “What are the odds?” and make use of the results. With deep learning, we’ll dive into the powerful biology inspired realms of AI that power some of the most effective methods in machine learning today. Learn more about AI in this eBook.
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artificial, intelligence, enterprise
    
BlackBerry Cylance
Published By: SAS     Published Date: Mar 14, 2014
This paper explores ways to qualify data control and measures to support the governance program. It will examine how data management practitioners can define metrics that are relevant.
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sas, data quality, business objectives, quality data, accuracy, lineage, structural consistency, relevant metrics, business relevance, measured value, emergent patterns, quality metrics, potential classifications, data analyst, scorecard, reporting the scorecard, improve scorecard, business process
    
SAS
Published By: SAS     Published Date: Mar 14, 2014
This paper will consider the relevance of measurement and monitoring – defining inspection routines, inserting them into the end-to-end application processing, and reporting the results.
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sas, data quality, business objectives, quality data, accuracy, lineage, structural consistency, relevant metrics, business relevance, measured value, emergent patterns, quality metrics, potential classifications, data analyst, scorecard, reporting the scorecard, improve scorecard, business process
    
SAS
Published By: IBM     Published Date: Oct 22, 2014
Read this Forrester whitepaper to learn more about the critical, yet often overlooked, role that data classification and data discovery can play in reducing your organization's risk and enhancing security.
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data classification, data discovery, defining data, data identity
    
IBM
Published By: IBM     Published Date: Feb 24, 2015
Read this Forrester whitepaper to learn more about the critical, yet often overlooked, role that data classification and data discovery can play in reducing your organization's risk and enhancing security.
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ibm, data discovery, data classification, big data, risk mitigation
    
IBM
Published By: IBM     Published Date: Apr 06, 2015
Read this Forrester whitepaper to learn more about the critical, yet often overlooked, role that data classification and data discovery can play in reducing your organization's risk and enhancing security.
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data discovery, data classification, security, risk, ibm
    
IBM
Published By: Varonis     Published Date: Nov 12, 2013
The purpose of this document is to explain how Varonis products can help ease migration to Microsoft SharePoint, and how it can help keep data properly managed and protected once data has been moved.
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varonis, sharepoint, sharepoint migrations, managing sharepoint content, protecting sharepoint content, project plan, sharepoint deployment, datadvantage, idu classification framework, data transport engine, metadata collection, pre-migration cleanup, moving data, business monitoring, human generated data
    
Varonis
Published By: Forcepoint     Published Date: Oct 03, 2019
In part two of the Unified Data Protection for a Hybrid IT World webinar series, we take a deeper dive into use cases around the challenges that businesses are facing today and how Forcepoint’s solutions meet your data protection needs. Understanding the location of all data, whether at-rest, in-use, or in motion across on-premises and the cloud is essential to keep data safe and secure. This enables companies to identify their riskiest users and behaviors. With Forcepoint, you can automate the identification, classification, and protection of critical data. This webinar explores how companies should rethink data security. Protecting critical data and intellectual property requires a risk-adaptive, dynamic approach, including these three main components. Analytics-Driven Visibility – to gain oversight everywhere users work and collaborate Risk-Adaptive Controls – to eliminate the complexity of policy enforcement Intelligent & Dynamic Automation – to build a better understanding of c
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Forcepoint
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