1. Understanding Your Data Flow
Using Tokenization to Secure Data
Ulf Mattsson
CTO Protegrity
1

2. 2

3. 03

4. Ulf Mattsson, CTO Protegrity
• 20 years with IBM Development & Global Services
• Started Protegrity 1994
• Inventor of 22 patents – Encryption and
Tokenization
• Member of
– PCI Security Standards Council (PCI SSC)
– American National Standards Institute (ANSI) X9
– International Federation for Information Processing (IFIP) WG
11.3 Data and Application Security
– ISACA (Information Systems Audit and Control Association)
– Information Systems Security Association (ISSA)
– Cloud Security Alliance (CSA)
4

5. Session topics
• Discuss threats against data
• Review solutions for securing data
– Evaluate different options for data tokenization and
encryption
• Review case studies
– Discuss how to stay out of scope for PCI DSS
• Review data protection cost efficiency
– Introduce a business risk approach
• Discuss cloud and outsourced environments
5

6. THIEVES ARE
STEALING
OUR DATA!
6

7. Albert Gonzalez
20 Years In US Federal Prison
US Federal indictments:
1. Dave & Busters
2. TJ Maxx
3. Heartland HPS
•Breach expenses
$140M
Source: http://en.wikipedia.org/wiki/Albert_Gonzalez
Source: http://www.youtube.com/user/ProtegrityUSA
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8. What about Breaches & PCI?
Was Data Protected?
9: Restrict physical access to cardholder data
5: Use and regularly update anti-virus software
4: Encrypt transmission of cardholder data
2: Do not use vendor-supplied defaults for security parameters
12: Maintain a policy that addresses information security
1: Install and maintain a firewall configuration to protect data
8: Assign a unique ID to each person with computer access
6: Develop and maintain secure systems and applications
10: Track and monitor all access to network resources and data
11: Regularly test security systems and processes
7: Restrict access to data by business need-to-know
3: Protect Stored Data
%
0 10 20 30 40 50 60 70 80 90 100
Based on post-breach reviews. Relevant Organizations in Compliance with PCI DSS. Verizon Study
8

9. WHAT TYPES OF DATA
ARE UNDER ATTACK
NOW?
9

10. What Data is Compromised?
Personal information (Name, SS#, Addr, etc.)
Payment card numbers/data
Unknown (specific type is not known)
Medical records Medical
Classified information
Trade secrets
Copyrighted/Trademarked material
System information (config, svcs, sw, etc.)
Bank account numbers/data
Sensitive organizational data (reports, plans, etc.)
Authentication credentials…
0 20 40 60 80 100 %120
By percent of records. Source: 2012, http://www.verizonbusiness.com/Products/security/dbir/
10

11. Today “Hacktivism” is Dominating
Activist group
Organized criminal group
Relative or acquaintance of employee
Former employee (no longer had access)
Unaffiliated person(s)
Unknown
0 10 20 30 40 50 60 70
%
By percent of records
Source: 2012, http://www.verizonbusiness.com/Products/security/dbir/
11

12. Growing Threat of “hacktivism” by
Groups such as Anonymous
Attacks by Anonymous include
• 2012: CIA and Interpol
• 2011: Sony, Stratfor and HBGary Federal
Source: 2012, http://www.verizonbusiness.com/Products/security/dbir/, http://en.wikipedia.org/wiki/Timeline_of_events_involving_Anonymous
12

13. Let’s Review Some Major Recent Breaches
April 2011 May 2011 Jun 2011 Jul 2011 Aug 2011
Attack
Type,
Time
and
Impact
$
Source: IBM 2012 Security Breaches Trend and Risk Report
13

14. The Sony Breach & Cloud
• Lost 100 million passwords and personal details
stored in clear
• Spent $171 million related to the data breach
• Sony's stock price has fallen 40 percent
• For three pennies an hour, hackers can rent
Amazon.com to wage cyber attacks such as the
one that crippled Sony
• Attack via SQL Injection
14

15. SQL Injection Attacks are Increasing
25,000
20,000
15,000
10,000
5,000
Q1 2011 Q2 2011 Q3 2011
Source: IBM 2012 Security Breaches Trend and Risk Report
15

16. WHAT IS
SQL INJECTION?
16

17. What is an SQL Injection Attack?
SQL Command Injected
Application
Data
Store
17

18. WHO IS
THE NEXT TARGET?
18

19. New Industry Groups are Targets
Accommodation and Food Services
Retail Trade
Finance and Insurance
Health Care and Social Assistance
Other
Information
0 10 20 30 40 50 60 %
By percent of breaches
Source: 2012, http://www.verizonbusiness.com/Products/security/dbir/
19

20. The Changing Threat Landscape
 Some issues have stayed constant:
 Threat landscape continues to gain sophistication
 Attackers will always be a step ahead of the defenders
 We are fighting highly organized, well-funded crime
syndicates and nations
 Move from detective to preventative controls needed
Source: http://www.csoonline.com/article/602313/the-changing-threat-landscape?page=2

21. How are Breaches Discovered?
Notified by law enforcement
Third-party fraud detection (e.g., CPP)
Reported by customer/partner affected
Brag or blackmail by perpetrator
Unknown
Witnessed and/or reported by employee
Other(s)
Internal fraud detection mechanism
Financial audit and reconciliation process
Log analysis and/or review process
Unusual system behavior or performance
0 10 20 30 40 50 60 70 %
By percent of breaches . Source: 2012, http://www.verizonbusiness.com/Products/security/dbir/
21

22. WHERE IS
DATA LOST?
22

23. What Assets are Compromised?
Database server
Web/application server
Desktop/Workstation
Mail server
Call Center Staff People
Remote Access server
Laptop/Netbook
File server
Pay at the Pump terminal User devices
Cashier/Teller/Waiter People
Payment card (credit, debit, etc.) Offline data
Regular employee/end-user People
Automated Teller Machine (ATM)
POS terminal User devices
POS server (store controller)
0 20 40 60 80 100 % 120
By percent of records
Source: 2012, http://www.verizonbusiness.com/Products/security/dbir/
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24. Hacking and Malware are Leading
Threat Action Categories
Hacking
Social
Misuse
Environmental
0 50 100 % 150
By percent of records
Source: 2012, http://www.verizonbusiness.com/Products/security/dbir/
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25. Thieves Are Attacking the Data Flow
Application Application
025

26. THIS IS A
CATCH 22!
26

27. Thieves Can't Steal What's Not There:
Fake Data
Application Application
???-??-????
27

28. HOW CAN WE SECURE
THE DATA FLOW?
28

29. Securing The Data Flow with Tokenization
Retail Bank
Store
Payment 9999 9999 Corporate
Network Systems
29

30. WHAT HAS
THE INDUSTRY
DONE TO
SECURE DATA?
30

31. What Has The Industry Done?
Total Cost of Ownership
Total Cost of 1. System Integration
Ownership 2. Performance Impact
3. Key Management
Strong Encryption:
High - 4. Policy Management
3DES, AES …
5. Reporting
6. Paper Handling
Format Preserving Encryption: 7. Compliance Audit
FPE, DTP … 8. …
Basic Tokenization
Vaultless Tokenization
Low -
I I I I Time
1970 2000 2005 2010
31

32. Case Study: Large Chain Store
Why? Reduce compliance cost by 50%
– 50 million Credit Cards, 700 million daily transactions
– Performance Challenge: 30 days with Basic to 90 minutes with Vaultless
Tokenization
– End-to-End Tokens: Started with the D/W and expanding to stores
– Lower maintenance cost – don’t have to apply all 12 requirements
– Better security – able to eliminate several business and daily reports
– Qualified Security Assessors had no issues
• “With encryption, implementations can spawn dozens of questions”
• “There were no such challenges with tokenization”
32

33. HOW CAN WE
POSITION
DIFFERENT
SECURITY OPTIONS?
33

34. Speed of Different Protection Methods
Transactions per second
10 000 000 -
1 000 000 -
100 000 -
10 000 -
1 000 -
100 -
I I I I
Basic Format AES CBC Vaultless
Data Preserving Encryption Data
Speed will depend on
the configuration Tokenization Encryption Standard Tokenization
34

35. WHAT IS
VAULT-LESS
DATA TOKENIZATION?
35

36. Different Tokenization Approaches
Basic Tokenization Vault-less Tokenization*
Footprint Large, Expanding. Small, Static.
High Availability, Complex, expensive No replication required.
Disaster Recovery replication required.
Distribution Practically impossible to Easy to deploy at different
distribute geographically. geographically distributed locations.
Reliability Prone to collisions. No collisions.
Performance, Will adversely impact Little or no latency. Fastest industry
Latency, and performance & scalability. tokenization.
Scalability
Extendibility Practically impossible. Unlimited Tokenization Capability.
*: Validated by 3rd party experts
36

37. HOW IMPORTANT
IS COST?
37

38. Impact of Different Protection Methods
Intrusiveness (to Applications and Databases)
Encryption
Standard
Hashing - !@#$%a^///&*B()..,,,gft_+!@4#$2%p^&*
Strong Encryption - !@#$%a^.,mhu7///&*B()_+!@
Data Type & Format
Alpha - aVdSaH 1F4hJ 1D3a
Tokenizing or
Encoding
Numeric - 666666 777777 8888 Formatted
Encryption
Partial - 123456 777777 1234
Clear Text Data - 123456 123456 1234
Data
I
Length
Original
38

39. WHEN CAN I
USE
TOKENIZATION?
39

40. How Should I Secure Different Data?
File Field
Encryption Tokenization
Use
Case
Card
Simple - PII Holder PCI
Data
PHI
Protected
Health
Complex - Information
Type of
I I
Data
Un-structured Structured
40

41. Tokenizing Different Types of
Data
Type of Data Input Token Comment
Credit Card 3872 3789 1620 3675 8278 2789 2990 2789 Numeric
Medical ID 29M2009ID 497HF390D Alpha-Numeric
Date 10/30/1955 12/25/2034 Date
E-mail Ulf.mattsson@protegrit empo.snaugs@svtiensnni Alpha Numeric,
Address y.com .snk delimiters in input
preserved
SSN delimiters 075-67-2278 287-38-2567 Numeric, delimiters in
input
Credit Card 3872 3789 1620 3675 8278 2789 2990 3675 Numeric, Last 4 digits
exposed
41

42. ANY
TOKENIZATION
GUIDELINES?
42

43. Tokenization Guidelines, Visa
Token Generation Token Types
Single Use Multi Use
Token Token
Algorithm Known strong
and Key
Reversible
algorithm  No
Unique Sequence
Number  
One way
Hash Secret per Secret per
Irreversible
Function transaction merchant
Randomly
generated value  
43

44. Tokenization vs. Encryption
Encryption Tokenization
Used Approach Cipher System Code System
Cryptographic algorithms
Cryptographic keys
Code books
Index tokens
Source: McGraw-HILL ENCYPLOPEDIA OF SCIENCE & TECHNOLOGY
44

45. HOW SECURE IS
ENCRYPTION?
45

46. Many Broken Algorithms

47. KEYS
EVERYWHERE!
47

48. PCI DSS : Tokenization and
Encryption are Different
If the token is
mathematically
derived from the
original PAN
through the use of
an encryption
algorithm and
cryptographic key
No Scope
Reduction
48

49. TOKENS ARE
RANDOM
49

50. Tokenization and “PCI Out Of Scope”
De-tokenization
No Available?
Random Number
Yes
Tokens?
No:
Yes FPE
Isolated from Card
Holder Data
Yes Environment? No
Out of Scope No Scope
Scope Reduction Reduction
Source: http://www.securosis.com
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51. Case Study: Energy Industry
Why? Reduce PCI Scope
• Best way to handle legacy, we got most of it out of PCI
• Get rid of unwanted paper copies
• No need to rewrite/redevelop or restructure business
applications
• A VERY efficient way of PCI Reduction of Scope
• Better understanding of your data flow
• Better understanding of business flow
• Opportunity to clean up a few business oddities
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52. Evaluating Encryption & Tokenization
Database Database Basic Vaultless
Area Criteria File Column Tokenization Tokenization
Encryption Encryption
Availability
Scalability Latency
CPU Consumption
Data Flow
Protection
Compliance Scoping
Security Key Management
Data Collisions
Separation of Duties
Best

53. Case Studies: Retail
Customer 1: Why? Three major concerns solved
– Performance Challenge; Initial tokenization
– Vendor Lock-In: What if we want to switch payment processor
– Extensive Enterprise End-to-End Credit Card Data Protection
Customer 2: Why? Desired single vendor to provide data
protection
– Combined use of tokenization and encryption
– Looking to expand tokens beyond CCN to PII
Customer 3: Why? Remove compensating controls from the
mainframe
– Tokens on the mainframe to avoid compensating controls
53

54. WHAT IS
THE CURRENT USE
OF ENABLING
TECHNOLOGIES?
54

55. Use of Enabling Technologies
Access controls 1% 91%
Database activity monitoring 18% 47%
Database encryption 30% 35%
Backup / Archive encryption 21% 39%
Data masking 28% 28%
Application-level encryption 7% 29%
Tokenization 22% 23%
Evaluating
55

56. Is Data Masking Secure?
Risk
Data at rest Data display
High –
Masking Masking
Exposure: Exposure:
Data is only Data in clear
obfuscated before masking
Low -
System
I I I I Type
Test / dev Integration Trouble Production
testing shooting
56

57. Data Tokens = Lower Risk
Risk
Data at rest Data display
High –
Masking Masking
Exposure: Exposure:
Data is only Data in clear
obfuscated before masking
Low - Data Tokens
System
I I I I Type
Test / dev Integration Trouble Production
testing shooting
57

58. CAN SECURITY
HELP CREATIVITY?
58

59. Old Security = Less Creativity
Risk
High
Traditional
Access
Control
Low
Access
I I
Right Level
Less More
Source: InformationWeek Aug 15, 2011
59

60. New Data Security = More Creativity
Risk
High
Traditional
Access
Control New:
Creativity
Happens
At the edge
Low Data Tokens
Access
I I
Right Level
Less More
Source: InformationWeek Aug 15, 2011
60

61. WHAT IS THE IMPACT ON
RISK MANAGEMENT?
61

62. Choose Your Defenses
Cost
Cost of Aversion – Expected Losses
Protection of Data from the Risk
Total Cost
Optimal
Risk
Protection
I I Option
Data Monitoring
Lockdown
62

63. DATA SECURITY
ADVANCES ARE
CHANGING
THE BALANCE
63

64. Matching Data Protection with Risk Level
Risk Level Solution
Data Risk
Field Level Tokenization, str
High Risk
ong encryption
Credit Card Number 25 (16-25)
Social Security Number 20
Email Address 20 Monitoring,
Customer Name 12 Medium Risk masking, format
Secret Formula 10 (6-15) controlling
Employee Name 9 encryption
Employee Health Record 6
Zip Code 3 Low Risk Monitoring
(1-5)
64

65. SEPARATION OF
DUTIES!
65

66. Security of Different Protection Methods
Security Level
High
Low
I I I I
Basic Format AES CBC Vaultless
Data Preserving Encryption Data
Tokenization Encryption Standard Tokenization
66

67. HOW CAN I
SECURE DATA IN
CLOUD?
67

68. Risks with Cloud Computing
Handing over sensitive data to a third…
Threat of data breach or loss
Weakening of corporate network…
Uptime/business continuity
Financial strength of the cloud…
Inability to customize applications
0 10 20 30 40 50 60 70
%
Source: The evolving role of IT managers and CIOs Findings from the 2010 IBM Global IT Risk Study
68

69. PCI & Cloud
• The PCI council's security caution over
virtualization is justified, because virtualized
environments are susceptible to types of
attacks not seen in any other environment
– Bob Russo, general manager of the PCI Security
Standards Council

70. Amazon’s PCI Compliance
• PCI-DSS 2.0 doesn't address multi-tenancy concerns
• You can store PAN data on S3, but it still needs to be
encrypted in accordance with PCI-DSS requirements
• Amazon doesn't do this for you -- it's something you need to
implement yourself; including key
management, rotation, logging, etc.
• If you deploy a server instance in EC2 it still needs to be assessed
by your QSA
• Your organization's assessment scope isn't necessarily
reduced
• It might be when you move to something like a tokenization
service where you reduce your handling of PAN data
Source: securosis.com
070

71. Securing The Data Flow with Tokenization
Retail Bank
Store
Payment 9999 9999 Corporate
Network Systems
71

72. Why Tokenization?
Why Tokenization
1. No Masking
2. No Encryption
3. No Key Management
Why Vaultless Tokenization
$
1. Lower Cost / TCO
2. Better
3. Faster
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73. Conclusion
• Organizations need to understand their data flow
and current security technologies
– Determine most significant security exposures
– Target budgets toward addressing the most critical issues
– Strengthen security and compliance profiles
• Achieve the right balance between business needs
and security demands
– I increasingly important as companies are changing their
security strategies to better protect sensitive data
– Following continuing attacks
73

74. About Protegrity
• Proven enterprise data security software and innovation leader
– Sole focus on the protection of data
– Patented Technology, Continuing to Drive Innovation
• Growth driven by compliance and risk management
– PCI (Payment Card Industry), PII (Personally Identifiable Information), PHI
(Protected Health Information)
– US State and Foreign Privacy Laws, Breach Notification Laws
• Cross-industry applicability
– Retail, Hospitality, Travel and Transportation
– Financial Services, Insurance, Banking
– Healthcare, Telecommunications, Media and Entertainment
– Manufacturing and Government
74

75. Thank you!
Q&A
ulf.mattsson@protegrity.com
www.protegrity.com
203-326-7200
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