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Cloud - Security - Big Data

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Raffael Marty
Raffael Marty

A quick overview on the topics of cloud, big data, and IT security.

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Raffael Marty, CEO Cloud and Big Data iTech Gulf, Kingdom of Bahrain April 10th, 2014
Security. Analytics. Insight.2 The Intersection of Three Domains Big DataCloud Security
Security. Analytics. Insight.3 Why use the Cloud? • Increased efficiency due to better use of resources (elasticity) • More predictable cost • Design with redundancy and failure tolerance needed • Automation necessary, but that’s a good thing • Outsource non core capabilities / responsibilities ! New Use Cases / New Applications • Enables new business models (pay as you go) • Access to large compute and storage Cloud
Security. Analytics. Insight.4 • Storage has gotten cheap ($0.01 / GB) • Access to large compute on demand —> Use larger data to gain insights ! ! • Search engines • NoSQL / NewSQL / Key-value stores • Map Reduce [really parallel computing (HPC)] • On commodity hardware • Bring compute to the data Cloud has Enabled Big Data Big Data = New technologies to deal with large amounts of data
Security. Analytics. Insight.5 Big Data Has Changed Data Analytics “memory has become the new hard disk, hard disks are the tapes of years ago” -- unknown source
Security. Analytics. Insight.6 Questions to answer: • “Show me all documents mentioning ‘pixlcloud’”? • “Which document contains the most relevant information about ‘bahrain’”? Big Data Stack - Search Raw Data (Documents)Storage Search Interface REST API Web Interface Index
Security. Analytics. Insight.7 Questions to answer: “Visualize user activity in clusters based on their behavior.” Big Data Stack - Large-Scale Processing Map Reduce Distributed Filesystem Impala, Stinger, HawQ SQL Layer Analytics Visualization 0xdata, Revolution Tableau Hadoop FileSystem (HDFS)
Security. Analytics. Insight. We can store and process PB of data … • How to analyze the data? What algorithms, what technology, … • How to get to insights? • How to do data science on all of that data? • Adopting machine learning / data mining to 
 larger amounts of data is hard • Setups can get complicated - many components 8 Big Data - Limits
Security. Analytics. Insight.9 Data Storage and Access • Isolation management / data multi-tenancy • Data retention issues • Data dispersal and international privacy laws • EU Data Protection Directive and U.S. Safe Harbor program • Exposure of data to foreign governments and data subpoenas
 Processing Infrastructure • Application multi-tenancy • Reliance on hypervisors • Process isolation / Application sandboxes What Has Changed
Security. Analytics. Insight.10 Trusting vendor’s security model • Obtaining support for investigations • Inability to respond to audit findings Risk = (Threat, Vulnerability) • Hypervisor escaping • Stored credentials • Web ubiquity • Shared resources • Using external services - Proprietary implementationscan’tbeexamined - Availability of services - Confidentialityof services • Malicious insiders • Data storage
Security. Analytics. Insight.11 The Good • Cloud homogeneity makes security auditing/testing simpler • Clouds enable automated security management • Redundancy / Disaster Recovery • Distributed denial of service (DDoS) protection The Bad • Loss of physical control • No more network-based Intrusion Detection • No data leak prevention (DLP) • Little network routing mechanisms • Reliance on third parties Changes in Security
Big Data For Cyber Security & Intelligence "There are 1000 ways for someone to steal information. If we knew how, we could prevent it. Visualization helps find that one way.” • Visualization to gain insight into big data • Cloud as an intelligence and data sharing platform • Security through insights • Security as a profit center
13 raffael.marty@pixlcloud.com

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Cloud - Security - Big Data

  • 1. Raffael Marty, CEO Cloud and Big Data iTech Gulf, Kingdom of Bahrain April 10th, 2014
  • 2. Security. Analytics. Insight.2 The Intersection of Three Domains Big DataCloud Security
  • 3. Security. Analytics. Insight.3 Why use the Cloud? • Increased efficiency due to better use of resources (elasticity) • More predictable cost • Design with redundancy and failure tolerance needed • Automation necessary, but that’s a good thing • Outsource non core capabilities / responsibilities ! New Use Cases / New Applications • Enables new business models (pay as you go) • Access to large compute and storage Cloud
  • 4. Security. Analytics. Insight.4 • Storage has gotten cheap ($0.01 / GB) • Access to large compute on demand —> Use larger data to gain insights ! ! • Search engines • NoSQL / NewSQL / Key-value stores • Map Reduce [really parallel computing (HPC)] • On commodity hardware • Bring compute to the data Cloud has Enabled Big Data Big Data = New technologies to deal with large amounts of data
  • 5. Security. Analytics. Insight.5 Big Data Has Changed Data Analytics “memory has become the new hard disk, hard disks are the tapes of years ago” -- unknown source
  • 6. Security. Analytics. Insight.6 Questions to answer: • “Show me all documents mentioning ‘pixlcloud’”? • “Which document contains the most relevant information about ‘bahrain’”? Big Data Stack - Search Raw Data (Documents)Storage Search Interface REST API Web Interface Index
  • 7. Security. Analytics. Insight.7 Questions to answer: “Visualize user activity in clusters based on their behavior.” Big Data Stack - Large-Scale Processing Map Reduce Distributed Filesystem Impala, Stinger, HawQ SQL Layer Analytics Visualization 0xdata, Revolution Tableau Hadoop FileSystem (HDFS)
  • 8. Security. Analytics. Insight. We can store and process PB of data … • How to analyze the data? What algorithms, what technology, … • How to get to insights? • How to do data science on all of that data? • Adopting machine learning / data mining to 
 larger amounts of data is hard • Setups can get complicated - many components 8 Big Data - Limits
  • 9. Security. Analytics. Insight.9 Data Storage and Access • Isolation management / data multi-tenancy • Data retention issues • Data dispersal and international privacy laws • EU Data Protection Directive and U.S. Safe Harbor program • Exposure of data to foreign governments and data subpoenas
 Processing Infrastructure • Application multi-tenancy • Reliance on hypervisors • Process isolation / Application sandboxes What Has Changed
  • 10. Security. Analytics. Insight.10 Trusting vendor’s security model • Obtaining support for investigations • Inability to respond to audit findings Risk = (Threat, Vulnerability) • Hypervisor escaping • Stored credentials • Web ubiquity • Shared resources • Using external services - Proprietary implementationscan’tbeexamined - Availability of services - Confidentialityof services • Malicious insiders • Data storage
  • 11. Security. Analytics. Insight.11 The Good • Cloud homogeneity makes security auditing/testing simpler • Clouds enable automated security management • Redundancy / Disaster Recovery • Distributed denial of service (DDoS) protection The Bad • Loss of physical control • No more network-based Intrusion Detection • No data leak prevention (DLP) • Little network routing mechanisms • Reliance on third parties Changes in Security
  • 12. Big Data For Cyber Security & Intelligence "There are 1000 ways for someone to steal information. If we knew how, we could prevent it. Visualization helps find that one way.” • Visualization to gain insight into big data • Cloud as an intelligence and data sharing platform • Security through insights • Security as a profit center