BrightTALK webinar January 14 2015 The biggest challenge in this new paradigm of the cloud and an interconnected world, is merging data security with data value and productivity. What’s required is a seamless, boundless security framework to maximize data utility while minimizing risk. In this webinar, you’ll learn about value-preserving data-centric security methods, how to keep track of your data and monitor data access outside the enterprise, and best practices for protecting data and privacy in the perimeter-less enterprise.

1. Where Data Security and Value of Data
Meet in the Cloud
Ulf Mattsson
CTO, Protegrity
Ulf.Mattsson@protegrity.com
BrightTALK webinar January 14 2015

2. Cloud Security Alliance (CSA)
PCI Security Standards Council
• Cloud & Virtualization SIGs
• Encryption Task Force
• Tokenization Task Force
IFIP
Ulf Mattsson, Protegrity CTO
• WG 11.3 Data and Application Security
• International Federation for Information Processing
ISACA
• (Information Systems Audit and Control Association)
ISSA
• (Information Systems Security Association)
2

3. The New Enterprise Paradigm
• Cloud computing, IoT and the disappearing perimeter
• Data is the new currency
Rethinking Data Security for a Boundless World
• The new wave of challenges to security and productivity
• Seamless, boundless security framework – data flow
• Maximize data utility & minimizing risk – finding the right balance
Agenda
• Maximize data utility & minimizing risk – finding the right balance
New Security Solutions, Technologies and Techniques
• Data-centric security technologies
• Data security and utility outside the enterprise
• Cloud data security in context to the enterprise
Best Practices
3

4. Verizon Data Breach Investigations Report
• Enterprises are losing ground in the fight
against persistent cyber-attacks
• We simply cannot catch the bad guys until it is
too late. This picture is not improving
• Verizon reports concluded that less than 14%
of breaches are detected by internal
Enterprises Losing Ground Against Cyber-attacks
of breaches are detected by internal
monitoring tools
JP Morgan Chase data breach
• Hackers were in the bank’s network for months
undetected
• Network configuration errors are inevitable,
even at the larges banks
We need a new approach to data security
4

5. High-profile Cyber Attacks
49% recommended Database security
40% of budget still on Network security
5
40%
only
19% to database security
Conclusion: Organisations have traditionally spent money on network security and so it is
earmarked in the budget and requires no further justification

6. The
Perimeter-less
6
Perimeter-less
World

7. Big data projects in 2015
• Integration with the
outside world
Security prevents big data
from becoming a prevalent
enterprise computing
Integration with Outside World
26 billion devices on the
Internet of Things by
2020 (Gartner)
7
www.infoworld.com/article/2866831/big-data/in-2015-big-data-will-slowly-
permeate-the-borders-of-the-enterprise.html
enterprise computing
platform
• 3rd party products are
helping
wikipedia.org

8. They’re Tracking When You Turn Off the Lights
8 Source: Wall Street Journal
Sensors to capture data on environmental conditions including sound volume,
wind and carbon-dioxide levels, as well as behavioral data such as pedestrian
traffic flow

9. The Department of Homeland Security investigating
• Two dozen cases of suspected cyber security flaws in
medical devices that could be exploited by hackers
• Can be detrimental to the patient, creating problems
such as instructing an infusion pump to overdose a
patient with drugs, or forcing a heart implant to deliver a
deadly jolt of electricity
Security Threats of Connected Medical Devices
deadly jolt of electricity
• Keep medical data stored encrypted
PricewaterhouseCoopers study
• $30bn annual cost hit to the US healthcare system due
to inadequate medical-device interoperability
9
www.computing.co.uk/ctg/opinion/2390029/security-threats-of-connected-
medical-devices#

10. CHALLENGE
How can I
Secure the
10
Secure the
Perimeter-less
Enterprise?

11. Cloud
ComputingComputing
11

12. What Is Your No. 1 Issue Slowing
Adoption of Public Cloud Computing?
12

13. Security of Data in Cloud at Board-level
13
Source: Cloud Adoption Practices & Priorities Survey Report January 2015

14. Data Security Holding Back Cloud Projects
14
Source: Cloud Adoption Practices & Priorities Survey Report January 2015

15. Threat Vector Inheritance
15

16. Public Cloud
16
Source: Wired.com

17. New Technologies
to Secure
17
to Secure
Cloud Data

18. Rather than making the protection platform based,
the security is applied directly to the data
Protecting the data wherever it goes, in any
environment
Data-Centric Protection Increases
Security in Cloud Computing
Cloud environments by nature have more access
points and cannot be disconnected
Data-centric protection reduces the reliance on
controlling the high number of access points
18

19. Corporate Network
Security Gateway Deployment – Hybrid Cloud
Client
System
Public Cloud
Cloud Gateway
Private Cloud
019
Enterprise
Security
Administrator
Security Officer
Out-sourced

20. Corporate NetworkCorporate Network
Security Gateway Deployment – Hybrid Cloud
Client
System
Private Cloud Public Cloud
Cloud
Gateway
020
Enterprise
Security
Administrator
Security Officer
Gateway
Out-sourced

21. Corporate Network
Client
System Cloud
Gateway
Security Gateway – Searchable Encryption
RDBMS
Query
re-write
021
Enterprise
Security
Administrator
Security Officer
Order preserving
encryption

22. Corporate Network
Client
System
Cloud
Gateway
Security Gateway – Search & Indexing
RDBMS
Query
re-write
022
Enterprise
Security
Administrator
Security Officer
IndexIndex

23. Cloud Gateway - Requirements Adjusted Protection
Data Protection Methods Scalability Storage Security Transparency
System without data protection
Weak Encryption (1:1 mapping)
Searchable Gateway Index (IV)
Vaultless Tokenization
Partial EncryptionPartial Encryption
Data Type Preservation Encryption
Strong Encryption (AES CBC, IV)
Best Worst
23

24. Comparing
Data ProtectionData Protection
Methods
24

25. Computational
Usefulness
Risk Adjusted Storage – Data Leaking Formats
H
25
Data
Leakage
Strong-encryption Truncation Sort-order-preserving-encryption Indexing
L
I I I I

26. Balancing Data Security & Utility
Value
Preserving
Classification of
Sensitive Data
Granular Protection
of Sensitive Data
26
Index Data
Leaking
Sensitive
Data ?
Encoding
Leaking
Sensitive
Data ?

27. Risk Adjusted Data Leakage
Index
Trust
H
Index
Leaking
Sensitive
Data
Sort Order Preserving
Encryption Algorithms
Leaking Sensitive
Data
27
Index Data
Elasticity
Out-sourcedIn-house
L
Index NOT
Leaking
Sensitive
Data

28. Reduction of Pain with New Protection Techniques
High
Pain
& TCO
Strong Encryption Output:
AES, 3DES
Format Preserving Encryption
DTP, FPE
Input Value: 3872 3789 1620 3675
!@#$%a^.,mhu7///&*B()_+!@
8278 2789 2990 2789
28
1970 2000 2005 2010
Low
Vault-based Tokenization
Vaultless Tokenization
8278 2789 2990 2789
Format Preserving
Greatly reduced Key
Management
No Vault
8278 2789 2990 2789

29. What is
Data Tokenization?
29
Data Tokenization?

30. Data Tokenization – Replacing The Data
30
Source: plus.google.com

31. Fine Grained Data Security Methods
Tokenization and Encryption are Different
Used Approach Cipher System Code System
Cryptographic algorithms
Cryptographic keys
TokenizationEncryption
31
Cryptographic keys
Code books
Index tokens
Source: McGraw-HILL ENCYPLOPEDIA OF SCIENCE & TECHNOLOGY

32. 10 000 000 -
1 000 000 -
100 000 -
10 000 -
Transactions per second*
Speed of Fine Grained Protection Methods
10 000 -
1 000 -
100 -
I
Format
Preserving
Encryption
I
Vaultless
Data
Tokenization
I
AES CBC
Encryption
Standard
I
Vault-based
Data
Tokenization
*: Speed will depend on the configuration
32

33. Significantly Different Tokenization Approaches
Property Dynamic Pre-generated
Vault-based Vaultless
33

34. Examples of Protected Data
Field Real Data Tokenized / Pseudonymized
Name Joe Smith csu wusoj
Address 100 Main Street, Pleasantville, CA 476 srta coetse, cysieondusbak, CA
Date of Birth 12/25/1966 01/02/1966
Telephone 760-278-3389 760-389-2289
E-Mail Address joe.smith@surferdude.org eoe.nwuer@beusorpdqo.org
SSN 076-39-2778 076-28-3390
CC Number 3678 2289 3907 3378 3846 2290 3371 3378
Business URL www.surferdude.com www.sheyinctao.com
Fingerprint Encrypted
Photo Encrypted
X-Ray Encrypted
Healthcare /
Financial
Services
Dr. visits, prescriptions, hospital stays
and discharges, clinical, billing, etc.
Financial Services Consumer Products
and activities
Protection methods can be equally
applied to the actual data, but not
needed with de-identification
34

35. Use
Case
How Should I Secure Different Data?
Simple – PCI
PII
Encryption
of Files
Card
Holder
Data
Tokenization
of Fields
Personally Identifiable Information
Type of
Data
I
Structured
I
Un-structured
Complex – PHI
Protected
Health
Information
35
Personally Identifiable Information

36. Example of Cross Border Data-centric Security
Data sources
Data
WarehouseWarehouse
In Italy
Complete policy-enforced de-
identification of sensitive data
across all bank entities

37. How to Balance
Risk andRisk and
Data Access
37

38. High -
Risk Adjusted Data Security – Access Controls
Risk Exposure
User Productivity and
Creativity
38
Access to
Sensitive Data in
Clear
Low Access to Data High Access to Data
Low -
I I

39. High -
Risk Adjusted Data Security – Tokenized Data
User Productivity and
Creativity
39
Access to
Tokenized Data
Low Access to Data High Access to Data
Low -
I I
Risk Exposure

40. Cost of
Application
Changes
High -
Risk Adjusted Data Security – Selective Masking
Risk Exposure
Cost Example: 16 digit credit card number
40
All-16-clear Only-middle-6-hidden All-16-hidden
Low -
I I I

41. Fine Grained Security: Securing Fields
Production Systems
Encryption of fields
• Reversible
• Policy Control (authorized / Unauthorized Access)
• Lacks Integration Transparency
• Complex Key Management
• Example: !@#$%a^.,mhu7///&*B()_+!@
41
Non-Production Systems
Masking of fields
• Not reversible
• No Policy, Everyone can access the data
• Integrates Transparently
• No Complex Key Management
• Example: 0389 3778 3652 0038

42. Fine Grained Security: Tokenization of Fields
Production Systems
Tokenization (Pseudonymization)
• No Complex Key Management
• Business Intelligence
• Example: 0389 3778 3652 0038
42
Non-Production Systems
• Reversible
• Policy Control (Authorized / Unauthorized Access)
• Not Reversible
• Integrates Transparently

43. Data–Centric Audit and Protection (DCAP)
Organizations that have not developed data-centric
security policies to coordinate management processes
and security controls across data silos need to act
By 2018, data-centric audit and protection strategies
will replace disparate siloed data security governance
approaches in 25% of large enterprises, up from less
043
Source: Gartner – Market Guide for Data – Centric Audit and Protection (DCAP), Nov 21 2014
approaches in 25% of large enterprises, up from less
than 5% today
Confidential

44. Centrally managed security policy
Across unstructured and structured silos
Classify data, control access and monitoring
Protection – encryption, tokenization and masking
Segregation of duties – application users and privileged
Data–Centric Audit and Protection (DCAP)
044
Segregation of duties – application users and privileged
users
Auditing and reporting
Source: Gartner – Market Guide for Data – Centric Audit and Protection (DCAP), Nov 21 2014
Confidential

45. Centralized Policy Management - Example
Application
RDBMS
MPP
Audit
Log
Audit
Log
Audit
Log
Enterprise
Security
Administrator
PolicyPolicyPolicyPolicyPolicyPolicyPolicyPolicyPolicy
Cloud
Security Officer
Audit
Log
Audit
Log
Audit
Log
45
File Servers
Big Data
Gateway
Servers
HP NonStop
Base24
IBM Mainframe
Protector
Audit
Log
Audit
Log Audit
Log
Audit
Log
Protection
Servers
Audit
Log
Audit
Log

46. Enterprise Data Security Policy
What is the sensitive data that needs to be protected.
How you want to protect and present sensitive data. There are several methods
for protecting sensitive data. Encryption, tokenization, monitoring, etc.
Who should have access to sensitive data and who should not. Security access
control.
What
Who
How
46
When should sensitive data access be granted to those who have access. Day
of week, time of day.
Where is the sensitive data stored? This will be where the policy is enforced.
Audit authorized or un-authorized access to sensitive data.
When
Where
Audit

47. The biggest challenge in this new paradigm
• Cloud and an interconnected world
• Merging data security with data value and productivity
What’s required?
• Seamless, boundless security framework – data flow
• Maximize data utility & Minimizing risk – finding the right balance
Value-preserving data-centric security methods
Summary
Value-preserving data-centric security methods
• How to keep track of your data and monitor data access outside the enterprise
• Best practices for protecting data and privacy in the perimeter-less enterprise.
What New Data Security Technologies are Available for Cloud?
How can Cloud Data Security work in Context to the Enterprise?
47

48. Thank you!Thank you!
Questions?
Please contact us for more information
www.protegrity.com
Ulf.Mattsson@protegrity.com