Big Data Everywhere Chicago: The Big Data Imperative -- Discovering & Protecting Sensitive Data in Hadoop (Dataguise)
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The document discusses the challenges and solutions for data security in Hadoop across various sectors, including banking, healthcare, and technology. It emphasizes the importance of data-centric protection approaches, compliance with regulations, and automation in managing sensitive data. The text also outlines case studies demonstrating successful implementations of data protection measures for credit card firms and healthcare organizations.
Big Data Everywhere Chicago: The Big Data Imperative -- Discovering & Protecting Sensitive Data in Hadoop (Dataguise)
- 1. © 2014 Dataguise Inc. All rights reserved. Discovering & Protecting Sensitive Data in Hadoop jeremy@dataguise.com
- 2. Goals For Today Big Data for banking, healthcare, tech, govt, education, etc. need data security (But few have workable approaches in production today) Hadoop security approaches (What works and doesn’t work from the past, challenges in the present) Real world case studies (data-centric protection) Credit card security Healthcare data lake (Data-as-a-Service) Product analytics in the cloud © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 2
- 3. Market Overview © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 3
- 4. Data Growth • 100% growth and 80% unstructured data by 2015 …finding and classifying sensitive data will get harder © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 4 Exabytes
- 5. Real-world unstructured data scenarios Web comment fields and customer surveys, CRM data Patient and doctor medical data in emails, PDFs, doctor’s notes © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 5 Voice-to-txt files in Hadoop for customer service optimization; Log data from wellheads and oil drilling sensors Web e-Commerce Pay System
- 6. From%2012%to%2020,%enterprise%Big% Data%will%grow%7500%% in%next%6;8%yrs%% % © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL IT%headcount%for% Big%Data%will%grow% 1.5x% The Importance of Automation
- 7. Why Security in Big Data Vertical Refine Explore Enrich Retail & Web • Log Analysis Site Optimization • Social Network Analysis © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL • Dynamic Pricing • Session & Content Optimization Retail • Loyalty Program Optimization • Brand & Sentiment Analysis • Dynamic Pricing/ Targeted Offer Intelligence • Threat Identification • Person of Interest Discovery • Cross Jurisdiction Queries Finance • Risk Modeling & Fraud Identification • Trade Performance Analytics • Surveillance & Fraud Detection • Customer Risk Analysis • Real-time upsell, cross sales marketing offers Energy • Smart Grid: Production Optimization • Grid Failure Prevention • Smart Meters • Individual Power Grid Manufacturing • Supply Chain Optimization • Customer Churn Analysis • Dynamic Delivery • Replacement Parts Healthcare & Payer • Electronic Medical Records (EMPI) • Clinical Trials Analysis • Insurance Premium Determination
- 8. Why Security in Big Data Vertical Refine Explore Enrich Retail & Web • Log Analysis Site Optimization • Social Network Analysis © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL • Dynamic Pricing • Session & Content Optimization Retail • Loyalty Program Optimization • Brand & Sentiment Analysis • Dynamic Pricing/ Targeted Offer Intelligence • Threat Identification • Person of Interest Discovery • Cross Jurisdiction Queries Finance • Risk Modeling & Fraud Identification • Trade Performance Analytics • Surveillance & Fraud Detection • Customer Risk Analysis • Real-time upsell, cross sales marketing offers Energy • Smart Grid: Production Optimization • Grid Failure Prevention • Smart Meters • Individual Power Grid Manufacturing • Supply Chain Optimization PCI or Financial • Customer Churn Analysis • Dynamic Delivery • Replacement Parts Healthcare & Payer • Electronic Medical Records (EMPI) • Clinical Trials Analysis • Insurance Premium Determination Privacy data PCI or Financial Personal Health (PHI) Personal Health (PHI) Privacy data Personal Health (PHI) Privacy data Privacy PdCaIt ao r Financial PCI or Financial Privacy data
- 9. Three Critical Considerations 1. Ensuring Compliance • The Big Ps (PCI, HIPAA, Privacy), data residency, FERPA,FISMA, FERC , etc. • 1200 laws in 63 countries 2. Reducing Breach Risk 3. Quantifying both 1. How much sensitive data? (“un-announced”) 2. Who is adding? (ad hoc user directories) 3. Who is accessing? (sharing, selling, re-purposing) © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 9
- 10. Lab Project • Hadoop as R&D • Strictly data science • Zero $$$ or selection of Distribution • Zero recognition of sensitive data or exposure Proof Stage • Achieving value • Data lake cost savings • Line of business ownership • Nodal expansion • Security elements? (unknown to InfoSec) ROI Validity • ROI and TCO validity • Distribution selection and purchase • The Security ‘A- Ha’ moment • Solved with legacy or penalty box Hadoop © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL On Demand Hadoop • Full scale production • Ad hoc new uses • Go Faster: Spark, Kafka • Security sanctified The Evolution of Hadoop Projects
- 11. On-Demand Hadoop. • Without adequate sensitive data protection, customers left to “Penalty Boxing” Hadoop » “Security zones” imposed by InfoSec » Slows business, costly and cumbersome • Data-centric protection can set those assets free © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 11
- 12. Data Protection In Hadoop © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 12
- 13. Security in Hadoop In Summary • Like Cloud, Mobile, Virtualization… Big Data drives fundamental new rules in security » Ad hoc computing, wide open data sets » Extended users and usages, sharing and selling » 3 Vs moving to 6 Vs (automation, non-blocking) • Problem #1 is compliance » Reporting/auditing/monitoring as/more important than data security • Data-centric protection can help © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 13
- 14. Hadoop Security Framework Access' Defining%what%users% and%applicaHons%can%do% with%data% Technical'Concepts:' Permissions% AuthorizaHon% Perimeter' % Guarding%access%to%the% cluster%itself% % %%% Technical'Concepts:' AuthenHcaHon% Network%isolaHon% % Perimeter' Guarding%access%to%the% cluster%itself% % Technical'Concepts:' AuthenHcaHon% Network%isolaHon% % ReporHng%on%where% data%came%from%and% how%it’s%being%used% % Technical'Concepts:' © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL Data' ProtecHng%data%in%the% cluster%from% unauthorized%visibility% % Technical'Concepts:' EncrypHon,%TokenizaHon,% Data%masking% % Visibility' AudiHng% Lineage% % • The 4 approaches to address security within Hadoop (Perimeter, Data, Access, Visibility) • Dataguise discovers & protects at the data layer and provides visibility for audit reporting and data lineage
- 15. Kerberos on Hadoop • Kerberos (developed at MIT) has been the de-facto standard for strong authentication/authz » Protection against user and service spoofing attacks, and allows for enforcement of user HDFS access permissions • What does Kerberos Do? » Establishes identity for clients, hosts, and services » Prevents impersonation, passwords are never sent over the wire » Tickets grant cryptographic “permissions” to resources • Kerberos is core of authentication in native Apache Hadoop from 2010 » Used for access ecosystem services HDFS, JT, Oozie., for server to server traffic auth. etc. BUT complex to manage! » Lots of steps for example: http://www.cloudera.com/content/cloudera--content/cloudera-- docs/CDH4/4.3.0/CDH4--Security--Guide/cdh4sg_topic_3.html © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 15 Access' Defining%what%users% and%applicaHons%can%do% with%data% % Technical'Concepts:' Permissions% AuthorizaHon% %
- 16. MapR Improvements on Auth/Authz • Vastly simpler » But no requirements for Kerberos in core » Identity represented using a ticket which is issued by MapR CLDB servers (Container Location DataBase) » Core services secured by default • Easier integration » User identity independent of host or operating system » Local to MapR (no external Kerberos required) • Faster » Leverage Intel accelerated hardware crypto © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 16
- 17. Elements of Data Centric Protection • 1. Identify which elements you want to protect via: » Delimiters (structured data), name-value pairs (semi-structured) or data discovery service (unstructured) • 2. Automated Protection Options: » Automatically apply protection via: » Format preserving encryption (FPE) » Masking (replace, randomize, intellimask, static) » Redaction (nullify) • 3. Audit Strategy » Sensitive data protection/access/lineage © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 17
- 18. Discovery • Within HDFS » Search for sensitive data per company policy – PII, PCI,… » Handle complex data types such as addresses » Process incrementally (default) to handle only the new content • In-flight » Processing data on the fly as they are ingested into Hadoop HDFS » Plug-in solution for FTP, Flume, Sqoop » Search for sensitive data per policy – PII, PCI, HIPAA… » NEXT UP: Kafka © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 18
- 19. How Discovery Works © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 19 • MapReduce or Flume/FTP/Sqoop Agent » Root directories and drill downs » Can scan entire dataset or incrementally (watermarking) • Runs pattern, logic, context, algorithm, and ontology filters • Can utilize white/black lists and reference sets
- 20. Protection Measures • Protection plan should start with cutting » What data can we delete/cut? » What data can be redacted? » Masking choices • Consistency • Realistic looking data • Partial reveal (Intellimask) Credit Card # 4541 **** **** 3241 • What data needs reversibility © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 20
- 21. Encryption “vs” Masking • Encryption: + Reversible + Trusted with security proofs + The first hammer + De-centralized architectures - Complex - Key management - Useless without robust authentication and authorization - Data value destruction - Needs both encrypt-decrypt tooling © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 21 • Masking: + Highest security + Realistic data + Range and value preserving + Once and done +Scale-out and distributed + No performance impact on usage + Zero need for authentication and authorization and key management - Not as well marketed - Not reversible
- 22. Encryption “vs” Masking • Masking: + Highest security + Realistic data + Range and value preserving + Format-preserving and partial reveals +Scale-out and distributed + No performance impact on usage + Zero need for authentication and authorization and key management - Not as well marketed - Not reversible - Perceived to grow data • Encryption: + Reversible + Trusted with security proofs + Format-preserving and partial reveals +Scale-out and distributed + The first hammer + De-centralized architectures - Complex - Key management - Useless without robust authentication and authorization - Data value destruction © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 22 The fundamental decision between masking and encryption comes down to reversibility: Some elements in analytics must resolve to original: (e.g. 66.249.22.145 or $34,332.12) Some elements ideal for psuedonyms: Social Security Numbers Credit Card Numbers Names
- 23. Real-World Performance • Leveraging the power of MapReduce to run distributed encryption or masking • Data volume: 2.2 TB • Run Time: 23 min • Sensitive Data %: 8/50 Columns in 2.2 Bn rows • Run on 360 node MapR system • In old-word database technology, this would type of job would have taken days/week(s) © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 23
- 24. Audit Strategy • Essential to all goals: Compliance, breach protection, visibility and metrics • Avoids the “gotcha” moment » Show all sensitive elements (count, location) » Remediation applied » Dashboard for fast access to critical policies and drill-downs for file and user action © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 24
- 25. How It works: Detection and Protection In-flight or @Rest RDBMS Xaction Data warehouse Site WEB FTP FlDugmFel uAmgee nt Plug-in DgFlume Agent 1. Detect sensitive data 2. Protect applying masking/encryption policies Production Cluster © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL Hadoop API Discover/ Mask/Encrypt DgHDFS Agent 1. Detect sensitive data 2. Protect applying masking/encryption policies Hadoop API DGHive, HDFS bulk decryption/Java app 1. Selective decryption based on user/role and policy 1 Data Discovery and protection while loaded into HDFS 2 Data masked or encrypted in HDFS with Map/Reduce job 3 Users can now access data DGDiscover-Masker 1. In DB (Oracle, SQL.. SharePoint, Files) 2. Protect applying masking/encryption policies Sqoop DgScoop Agent 1. Detect sensitive data 2. Protect applying masking/encryption policies
- 26. Case Studies © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 26
- 27. Protecting sensitive data in top credit card firm Source Data Protection Analysis Credit Card Transactions Omniture Files © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL Selective access to sensitive data based on role and app 27 Objectives ! Consolidate existing payment risk analysis inside high-scale, lower cost Hadoop ! Provide tiered access authorization for multiple business apps (fraud, risk, cross-sell Solution ! MapR Hadoop for single, reliable, high performance data analysis platform ! Dataguise consistent masking enables analysis and unique index key values for de-identified data ! Unique ability to output protected data in adjacent column or appended with delimiter inside existing column to protect data while governing access via authorization rules Incremental updates to HDFS automatically protected Results Benefits • Continuous real-time protection (job runs every 5 mins on ingest) • Analytics draws on the secure purchasing data of 90 million credit card holders across 127 countries
- 28. Protecting personal health info (PHI) in aggregate data lake DG FTP Agent SQL Data FTP Health Records © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL HDFS Authorization controlled through group membership in Active Directory 28 Objectives ! Reduce costly and preventable readmission, decrease mortality rates, and improve the quality of life for patients ! Internal data service model DAaaS (Data Architecture as a Service) Solution ! Solution needs to protect structured and unstructured source data in database, data warehouse, and flat file structures ! Customer required customization of encryption and key management to fit into their existing corp infrastructure and security policies ! Dataguise dashboard gives admins easy way to identify directories/files containing sensitive data Results Benefits • Delivered a cost-effective and easy way to determine where sensitive data resides within the cluster, and how it’s been protected ! Seamless access to encrypted data from a variety of data access methods {Hive, Pig, Analytic tools}
- 29. Global Tech Product Analytics DG Flume Agent © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 29 Objectives ! Aggregate logging data (product, usage, user configuration) for all smartphones worldwide t ! De-identify personal user info to ensure privacy and compliance with European/US Privacy Solution • Customer routes all device logging data into 7 Global AWS clouds • Uses Dataguise Flume agent to protect all sensitive data being written to Amazon S3 • Runs Dataguise in AWS, also utilizes Dataguise EMR security agents to selectively decrypt for authorized analytics in AWS Results Benefits Apache Flume • On-demand Hadoop for product analytics, user behavior, supply chain optimization • High scale-out, high performance and high scale-out paramount • 100% cloud based security Virtualized DG Secure Protected Data Amazon S3 Smartphone Device Log Collectors AWS Clouds in Korea, Singapore, US (3), UK, and Ireland
- 30. Hadoop Data-Protection Checklist © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 30 Discover sensitive data Automate protective measures Integrate into Hadoop authorization With continuous real-time tracking Dashboards, Reports Auditing Automated Risk Assessment/Scoring Automated inference protection (roadmap)
- 31. Thank You © 2014 Dataguise Inc. All rights reserved. COMPANY CONFIDENTIAL 31 Jeremy Stieglitz VP Products jeremy@dataguise.com