What will you learn:
- Key Insights on Existing Big Data Architecture
- Unique Security Risks and Vulnerabilities of Big Data Technologies
- Top 5 Solutions to mitigate these security challenges
Big Data is the "next" Bg Technology and Business and Hadoop is one of the important framework of Big Data. Hadoop is currently used by Yahoo, EBay and 100s of organisations.
As the Big Data use cases will grow, security of Big Data technologies, solutions and applications will become extremely important. In this presentation, I have described top 5 key security challenges related to developing Big Data solutions and applications.
The document discusses how big data, increased data volumes, and weaknesses in security present a "perfect storm" risk scenario. It notes that while big data deployments are growing fast to realize business value, security is often not properly prioritized or implemented. This can allow breaches to go undetected. The document also outlines how data sources and volumes are expanding dramatically, while relevant security skills remain limited. Overall it argues that the confluence of these factors poses significant security challenges for organizations working with big data.
Information Security in Big Data : Privacy and Data Miningwanani181
This document discusses the roles involved in data mining processes and privacy concerns. It describes the roles of data provider, data collector, data miner, and decision maker. For each role, it outlines their privacy concerns and approaches that can be used to address those concerns, such as limiting data access, anonymization techniques, and secure multi-party computation. The goal of privacy-preserving data mining is to protect sensitive information while still allowing for useful knowledge discovery from data.
Information security in big data -privacy and data miningharithavijay94
The document discusses privacy and data mining in big data. It describes the four types of users in data mining - data providers, data collectors, data miners, and decision makers. Each have different privacy concerns. For data providers, the major concern is controlling sensitive data access. Approaches include limiting access, trading privacy for benefits, and providing false data. For data collectors, the concern is guaranteeing modified data preserves utility while removing sensitive information. Approaches include anonymization techniques. For data miners, the concern is preventing sensitive results. Approaches include privacy-preserving association rule and classification mining. For decision makers, the concerns are preventing unwanted disclosure of results and evaluating result credibility. Approaches include legal measures and using data provenance
Big Data Security Analytics (BDSA) with Randy FranklinSridhar Karnam
The document discusses big data security analytics and how HP addresses related challenges. It notes that big data analytics for security requires real-time analysis of high-volume, diverse data streams. While many big data solutions focus on batch analytics, security demands real-time correlation and detection of threats. The document outlines how HP's ArcSight platform collects, correlates, and analyzes security data from many sources in real-time. It also explains how HP uses Hadoop for long-term storage and analytics, and Autonomy for semantic analysis of unstructured data to enable predictive security.
This document discusses security challenges in big data and cloud computing environments. It notes that HDFS and MapReduce do not provide adequate security for sensitive data. It proposes several techniques to improve security, such as encrypting data, using honeypot detection, logging all MapReduce jobs and user information, and having honeypot nodes to trap hackers. Encrypting network communication and data is also recommended to prevent hackers from extracting meaningful information even if they are able to access data or network packets.
The document discusses securing big data in enterprises. It notes that big data presents both challenges and opportunities for security. Throughout the data lifecycle, from collection to analysis, security is crucial. This involves securing access to data, enforcing policies, detecting threats, and protecting data across systems. With the right tools for logging, analysis, and reporting, organizations can better understand normal network activity and secure vast amounts of information to leverage the opportunities big data provides.
The REAL Impact of Big Data on PrivacyClaudiu Popa
The awesome promise of Big Data is tempered by the need to protect personal information. Data scientists must expertly navigate the legislative waters and acquire the skills to protect privacy and security. This talk provides enterprise leaders with answers and suggests questions to ask when the time comes to consider the vast opportunities offered by big data.
Big Data is the "next" Bg Technology and Business and Hadoop is one of the important framework of Big Data. Hadoop is currently used by Yahoo, EBay and 100s of organisations.
As the Big Data use cases will grow, security of Big Data technologies, solutions and applications will become extremely important. In this presentation, I have described top 5 key security challenges related to developing Big Data solutions and applications.
The document discusses how big data, increased data volumes, and weaknesses in security present a "perfect storm" risk scenario. It notes that while big data deployments are growing fast to realize business value, security is often not properly prioritized or implemented. This can allow breaches to go undetected. The document also outlines how data sources and volumes are expanding dramatically, while relevant security skills remain limited. Overall it argues that the confluence of these factors poses significant security challenges for organizations working with big data.
Information Security in Big Data : Privacy and Data Miningwanani181
This document discusses the roles involved in data mining processes and privacy concerns. It describes the roles of data provider, data collector, data miner, and decision maker. For each role, it outlines their privacy concerns and approaches that can be used to address those concerns, such as limiting data access, anonymization techniques, and secure multi-party computation. The goal of privacy-preserving data mining is to protect sensitive information while still allowing for useful knowledge discovery from data.
Information security in big data -privacy and data miningharithavijay94
The document discusses privacy and data mining in big data. It describes the four types of users in data mining - data providers, data collectors, data miners, and decision makers. Each have different privacy concerns. For data providers, the major concern is controlling sensitive data access. Approaches include limiting access, trading privacy for benefits, and providing false data. For data collectors, the concern is guaranteeing modified data preserves utility while removing sensitive information. Approaches include anonymization techniques. For data miners, the concern is preventing sensitive results. Approaches include privacy-preserving association rule and classification mining. For decision makers, the concerns are preventing unwanted disclosure of results and evaluating result credibility. Approaches include legal measures and using data provenance
Big Data Security Analytics (BDSA) with Randy FranklinSridhar Karnam
The document discusses big data security analytics and how HP addresses related challenges. It notes that big data analytics for security requires real-time analysis of high-volume, diverse data streams. While many big data solutions focus on batch analytics, security demands real-time correlation and detection of threats. The document outlines how HP's ArcSight platform collects, correlates, and analyzes security data from many sources in real-time. It also explains how HP uses Hadoop for long-term storage and analytics, and Autonomy for semantic analysis of unstructured data to enable predictive security.
This document discusses security challenges in big data and cloud computing environments. It notes that HDFS and MapReduce do not provide adequate security for sensitive data. It proposes several techniques to improve security, such as encrypting data, using honeypot detection, logging all MapReduce jobs and user information, and having honeypot nodes to trap hackers. Encrypting network communication and data is also recommended to prevent hackers from extracting meaningful information even if they are able to access data or network packets.
The document discusses securing big data in enterprises. It notes that big data presents both challenges and opportunities for security. Throughout the data lifecycle, from collection to analysis, security is crucial. This involves securing access to data, enforcing policies, detecting threats, and protecting data across systems. With the right tools for logging, analysis, and reporting, organizations can better understand normal network activity and secure vast amounts of information to leverage the opportunities big data provides.
The REAL Impact of Big Data on PrivacyClaudiu Popa
The awesome promise of Big Data is tempered by the need to protect personal information. Data scientists must expertly navigate the legislative waters and acquire the skills to protect privacy and security. This talk provides enterprise leaders with answers and suggests questions to ask when the time comes to consider the vast opportunities offered by big data.
Big Data Meets Privacy:De-identification Maturity Model for Benchmarking and ...Khaled El Emam
The document discusses de-identification and the De-identification Maturity Model (DMM). The DMM is a framework that evaluates an organization's maturity in de-identifying data based on their people, processes, technologies, and measurement practices. It assesses an organization across three dimensions: practice, implementation, and automation. Higher levels of maturity indicate more robust de-identification processes that better balance privacy and data utility. The document provides examples of how the DMM could be used to evaluate different organizations' de-identification practices.
Expanded top ten_big_data_security_and_privacy_challengesTom Kirby
The document discusses the top 10 security and privacy challenges of big data. It begins by explaining how big data has expanded through streaming cloud technology, rendering traditional security mechanisms inadequate. It then outlines a 3-step process used to identify the top 10 challenges: 1) interviewing CSA members and reviewing trade journals to draft an initial list, 2) studying published solutions, and 3) characterizing remaining problems as challenges if solutions did not adequately address problem scenarios. The top 10 challenges are then grouped into 4 aspects: infrastructure security, data privacy, data management, and integrity and reactive security. The first challenge discussed in detail is securing computations in distributed programming frameworks.
The advent of Big Data has presented nee challenges in terms of Data Security. There is an increasing need of research
in technologies that can handle the vast volume of Data and make it secure efficiently. Current Technologies for securing data are
slow when applied to huge amounts of data. This paper discusses security aspect of Big Data.
This document outlines security issues associated with big data in cloud computing. It begins with introductions to big data, cloud computing, and Hadoop. It describes how big data is related to cloud computing and discusses advantages and applications of big data. The document then discusses security issues at the network, authentication, and data levels. It proposes several approaches to address these security issues, such as file encryption, network encryption, and access control. Finally, it discusses conclusions and opportunities for future work.
Big Data and Security - Where are we now? (2015)Peter Wood
Peter Wood started looking at Big Data as a solution for Advanced Threat Protection in 2013. This presentation examines how Big Data is being used for security in 2015, how this market is developing and how realistic vendor offerings are.
This document provides an overview of MasterCard's approach to securing big data. It discusses security pillars like perimeter security, access security, visibility security and data security. It also covers infrastructure and data architecture vulnerabilities and recommends steps like implementing role-based access controls, encrypting data, regularly monitoring systems and updating software. The document emphasizes that security is an ongoing process requiring collaboration, training and maturity across people, processes and technologies.
Atlanta ISSA 2010 Enterprise Data Protection Ulf MattssonUlf Mattsson
Ulf Mattsson is the CTO of Protegrity, a company that provides data security solutions through encryption, tokenization, and policy-driven approaches. He has over 20 years of experience in data security research. This presentation discusses evolving data security risks and reviews options for enterprise data protection strategies. It examines studies on implementing protection in real-world scenarios and recommends balancing performance, security, and compliance when choosing defenses for sensitive data across different systems and storage locations. The presentation also introduces Protegrity's centralized risk-adjusted platform for securing data throughout its lifecycle.
In depth presentation covers market trends and risks related to network security & big data analytics. The presentation was given by Matan Trogan at Cybertech Singapore.
The value of the fast growing class of big data technologies is the ability to handle high velocity and volumes of data. However, a lack of robust security and auditing capabilities are holding organizations back from fully using the potential of these systems. Learn how you can use Big Data technologies to help you meet this compliance and data protection challenge head on so you can return to innovating for competitive advantage.
Using InfoSphere Guardium and BigInsights, we'll show you how you can meet your Hadoop security, compliance and audit requirements.
Threat Ready Data: Protect Data from the Inside and the OutsideDLT Solutions
Is your current state really threat ready?
Amit Walia, Senior Vice President, General Manager of Data Integration and Security at Informatica, shares how to protect data from the inside and the outside from the 2015 Informatica Government Summit.
This document discusses security challenges related to big data and Hadoop. It notes that as data grows exponentially, the complexity of managing, securing, and enforcing privacy restrictions on data sets increases. Organizations now need to control access to data scientists based on authorization levels and what data they are allowed to see. Mismanagement of data sets can be costly, as shown by incidents at AOL, Netflix, and a Massachusetts hospital that led to lawsuits and fines. The document then provides a brief history of Hadoop security, noting that it was originally developed without security in mind. It outlines the current Kerberos-centric security model and talks about some vendor solutions emerging to enhance Hadoop security. Finally, it provides guidance on developing security and privacy
Big Data Analytics to Enhance Security
Predictive Analtycis and Data Science Conference May 27-28
Anapat Pipatkitibodee
Technical Manager
anapat.p@Stelligence.com
Security Analytics and Big Data: What You Need to KnowMapR Technologies
The number of attacks on organization's' IT infrastructure are continuously increasing. It is becoming more and more difficult to identify unknown threats, in particular. This problem requires the ability to store more data and better tools to analyze the data.
Learn in this webinar why big data is enabling new security analytics solutions and why the MapR Quick Start Solution for Security Analytics offers an easy starting point for faster and deeper security analytics.
To Serve and Protect: Making Sense of Hadoop Security Inside Analysis
HP Security Voltage provides data-centric security solutions to protect sensitive data in Hadoop environments. Their solutions leverage tokenization and encryption to safeguard data at rest, in motion, and in use across the data lifecycle. They presented use cases where their technology helped secure financial, healthcare, and telecommunications customer data in Hadoop and other platforms. Questions from analysts focused on implementation experience, performance impacts, integration with authentication, costs, and supported environments and partnerships.
This document summarizes a presentation on preventing data leakage. It defines data leakage and data loss prevention. It identifies gaps in the company's current security measures, including a lack of mechanisms to capture sensitive data. It evaluates vendors that could address this gap, selecting Vontu. It discusses Vontu products that could protect data in motion and meet pricing estimates. It recommends additionally implementing Blue Coat Proxy to handle network loads and provide URL filtering to support the Vontu solution.
Privacy and Security by Design Spotlight Presentation at HIMMS Privacy and Security Forum, December 5th 2016. Presented by Jeff R. Livingstone, PhD, Vice President and Global Lead, Life Sciences & Healthcare, Unisys Corporation.
All the essential information you need about DLP in one eBook.
As security professionals struggle with how to keep up with threats, DLP - a technology designed to ensure sensitive data isn't stolen or lost - is hot again. This comprehensive guide provides what you need to understand, evaluate, and succeed with today's DLP. It includes insights from DLP Experts, Forrester Research, Gartner, and Digital Guardian's security analysts.
What's Inside:
-The seven trends that have made DLP hot again
-How to determine the right approach for your organization
-Making the business case to executives
-How to build an RFP and evaluate vendors
-How to start with a clearly defined quick win
-Straight-forward frameworks for success
Security and privacy of cloud data: what you need to know (Interop)Druva
Is your company thinking about the cloud or already in there but learning fast about the many challenges of the security and privacy of cloud data?
Learn more about the landscape of data in the cloud, and the obstacles that every company should consider when it comes to protecting their down.
Privacy Secrets Your Systems May Be TellingRebecca Leitch
Privacy has overtaken security as a top concern for many organizations. New laws such as GDPR come with steep fines and stringent rules, and more are certainly to come. Attend this webcast to learn how everyday business operations put customer privacy data at risk. More importantly understand best practices on protecting this data and dealing with disclosure requirements. Topics include:
* Types of privacy and threats to them
* How is privacy different than security?
* Business systems putting you most at risk
This document provides an overview of cyber crime and security. It defines cyber crime as illegal activity committed on the internet, such as stealing data or importing malware. The document then covers the history and evolution of cyber threats. It categorizes cyber crimes as those using the computer as a target or weapon. Specific types of cyber crimes discussed include hacking, denial of service attacks, virus dissemination, computer vandalism, cyber terrorism, and software piracy. The document concludes by emphasizing the importance of cyber security.
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy.
Big Data Meets Privacy:De-identification Maturity Model for Benchmarking and ...Khaled El Emam
The document discusses de-identification and the De-identification Maturity Model (DMM). The DMM is a framework that evaluates an organization's maturity in de-identifying data based on their people, processes, technologies, and measurement practices. It assesses an organization across three dimensions: practice, implementation, and automation. Higher levels of maturity indicate more robust de-identification processes that better balance privacy and data utility. The document provides examples of how the DMM could be used to evaluate different organizations' de-identification practices.
Expanded top ten_big_data_security_and_privacy_challengesTom Kirby
The document discusses the top 10 security and privacy challenges of big data. It begins by explaining how big data has expanded through streaming cloud technology, rendering traditional security mechanisms inadequate. It then outlines a 3-step process used to identify the top 10 challenges: 1) interviewing CSA members and reviewing trade journals to draft an initial list, 2) studying published solutions, and 3) characterizing remaining problems as challenges if solutions did not adequately address problem scenarios. The top 10 challenges are then grouped into 4 aspects: infrastructure security, data privacy, data management, and integrity and reactive security. The first challenge discussed in detail is securing computations in distributed programming frameworks.
The advent of Big Data has presented nee challenges in terms of Data Security. There is an increasing need of research
in technologies that can handle the vast volume of Data and make it secure efficiently. Current Technologies for securing data are
slow when applied to huge amounts of data. This paper discusses security aspect of Big Data.
This document outlines security issues associated with big data in cloud computing. It begins with introductions to big data, cloud computing, and Hadoop. It describes how big data is related to cloud computing and discusses advantages and applications of big data. The document then discusses security issues at the network, authentication, and data levels. It proposes several approaches to address these security issues, such as file encryption, network encryption, and access control. Finally, it discusses conclusions and opportunities for future work.
Big Data and Security - Where are we now? (2015)Peter Wood
Peter Wood started looking at Big Data as a solution for Advanced Threat Protection in 2013. This presentation examines how Big Data is being used for security in 2015, how this market is developing and how realistic vendor offerings are.
This document provides an overview of MasterCard's approach to securing big data. It discusses security pillars like perimeter security, access security, visibility security and data security. It also covers infrastructure and data architecture vulnerabilities and recommends steps like implementing role-based access controls, encrypting data, regularly monitoring systems and updating software. The document emphasizes that security is an ongoing process requiring collaboration, training and maturity across people, processes and technologies.
Atlanta ISSA 2010 Enterprise Data Protection Ulf MattssonUlf Mattsson
Ulf Mattsson is the CTO of Protegrity, a company that provides data security solutions through encryption, tokenization, and policy-driven approaches. He has over 20 years of experience in data security research. This presentation discusses evolving data security risks and reviews options for enterprise data protection strategies. It examines studies on implementing protection in real-world scenarios and recommends balancing performance, security, and compliance when choosing defenses for sensitive data across different systems and storage locations. The presentation also introduces Protegrity's centralized risk-adjusted platform for securing data throughout its lifecycle.
In depth presentation covers market trends and risks related to network security & big data analytics. The presentation was given by Matan Trogan at Cybertech Singapore.
The value of the fast growing class of big data technologies is the ability to handle high velocity and volumes of data. However, a lack of robust security and auditing capabilities are holding organizations back from fully using the potential of these systems. Learn how you can use Big Data technologies to help you meet this compliance and data protection challenge head on so you can return to innovating for competitive advantage.
Using InfoSphere Guardium and BigInsights, we'll show you how you can meet your Hadoop security, compliance and audit requirements.
Threat Ready Data: Protect Data from the Inside and the OutsideDLT Solutions
Is your current state really threat ready?
Amit Walia, Senior Vice President, General Manager of Data Integration and Security at Informatica, shares how to protect data from the inside and the outside from the 2015 Informatica Government Summit.
This document discusses security challenges related to big data and Hadoop. It notes that as data grows exponentially, the complexity of managing, securing, and enforcing privacy restrictions on data sets increases. Organizations now need to control access to data scientists based on authorization levels and what data they are allowed to see. Mismanagement of data sets can be costly, as shown by incidents at AOL, Netflix, and a Massachusetts hospital that led to lawsuits and fines. The document then provides a brief history of Hadoop security, noting that it was originally developed without security in mind. It outlines the current Kerberos-centric security model and talks about some vendor solutions emerging to enhance Hadoop security. Finally, it provides guidance on developing security and privacy
Big Data Analytics to Enhance Security
Predictive Analtycis and Data Science Conference May 27-28
Anapat Pipatkitibodee
Technical Manager
anapat.p@Stelligence.com
Security Analytics and Big Data: What You Need to KnowMapR Technologies
The number of attacks on organization's' IT infrastructure are continuously increasing. It is becoming more and more difficult to identify unknown threats, in particular. This problem requires the ability to store more data and better tools to analyze the data.
Learn in this webinar why big data is enabling new security analytics solutions and why the MapR Quick Start Solution for Security Analytics offers an easy starting point for faster and deeper security analytics.
To Serve and Protect: Making Sense of Hadoop Security Inside Analysis
HP Security Voltage provides data-centric security solutions to protect sensitive data in Hadoop environments. Their solutions leverage tokenization and encryption to safeguard data at rest, in motion, and in use across the data lifecycle. They presented use cases where their technology helped secure financial, healthcare, and telecommunications customer data in Hadoop and other platforms. Questions from analysts focused on implementation experience, performance impacts, integration with authentication, costs, and supported environments and partnerships.
This document summarizes a presentation on preventing data leakage. It defines data leakage and data loss prevention. It identifies gaps in the company's current security measures, including a lack of mechanisms to capture sensitive data. It evaluates vendors that could address this gap, selecting Vontu. It discusses Vontu products that could protect data in motion and meet pricing estimates. It recommends additionally implementing Blue Coat Proxy to handle network loads and provide URL filtering to support the Vontu solution.
Privacy and Security by Design Spotlight Presentation at HIMMS Privacy and Security Forum, December 5th 2016. Presented by Jeff R. Livingstone, PhD, Vice President and Global Lead, Life Sciences & Healthcare, Unisys Corporation.
All the essential information you need about DLP in one eBook.
As security professionals struggle with how to keep up with threats, DLP - a technology designed to ensure sensitive data isn't stolen or lost - is hot again. This comprehensive guide provides what you need to understand, evaluate, and succeed with today's DLP. It includes insights from DLP Experts, Forrester Research, Gartner, and Digital Guardian's security analysts.
What's Inside:
-The seven trends that have made DLP hot again
-How to determine the right approach for your organization
-Making the business case to executives
-How to build an RFP and evaluate vendors
-How to start with a clearly defined quick win
-Straight-forward frameworks for success
Security and privacy of cloud data: what you need to know (Interop)Druva
Is your company thinking about the cloud or already in there but learning fast about the many challenges of the security and privacy of cloud data?
Learn more about the landscape of data in the cloud, and the obstacles that every company should consider when it comes to protecting their down.
Privacy Secrets Your Systems May Be TellingRebecca Leitch
Privacy has overtaken security as a top concern for many organizations. New laws such as GDPR come with steep fines and stringent rules, and more are certainly to come. Attend this webcast to learn how everyday business operations put customer privacy data at risk. More importantly understand best practices on protecting this data and dealing with disclosure requirements. Topics include:
* Types of privacy and threats to them
* How is privacy different than security?
* Business systems putting you most at risk
This document provides an overview of cyber crime and security. It defines cyber crime as illegal activity committed on the internet, such as stealing data or importing malware. The document then covers the history and evolution of cyber threats. It categorizes cyber crimes as those using the computer as a target or weapon. Specific types of cyber crimes discussed include hacking, denial of service attacks, virus dissemination, computer vandalism, cyber terrorism, and software piracy. The document concludes by emphasizing the importance of cyber security.
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy.
Overview of policies for security and data sharingbdemchak
This document provides an overview of policies for security and data sharing in the Physical Activity Location Measurement System (PALMS). PALMS aims to support data collection and analysis for exposure biology studies while being extensible, flexible, and HIPAA compliant. It discusses PALMS' logical architecture and policy composition, as well as the relationship between PALMS and the cancer Biomedical Informatics Grid (caBIG) framework. Key topics covered include identity management, access control policies, and integrating PALMS with caBIG services and tools for enforcing security policies in enterprise grids.
Presented by Fernando Gont at CISO Platform Annual Summit, 2013. Fernando specializes in the field of communications protocols security, working for private and governmental organisations both in Argentina and overseas.
PaaSword: A Holistic Data Privacy and Security by Design Framework for Cloud ...Yiannis Verginadis
This is a paper presentation held at the 5th International Conference on Cloud Computing and Services Science (CLOSER 2015) in Lisbon, Portugal. The authors outline significant security challenges presented when migrating to a cloud environment and described a novel holistic framework that aspires to alleviate these challenges, corresponding to the high level description of the vision of the PaaSword project.
Enterprise 2.0: What it is and why it mattersdigitallibrary
While Web 2.0 is now considered mainstream, Enterprise 2.0 is relatively new and leverages Web 2.0 technologies in the context of business. Get an analyst's view of Enterprise 2.0. What is it? How does it impact enterprise software? How can IT organizations use it?
SMB Security Opportunity –Use and Plans for Solutions and Profile of "Securit...Motty Ben Atia
This document summarizes survey results about SMB security technology use and plans. It finds that while most SMBs prioritize basic security like antivirus, a group of "security intensive" SMBs in fields like engineering and healthcare devote more resources to security. These intensives are more likely to have IT staff, networks, and cloud services. They currently use and plan to adopt more advanced security technologies at higher rates than average SMBs, especially mid-sized businesses. The document concludes SMBs recognize the importance of security but need guidance on effective solutions as risks grow with mobility and online activities.
Keynote Address at 2013 CloudCon: A day in the life of the SMB by Michael To...exponential-inc
How can I benefit from the cloud? I hear about the cloud all the time, but what will it really do for me and my business? These and other questions about “cloud” and IT services are part of the day in the life of every SMB (Small to Medium-sized Business) customer in the U.S. market. The reason they are in business or running a business does not center around Cloud and IT, but on their business. Whether it is keeping the retail sales flowing or food products going out the door, which is why they are in business. A good IT services and Cloud provider is there to provide the support they need to run their businesses more efficiently and effectively so they can truly focus on what they love, their business. Michael Toplisek, the EVP of Marketing and Product at EarthLink will use real customer examples to illustrate how excellent cloud services can help the smb customer lift some of the burdens of their daily business allowing them to do the things they do best.
Keeping up with the Revolution in IT SecurityDistil Networks
For many of today’s businesses, web applications are their lifeline. The growing complexity involved in keeping these applications fast, secure, and available can be seen as a byproduct of shifts in how these apps are developed, deployed, and attacked. This discussion will explore how high level trends in today’s web environments and the cyber attack landscape are shaping tomorrow’s application security solutions.
Key Takeaways:
- Trends in contemporary web applications that are forcing security evolution
- How today’s cyber attack landscape impacts cybersecurity
- What modern IT security solutions look like
- Distil Networks Overview
This presentation was given at the BSidesMemphis 2012 and DerbyCon 2012 information security conferences. It lays out the process that a person should follow to implement a database security program specific to their organization.
Box is an online file storage and sharing service with over 2 million registered users and growing at 3,000 users per day. It provides access to files from any device, web-based file sharing without large email attachments, and online collaboration tools. Box has over 100 million files stored across 300 terabytes of data in redundant data centers with 99.99% uptime. It offers solutions for individuals, small businesses, and large enterprises.
Security Essentials for the SMB IT Network (on a Shoestring Budget!) - Adam W...Spiceworks
This document discusses security issues that small businesses face and provides recommendations to address them. It notes that networks are large and complex, and that businesses have limited time and money for security. However, the size of a business does not correlate with the likelihood of a data breach. The document recommends pursuing simplicity, vigilance, consistency, and utilizing managed security services and affordable utilities to help protect against threats. It concludes by asking if the audience has any other questions.
Advanced IT and Cyber Security for Your BusinessInfopulse
Infopulse delivers advanced IT and cyber security and data protection services, ensuring financial, technical and strategic benefits for your business. Check out the presentation to learn more.
modern security risks for big data and mobile applicationsTrivadis
The document discusses modern security risks for big data and mobile applications. It covers past data breach incidents, privacy and protection issues with big data like lack of anonymity and profiling risks. For mobile applications, it outlines decomposition risks, bad permission definitions, and risks with unencrypted data both in transit and at rest. The presentation recommends focusing on security controls like access controls and monitoring for big data, and addressing vulnerabilities from permissions, encryption, and component interactions for mobile applications.
How future astronomy projects will generate enormous amounts of data, and what does that mean for astronomical data processing. Part of the virtual observatory course by Juan de Dios Santander Vela, as imparted for the MTAF (Métodos y Técnicas Avanzadas en Física, Advanced Methods and Techniques in Physics) Master at the University of Granada (UGR).
John Shaw, VP of Product management at Sophos, introduced us to the world of Project Galileo. What is Sophos doing to bring Network Security and Endpoint security together? How do we make these two pillars of IT security work together?
Building a Data Driven Culture and AI Revolution With Gregory Little | Curren...HostedbyConfluent
Building a Data Driven Culture and AI Revolution With Gregory Little | Current 2022
Transforming business or mission through AI/ML doesn't start with technology but with culture…and an audit. At least as much is true for the US Department of Defense (DoD), which presents significant modernization challenges because of its mission scope, expansive global footprint, and massive size - with over 2.8 million people, it is the largest employer in the world. Greg Little discusses how establishing the DoD’s annual audit became a surprising accelerator for the department’s data and analytics journey. It revealed the foundational needs for data management to run a $3 trillion in assets enterprise, and its successful implementation required breaking through deeply entrenched cultural and organizational resistance across DoD.
In this session, Greg will discuss what it will take to guide the evolution of technology and culture in parallel: leadership, technology that enables rapid scale and a complete & reliable data flow, and a data driven culture.
This document provides an overview of big data and Hadoop. It discusses that Hadoop is a software framework for distributed processing of large datasets across clusters of computers. It can handle terabytes or petabytes of data stored on hundreds or thousands of nodes. The document also summarizes key Hadoop concepts like HDFS for storage, MapReduce for processing, and YARN for resource management. It highlights top reasons for using big data and Hadoop like scalability, flexibility to handle all data formats, and ability to use inexpensive commodity hardware. Finally, it presents a typical big data architecture showing data sources, storage, processing, visualization, and machine learning capabilities.
R+Hadoop - Ask Bigger (and New) Questions and Get Better, Faster AnswersRevolution Analytics
The business cases for Hadoop can be made on the tremendous operational cost savings that it affords. But why stop there? The integration of R-powered analytics in Hadoop presents a totally new value proposition. Organizations can write R code and deploy it natively in Hadoop without data movement or the need to write their own MapReduce. Bringing R-powered predictive analytics into Hadoop will accelerate Hadoop’s value to organizations by allowing them to break through performance and scalability challenges and solve new analytic problems. Use all the data in Hadoop to discover more, grow more quickly, and operate more efficiently. Ask bigger questions. Ask new questions. Get better, faster results and share them.
Data, the way that we process it and store it, is one of many important aspects of IT. Data is the lifeblood of our organizations, supporting real-time business processes and decision-making. For our DevOps strategy to be truly effective we must be able to safely and quickly evolve production databases, just as we safely and quickly evolve production code. Yet for many organizations their data sources prove to be less than trustworthy and their data-oriented development efforts little more than productivity sinkholes. We can, and must, do better.
This presentation begins with a collection of agile principles for data professionals and of data principles for agile developers - the first step in working together is to understand and appreciate the priorities and strengths of the people that we work with. Our focus is on a collection of practices that enable development teams to easily and safely evolve and deploy databases. These techniques include agile data modeling, database refactoring, database regression testing, continuous database integration, and continuous database deployment.
We also work through operational strategies required of production databases to support your DevOps strategy. If data sources aren’t an explicit part of your DevOps strategy then you’re not really doing DevOps, are you?
The document discusses challenges for machine learning data storage and management. It notes that machine learning workloads involve large and growing data sizes and types. Proper data governance is also essential for ensuring trustworthy machine learning systems, through mechanisms like data lineage tracking and access control. Emerging areas like edge computing further complicate storage needs. Effective machine learning storage systems will need to address issues of data access speeds, management, reproducibility and governance.
Transforming and Scaling Large Scale Data Analytics: Moving to a Cloud-based ...DataWorks Summit
The Census Bureau is the U.S. government's largest statistical agency with a mission to provide current facts and figures about America's people, places and economy. The Bureau operates a large number of surveys to collect this data, the most well known being the decennial population census. Data is being collected in increasing volumes and the analytics solutions must be able to scale to meet the ever increasing needs while maintaining the confidentiality of the data. Past data analytics have occurred in processing silos inhibiting the sharing of information and common reference data is replicated across multiple system. The use of the Hortonworks Data Platform, Hortonworks Data Flow and other open-source technologies is enabling the creation of a cloud-based enterprise data lake and analytics platform. Cloud object stores are used to provide scalable data storage and cloud compute supports permanent and transient clusters. Data governance tools are used to track the data lineage and to provide access controls to sensitive data.
Securing Sensitive IBM i Data At-Rest and In-MotionPrecisely
Driven by a continuous stream of news about personal information stolen from major retailers and financial institutions, consumers and regulatory bodies are demanding more in terms of data protection and privacy. Personal data protection is required by government and industry regulations such as PCI, HIPAA, GDPR, FISMA and more. Data encryption provides another layer of protection around IBM i Db2 columns that contain sensitive data, and it’s never been easier since the introduction of FIELDPROC in IBM i 7.1. Other solutions are also available to remove sensitive data from servers entirely and to secure data in motion.
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• Tradeoffs between do-it-yourself and third-party solutions
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The document provides an overview of database, big data, and data science concepts. It discusses topics such as database management systems (DBMS), data warehousing, OLTP vs OLAP, data mining, and the data science process. Key points include:
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- OLTP systems are for real-time transactional systems, while OLAP systems are used for analysis and reporting of historical data.
- Data mining involves applying algorithms to large datasets to discover patterns and relationships. The data science process involves business understanding, data preparation, modeling, evaluation, and deployment
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Regulatory compliance mandates have historically focused on IT & endpoint security as the primary means to protect data. However, as our digital economy has increasingly become software dependent, standards bodies have dutifully added requirements as they relate to development and deployment practices. Enterprise applications and cloud-based services constantly store and transmit data; yet, they are often difficult to understand and assess for compliance.
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* Consolidating security and compliance controls
* Creating application security standards for development and operations teams
* Identifying and remediating gaps between current practices and industry accepted "best practices”
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Your database holds your company's most sensitive and important assets- your data. All those customers' personal details, credit card numbers, social security numbers- you can't afford leaving them vulnerable to any- outside or inside- breaches.
Introducing Trillium DQ for Big Data: Powerful Profiling and Data Quality for...Precisely
The advanced analytics and AI that run today’s businesses rely on a larger volume, and greater variety, of data. This data needs to be of the highest quality to ensure the best possible outcomes, but traditional data quality tools weren’t designed for today’s modern data environments.
That’s why we’ve developed Trillium DQ for Big Data -- an integrated product that delivers industry-leading data profiling and data quality at scale, in the cloud or on premises.
In this on-demand webcast, you will learn how Trillium DQ:
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Curiosity Software and RCG Global Services Present - Solving Test Data: the g...Curiosity Software Ireland
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Outdated test data management practices are today a sinkhole for testing and development time. They stifle release velocity, risk costly legislative non-compliance, and yet still do not provide the data needed to protect releases from damaging bugs. To achieve true quality at speed, the test data paradigm must shift. Enterprises must move beyond slowly copying large sets of production data to a limited number of out-of-date test environments.
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Read through our latest webinar held in conjunction with Google Enterprise all about Enterprise Best Practices and creating successful search applications using the Google Search Appliance 7.0. Search Technologies provides implementation and consulting services to Google search Appliance Customers. For further information, see http://www.searchtechnologies.com/google-search-appliance-services.html
http://searchtechnologies.com
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7. Big Data Architecture
No Single Silver Bullet
• Hadoop is already unsuitable for many Big
data problems
• Real-time analytics
• Cloudscale, Storm
• Graph computation
o Giraph and Pregel (Some examples graph
computation are Shortest Paths, Degree of
Separation etc.)
• Low latency queries
o Dremel
10. Input Validation and Filtering
• Input Validation
o What kind of data is untrusted?
o What are the untrusted data sources?
• Data Filtering
o Filter Rogue or malicious data
• Challenges
o GBs or TBs continuous data
o Signature based data filtering has limitations
How to filter Behavior aspect of data?
11. Granular Access Controls
• Designed for Performance, almost no
security in mind
• Security in Big Data still ongoing research
• Table, Row or Cell level access control gone
missing
• Adhoc Queries poses additional challenges
• Access Control is disabled by default
12. Insecure Data Storage
• Data at various nodes, Authentication,
Authorization & Encryption is challenging
• Autotiering moves cold data to lesser secure
medium
o What if cold data is sensitive?
• Encryption of Real time data can have
performance impacts
• Secure communication among nodes,
middleware and end users are disabled by
default
13. Privacy Concerns in Data Mining
and Analytics
• Monetization of Big Data generally involves
Data Mining and Analytics
• Sharing of Results involve multiple
challenges
o Invasion of Privacy
o Invasive Marketing
o Unintentional Disclosure of Information
• Examples
o AOL release of Anonymzed search logs, Users can
easily be identified
o Netflix faced a similar problem
14. Top 5 Best Practices
• Secure your Computation Code
• Implement access control, code signing, dynamic
analysis of computational code
• Strategy to prevent data in case of untrusted code
• Implement Comprehensive Input Validation
and Filtering
• Implement validation and filtering of input data, from
internal or external sources
• Evaluate input validation filtering of your Big Data
solution
15. Top 5 Best Practices
• Implement Granular Access Control
• Review Role and Privilege Matrix
• Review permission to execute Adhoc queries
• Enable Access Control
• Secure your Data Storage and Computation
• Sensitive Data should be segregated
• Enable Data encryption for sensitive data
• Audit Administrative Access on Data Nodes
• API Security
16. Top 5 Best Practices
• Review and Implement Privacy Preserving
Data Mining and Analytics
• Analytics data should not disclose sensitive
information
• Get the Big Data Audited
18. Big Data Architecture
Key Insights
• Distributed Architecture & Auto Tiering
• Real Time, Streaming and Continuous
Computation
• Adhoc Queries
• Parallel and Powerful Computation
Language
• Move the Code, Not the data
• Non Relational Data
• Variety of Input Sources
19. Top 5 Security Risks
• Insecure Computation
• End Point Input Validation and
Filtering
• Granular Access Control
• Insecure Data Storage and
Communication
• Privacy Preserving Data Mining and
Analytics
Editor's Notes
Partitioned, Distributed and Replicated among multiple Data Nodes
1000,s of Data nodes
Autotiering: Moving hottest data to high performance drive, coldest data to low performance, less secure drive