Dataguise & MapR: Action Items for the Financial Industry

Action Items for the
Financial Industry:
Addressing fraud and privacy
issues in Hadoop
May 1, 2014

Our Speakers
Jeremy Stieglitz
VP Product Management
Dataguise
Dale Kim
Director, Industry Solutions
MapR Technologies
5 Action Items for the Financial Services Industry – April 2014 2

Executive Summary
 Big Data has become priority #1 for
finance and large enterprise in 2014:
– Faster time to insights
– Increasingly touching $$$
– Real-time, Automation and On Demand
 Key challenges on Big Data
– Leveraging sensitive assets
– Automating protection techniques
– Maintaining high performance
– Ensuring scalability

Agenda
 Market overview: Hadoop in Financial Services
 Solution priorities
 5 action items
– Strategize – develop concrete goals
– Automate, automate, automate
– Foresee high rates of innovation
– Expand your community
– Explore the technology landscape

Hadoop in Financial Services
Hadoop addresses:
• Scale
• Infrastructure costs
• Anomaly detection
• Data archiving for
compliance
• Aggregated risk
• Data protection
• Etc.
Risk
Market Uncertainty
Regulations
Costs
Fraud
Security Attacks
Big Data

Business Challenge: Data Growth
 100% growth and 80% unstructured data by 2015
…finding and classifying sensitive data will get
harder
7
Exabytes
5 Action Items for the Financial Services Industry – April 2014

Compliance Universe
• Sarbanes-Oxley
• Frank-Dodd
• PCI-DSS
• HIPAA
• State Data Breach Laws (CA SB 1386)
• Data Privacy (International)
• EU Data Protection Directive
• Singapore Personal Data Protection Act
• Canadian Personal Information Protection
and Electronic Documents Act (PIPEDA)
• Germany’s Federal Data Protection Act
(BDSG)
• Great Britain Data Protection Act
• Data Privacy (USA)
• Ohio 1347.15
• California SB 1386
• Massachusetts Data Privacy Law
• Financial
• Basel III
• Gramm-Leach-Bliley Act (GLBA)
• J-SOX
• Technology Risk Management
Guidelines
• Health/Pharmaceutical
• NAIC Model Audit Rule (MAR)
• 21 CFR Part 11 (FDA)
• Energy
• North American Electric Reliability
Corp (NERC)
• Federal Energy Regulatory
Commission Regulations (FERC)
• Service Providers
• Statement on Auditing Standards
(SAS 70)
• Education
• Family Educational Rights and Privacy
Act (FERPA)
• Federal Information Security Management
Act (FISMA)
63 countries, 1200+ laws

9
Solution
Priorities

Solution Priorities
Performance
Compute Memory
Network I/ODisk
Software
If you skimp on
one, you impact
performance

Solution Priorities
Scale
Plan for growth,
beyond your simple
projections:
• Longer time
windows of data
• New data sources
• New use cases

Solution Priorities
Reliability Plan ahead with all
the right people --
devops, sys admins,
application
developers, etc.

Hadoop Security Framework
Perimeter
Guarding access to the
cluster itself
Technical Concepts:
Authentication
Network isolation
Data
Protecting data in the
cluster from
unauthorized visibility
Technical Concepts:
Encryption, Tokenization,
Data masking
 The 4 approaches to address security within Hadoop (Perimeter,
Data, Access, Visibility)
 MapR provides true multi-tenant Hadoop, along with authentication/
authorization controls
 Dataguise discovers & protects at the data layer and provides visibility
for audit reporting and data lineage
Perimeter
Guarding access to the
cluster itself
Technical Concepts:
Authentication
Network isolation
Access
Defining what users
and applications can do
with data
Technical Concepts:
Permissions
Authorization
Visibility
Reporting on where
data came from and
how it’s being used
Technical Concepts:
Auditing
Lineage

5 Actions You Should Take

#1: Strategize – Develop Concrete Goals
 Are you prepared for production?
– Expect stringent SLAs
– Plan for performance, scale, reliability,
and data security
 Additional use cases?
– Do not limit your possibilities
– Hadoop deployments typically handle
multiple business problems
 The ‘Aha’ vs. ‘Gotcha’ moment

#2: Automate, Automate, Automate
– Keep up with data growth
– Simplify critical tasks
– Reduce the risk of error with manual effort
Your Data will grow 6000% in six years. Your headcount will grow 1.5x

Payment Risk Management
at Major Credit Card Brand
 Multiple business apps: fraud
detection, risk analytics, cross sell
 High degree of automation
 “Silver”, “Gold” and “Platinum”
Hadoop domains with increasing
lockdown of customer data
17
“Our analytics business
draws on the
purchasing data of its
90 million credit card
holders across 127
countries.”

Hadoop
Payment Risk Management
at Major Credit Card Brand
Customer uses SFTP and
NFS to load data into
Hadoop. All data loaded
in the clear.
Dataguise masking runs as
MapReduce JAR (automatically, with
no programming required).
HDFS “Gold” Cluster
Any incremental updates to HDFS are
automatically protected. Credit card
firm uses access control to determine
access to private data
Dataguise masking
guarantees consistency
between sensitive
elements, ensuring credit
card group can run same
statistical distribution and
analytics without
exposure risk
u v
w
Omniture FilesCredit Card
Transactions (txt)
NFS copy
SFTP
HDFS Cluster “Silver”
x
Source Data

#3: Foresee High Rates of Innovation
 Hadoop benefits from many new innovations:
{Falcon, Kite, Storm, Mesos, Drill, Impala, Spark,
Shark, etc.}
 Design non-blocking technical choices
(especially in areas of high velocity).
– Many files formats you can use (txt, Snappy, Avro,
SequenceFile, RC, ORC, etc.)
– Make sure your security assumptions aren’t tied to
specific formats

#4: Expand Your Community
 Security is a “common good,” share ideas with:
– Different divisions at your firm
– Professionals in your network
– Competitors
 Who else are Hadoop/security experts?
– E-commerce
– Telecommunications
– Web 2.0
– Government

 Security analytics and fraud
detection
 Build statistical models to detect
fraud, and mine data to evaluate
suspicious activities
 Huge volumes of collected data to
identify fraud patterns required
massive scalability
Fraud Detection and Security Analytics
at Zions Bank
“We initially got into
centralizing all of our data
from an information
security perspective. We
then saw that we could
use this same environment
to help with fraud
detection.”

#5: Explore the Technology Landscape
• Deciding on the right technology is hard
• Undoing a bad decision is harder
• Talk to vendors, analysts, community
• Attend meet-ups, conferences
• Validate advice you get with proof points

MapR Distribution for Hadoop
BIG
DATA
BEST PRODUCT BUSINESS
IMPACT
Hadoop
Top Ranked
Production
Success

Dataguise: Market Leader in
Big Data Protective Intelligence (BDPI)
Only solution with Hadoop data
discovery
Best in class– data protection with
simplicity, scalability, and
automation
Business friendly to operators and
business analysts
24

Thank You
Jeremy Stieglitz
VP Product Management
jeremy@dataguise.com
Dale Kim
Director, Industry Solutions
dalekim@mapr.com

Dataguise & MapR: Action Items for the Financial Industry

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Dataguise & MapR: Action Items for the Financial Industry