Securing the Big Data Ecosystem

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1 Securing the Big Data Ecosystem SESSION ID: STU-T07A Davi Ottenheimer Senior Director of Trust,

2 COWS NOT PETS ( ) (xx) / \/ / * ---- ^^ ^^ Systematic Treatment of Illness Easily Identified Routine Treatment Minimum Judgment 1. Identify Sick Cattle ASAP 2. Keep Adequate Records 3. Evaluate Daily Sick Cattle 4. Adapt Until Noted Improvement 2

3 2012 PRESENTATION 1854: London Cholera Death Scale 1854: London Cholera Death Polygons 2012: Data Breach Investigations Source Observation 2010: Rinderpest 2013: AIDS 3

4 4 https://secure.flickr.com/photos/boston_public_library/ / DATA

5 BIG DATA 5

6 OBSTACLES TO BIG DATA Slow Ingest and Process Time Isolated Analysis Untapped Sources 6

7 PATHS TO BIG DATA Speedy Ingest and Process Time Analysis of Data Lakes Access to Sources 7

8 CIO SURVEY: TOP CONCERNS 54% What to Collect 85% How to Analyze Sources: Barclays September 2013 CIO Survey, KPMG January 2014 CIO/CFO Survey 8

9 NEW ERA OF DATA INDUSTRIALIZATION 54% What to Collect Data Streams to Data Lakes Save Everything (Lake Conservation) Indirect Communications - Internet of Things 85% How to Analyze (26 billion devices by 2020) No Two Anomalies Alike Indicators of Leaks, Tampering or Loss 9

10 WHY TYPICAL SOLUTIONS DON T FIT

11 CONTROLS AND DE-IDENTIFICATION LOCATION NAME UNIQUE ID# DATE >STATE NO NAME TEMP ID YEAR IDENTIFIABLE ANONYMOUS Role-Based Access Controls (AAA) Scrubbing / Substitution Encryption / Secure-Erase k-anonymity In-Memory Processing Limits (Classification) CONTROL DE-IDENTIFICATION

12 DE-IDENTIFICATION HURDLES FEW CHARACTERISTICS NEEDED Latanya Sweeney, % of U.S. IDs Unique by Zip+Sex+DOB 53% of U.S. IDs Unique by City+Sex+DOB State / Year Advised Minimums Voter Reg. Compared To Group Insurance Commission (GIC) Data 2014: Neighbor Identity for $ : MA Governor Identity for $20 Cambridge Voters GIC Customers Birth Date Gender Zipcode 12

13 EXAMPLE: WASTEWATER ANALYSIS Meta, Ripples, Tails, Exhausts, Shadows, etc. 1.5B gal/day Chicago Environment Disease Drugs? Know estimated numbers of people served by each waste water treatment plant Can back-calculate daily [drug] loads - Dr Kasprzyk-Horder 13

14 EXAMPLE: WASTEWATER ANALYSIS Croatia Italy Finland London Oregon Canada 14

15 ON THE OTHER HAND: CONTROLS Authentication Authorization Encryption Caveats All or Nothing (Security Required to Communicate) Rolling Upgrades Impossible

16 DATA CONTROL DESIGN Brakes Suspension Horn Mirrors Seatbelts 16

17 BIG DATA CONTROL DESIGN TRUST REDEFINED Brakes Suspension Horn Mirrors Seatbelts Threat Avoidance Checklists (Rapid Repairs) 10X More Data, Accessible 24x7x365 17

18 SIMPLE CHECKLISTS INTELLIGENT ANALYSIS PCI DSS Requirement 2.1: Always change vendor-supplied defaults before installing 18

19 THREAT ANALYSIS MATURITY SCALE BINARY RANKED MEANING ZERO POINT EXACT ERROR MARGIN CAVEAT: NO FISH IN TOO CLEAR WATER INTELLIGENCE 19

20 INTELLIGENCE REDEFINES CONTROLS Long-Term <NOUN> Users Apps Content API Alert & Report Investigate & Analyze Visualize Record Sort Collect <ADJ> Time Alias Property Respond GRC Devices Networks Real-Time 20

21 TRUST REDEFINES BIG DATA Annual Savings 33 Years of Time US$8,000, Fuel Tanker Trucks 21

22 22 ARCHITECTURE AND USE CASES

23 NEW WAVES OF BIG DATA TECHNOLOGY Hive Pig Mahout Behavior MapReduce R Sentiment Business Hawq Pivotal Predictive Hadoop Sqoop SAS SPSS Network Simulation Objectives Data Analytics Reporting 23

24 TYPICAL ARCHITECTURE AND CONTROL Data Shared Nodes Distributed Clients Unauthenticated Access Controls Open Web Services Open Networks Open SQOOP (DB SYNC) INTERFACES (REPORTING) PIG (PROCEDURAL) MAP-REDUCE (PROGRAMMING MODEL) HIVE (DECLARATIVE) HBASE (RANDOM R/W) HADOOP DISTRIBUTED FILE SYSTEM (HDFS) PROCESSING 24

25 PROCESSING ROLES Client Client MapReduce HDFS Masters Job Tracker Name Node Name Node (checkpoint) 2 nd Slaves 25

26 PROCESSING AUTOMATION CLUSTER 1 Admin : 30,000+ Nodes B3 B2 switch switch job tracker name node name node client A A1 B1 switch A2 A3 switch 2 nd client B switch Rack 1 Rack 2 Rack 3 Rack 4 Rack n (0.5 PB) 26

27 PROCESSING PATHS Splits Splits Split Splits Splits Split JSON Splits Splits Split Job Tracker Task NameNode Data Node HDFS Block Data Node Data Node HDFS Block HDFS Block Data Node Data Node HDFS Block HDFS Block RPC Read REDUCE MAP Data 27 Output Output Files File

28 TRUST DELEGATION VS. BEHAVIOR Runaway Job! Kill -9 Job Tracker Name Node Task Tracker 28

29 NEW AND DIFFERENT WAYS TO MANAGE RISK

30 DATA IS THE NEW CENTER OF GRAVITY SOCIAL DATA CLOUD MOBILE 30

31 TRUST REDFINED: SPACE-TIME BENDS BECAUSE GRAVITY 31

32 TRUSTED ARCHITECTURE Enhanced DB Services Resource Management & Workflow HBase ANSI SQL + Analytics Xtension Catalog Query Framework Hadoop Services Virtualization (HVE) Optimizer Dynamic Pipeline Pig, Hive Mahout Map Reduce Command Center Configure Deploy Yarn Zookeeper HDFS DataLoader Monitor Manage Sqoop Flume Apache Non-Apache 32

33 INTELLIGENCE-DRIVEN SECURITY EASY, ROUTINE & MINIMUM JUDGMENT 33

34 Securing the Big Data Ecosystem THANK YOU! SESSION ID: STU-T07A Davi Ottenheimer Senior Director of Trust, 2/25/14 (Tuesday) 1:20 PM - West 3012

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