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Copyright ©Protegrity Corp. | Protegrity Confidential
Unlock the Potential of
Data Security
Ulf Mattsson
Chief Security Strategist
www.Protegrity.com
Copyright ©Protegrity Corp. | Protegrity Confidential
Ulf Mattsson
• Chief Security Strategist at Protegrity, previously Head of Innovation at
TokenEx and Chief Technology Officer at Atlantic BT, Compliance Engineering,
and IT Architect at IBM
• Products and Services:
• Data Encryption, Tokenization, Data Discovery, Cloud Application Security
Brokers (CASB), Web Application Firewalls (WAF), Robotics, and
Applications
• Security Operation Center (SOC), Managed Security Services (MSSP)
• Inventor of more than 70 issued US Patents and developed Industry
Standards with ANSI X9, CSA and PCI DSS
2
Copyright ©Protegrity Corp. | Protegrity Confidential
Unlockthe Potential of Data Security
- Data Security Governance Stakeholders
33
Copyright ©Protegrity Corp. | Protegrity Confidential
Opportunities
Controls
&
Tools
Regulations
Policies
RiskManagement
Breaches
Balance
Protect datainwaysthatare transparent to business processes andcompliantto
regulations 4
Copyright ©Protegrity Corp. | Protegrity Confidential 5
Copyright ©Protegrity Corp. | Protegrity Confidential
Verizon Data Breach Investigations Report (DBIR) 2020
Assetsin breaches
• On-premises assets are still 70% in ourreported breachesdataset.
• Cloud assets were involved inabout 24%of breaches.
• Email or web application server 73% of the time.
6
Copyright ©Protegrity Corp. | Protegrity Confidential
American officials aredrawing cellphone location data from mobile advertising firms totrackthe presence of crowds—but not individuals.
• AppleInc.and AlphabetInc.’sGoogle - avoluntaryapp thathealthofficialscan usetoreverse-engineersickenedpatients’recentwhereabouts—providedtheyagreetoprovidesuch information.
Collect personal or anonymized data?
InWesternAustralia,lawmakersapproveda billtoinstall surveillancegadgetsin people’shomes tomonitorthoseplacedunderquarantine.
Authoritiesin HongKongand India areusinggeofencing thatdrawsvirtualfencesaroundquarantinezones.
• Theymonitordigitalsignalsfromsmartphoneorwristbands todeterrulebreakersandnaboffenders,who can besenttojail.
7
Copyright ©Protegrity Corp. | Protegrity Confidential
Identity Theft Reports
• The USFEDERAL TRADE COMMISSION
(FTC) received nearly three million
complaints from consumers
• The FTC received morethan 167,000
reports frompeople whosaid their
information was misused on an
existing account or to opena new
credit cardaccount
8
Copyright ©Protegrity Corp. | Protegrity Confidential
Legal Compliance and Nation-State Attacks
• Manycompanies have information that is attractive to governments andintelligence services.
• Others worrythat litigation may result in a subpoena forall their data.
Securosis, 2019
Multi-Cloud Data Privacyconsiderations
Jurisdiction
• Cloudservice providers
redundancy is great for
resilience, but regulatory
concerns arises when moving
data across regions which may
have different laws and
jurisdictions.
9
Copyright ©Protegrity Corp. | Protegrity Confidential
Securosis, 2019
Consistency
• Most firmsarequite familiar with their on-premises
encryption andkeymanagement systems, so they often
prefer toleverage the same tool and skills across multiple
clouds.
• Firms often adopt a “best of breed”cloud approach.
Examples ofHybrid Cloud considerations
Trust
• Some customers simply donot trusttheir vendors.
Vendor Lock-in and Migration
• A commonconcern is vendorlock-in, andan
inabilitytomigratetoanothercloud serviceprovider.
Google Cloud AWSCloud Azure Cloud
Cloud Gateway
S3 SalesforceData Analytics
BigQuery
10
Copyright ©Protegrity Corp. | Protegrity Confidential
Current use or planto use:
Spending byDeploymentModel, DigitalCommercePlatforms,Worldwide
11
Copyright ©Protegrity Corp. | Protegrity Confidential
Whichof thefollowing aspectsof dataprivacyare you particularlyconcernedabout?
FTIConsulting- CorporateData
Privacy Today,2020
12
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Global Map Of PrivacyRights And Regulations
13
Copyright ©Protegrity Corp. | Protegrity Confidential
GDPR vs. CCPA
14
Copyright ©Protegrity Corp. | Protegrity Confidential
TrustArc
Legal and Regulatory Risks Are Exploding
15
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Encryption*and
Tokenization
Discover Data
Assets
Security by
Design
GDPR Security Requirements –Encryption and Tokenization
16
Copyright ©Protegrity Corp. | Protegrity Confidential
FindYour Sensitive Datain Cloudand On-Premise
www.protegrity.com
17
Copyright ©Protegrity Corp. | Protegrity Confidential
PaymentApplication
Payment
Network
Payment
Data
Policy, tokenization,
encryption
and keys
Gateway
Call Center
Application
PI*Data
Salesforce
Analytics
Application
DifferentialPrivacy
AndK-anonymity
PI*Data
Microsoft
ElectionGuard
Election
Data
Homomorphic Encryption
DataWarehouse
PI*Data
Vault-less tokenization
Use-Cases of Some Data Privacy Techniques
Voting
Application
Dev/testSystems
Masking
PI*Data
Vault-less tokenization
18
Copyright ©Protegrity Corp. | Protegrity Confidential
A DataSecurityGateway Can Protect Sensitive Datain Cloud and On-premise
19
Copyright ©Protegrity Corp. | Protegrity Confidential
Big DataProtectionwith GranularField Level Protectionfor GoogleCloud
20
Copyright ©Protegrity Corp. | Protegrity Confidential
Use Case (Financial Services) - Compliance with Cross-Border and Other
Privacy Restrictions
21
Copyright ©Protegrity Corp. | Protegrity Confidential
Use this shape toput
copy inside
(you can change the sizing tofit your copy needs)
Protection ofdata
in AWS S3 with Separation ofDuties
• Applications can use de-identified
data or data inthe clear based on
policies
• Protection of data inAWSS3 before
landing in a S3 bucket
Separation ofDuties
• EncryptionKeyManagement
• PolicyEnforcementPoint(PEP)
22
Copyright ©Protegrity Corp. | Protegrity Confidential
Examples of Data De-identification
23
Copyright ©Protegrity Corp. | Protegrity Confidential
Data protection techniques: Deployment on-premises, and clouds
Data
Warehouse
Centralized Distributed
On-
premises
Public
Cloud
Private
Cloud
Vault-based tokenization y y
Vault-less tokenization y y y y y y
Format preserving
encryption
y y y y y
Homomorphic encryption y y
Masking y y y y y y
Hashing y y y y y y
Server model y y y y y y
Local model y y y y y y
L-diversity y y y y y y
T-closeness y y y y y y
Privacy enhancing data de-identification
terminology and classification of techniques
De-
identification
techniques
Tokenization
Cryptographic
tools
Suppression
techniques
Formal
privacy
measurement
models
Differential
Privacy
K-anonymity
model
24
Copyright ©Protegrity Corp. | Protegrity Confidential
2-way
HomomorphicEncryption
(HE) K-anonymity
Tokenization
MaskingHashing
1-way
Analytics andMachine Learning(ML)
Different DataProtectionTechniques
AlgorithmicRandom
Computingon
encrypteddata
Format
Preserving
Fast Slow Very slow Fast Fast
FormatPreserving
DifferentialPrivacy
(DP)
Noise
added
FormatPreserving
Encryption
(FPE)
25
Copyright ©Protegrity Corp. | Protegrity Confidential
IS: International Standard
TR: Technical Report
TS: Technical Specification
Guidelines to help
comply with ethical
standards
20889 IS Privacy enhancing de-identification terminology and
classification of techniques
27018 IS Code of practice for protection of PII in public clouds acting
as PII processors
27701 IS Security techniques - Extension to ISO/IEC 27001 and
ISO/IEC 27002 for privacy information management - Requirements
and guidelines
29100 IS Privacy framework
29101 IS Privacy architecture framework
29134 IS Guidelines for Privacy impact assessment
29151 IS Code of Practice for PII Protection
29190 IS Privacy capability assessment model
29191 IS Requirements for partially anonymous, partially un-linkable
authentication
Cloud
11 Published International Privacy Standards
Framewor
k
Manageme
nt
Technique
s
Impact
19608 TS Guidance for developing security and privacy functional
requirements based on 15408
Requirement
s
27550 TR Privacy engineering for system lifecycle processes
Process
ISO Privacy Standards
26
Copyright ©Protegrity Corp. | Protegrity Confidential
Risk
Reduction
Source:
INTERNATIONAL
STANDARD ISO/IEC
20889
27
Copyright ©Protegrity Corp. | Protegrity Confidential
Reduction of Pain with New
Protection Techniques
28
Copyright ©Protegrity Corp. | Protegrity Confidential
Personally Identifiable Information(PII) in compliance with the
EUCross Border Data Protection Laws, specifically
• Datenschutzgesetz 2000(DSG 2000)in Austria, and
• Bundesdatenschutzgesetz inGermany.
This requiredaccess to Austrianand German customer data to
berestricted to onlyrequesters ineach respective country.
• Achieved targeted compliance with EU Cross Border Data
Security laws
• Implemented country-specificdata access restrictions
Datasources
Case Study
Amajor international bankperformed a consolidationofallEuropeanoperationaldatasources toItaly
29
Copyright ©Protegrity Corp. | Protegrity Confidential
Speed ofFine-GrainedProtection Methods
10000000-
1000000-
100000-
10000-
1000-
100-
Transactions per second*
I
Format
Preserving
Encryption
I
AESCBC
Encryption
Standard
I
Vault-based
Data
Tokenization
I
Vaultless
Data
Tokenization
30
Copyright ©Protegrity Corp. | Protegrity Confidential
Significantly Different Tokenization Approaches
31
Copyright ©Protegrity Corp. | Protegrity Confidential
Lower Risk andHigher Productivity with More AccesstoMoreData
32
Copyright ©Protegrity Corp. | Protegrity Confidential
UlfMattsson
Chief SecurityStrategist
www.Protegrity.com
Thank You!

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Unlock the potential of data security 2020

  • 1. Copyright ©Protegrity Corp. | Protegrity Confidential Unlock the Potential of Data Security Ulf Mattsson Chief Security Strategist www.Protegrity.com
  • 2. Copyright ©Protegrity Corp. | Protegrity Confidential Ulf Mattsson • Chief Security Strategist at Protegrity, previously Head of Innovation at TokenEx and Chief Technology Officer at Atlantic BT, Compliance Engineering, and IT Architect at IBM • Products and Services: • Data Encryption, Tokenization, Data Discovery, Cloud Application Security Brokers (CASB), Web Application Firewalls (WAF), Robotics, and Applications • Security Operation Center (SOC), Managed Security Services (MSSP) • Inventor of more than 70 issued US Patents and developed Industry Standards with ANSI X9, CSA and PCI DSS 2
  • 3. Copyright ©Protegrity Corp. | Protegrity Confidential Unlockthe Potential of Data Security - Data Security Governance Stakeholders 33
  • 4. Copyright ©Protegrity Corp. | Protegrity Confidential Opportunities Controls & Tools Regulations Policies RiskManagement Breaches Balance Protect datainwaysthatare transparent to business processes andcompliantto regulations 4
  • 5. Copyright ©Protegrity Corp. | Protegrity Confidential 5
  • 6. Copyright ©Protegrity Corp. | Protegrity Confidential Verizon Data Breach Investigations Report (DBIR) 2020 Assetsin breaches • On-premises assets are still 70% in ourreported breachesdataset. • Cloud assets were involved inabout 24%of breaches. • Email or web application server 73% of the time. 6
  • 7. Copyright ©Protegrity Corp. | Protegrity Confidential American officials aredrawing cellphone location data from mobile advertising firms totrackthe presence of crowds—but not individuals. • AppleInc.and AlphabetInc.’sGoogle - avoluntaryapp thathealthofficialscan usetoreverse-engineersickenedpatients’recentwhereabouts—providedtheyagreetoprovidesuch information. Collect personal or anonymized data? InWesternAustralia,lawmakersapproveda billtoinstall surveillancegadgetsin people’shomes tomonitorthoseplacedunderquarantine. Authoritiesin HongKongand India areusinggeofencing thatdrawsvirtualfencesaroundquarantinezones. • Theymonitordigitalsignalsfromsmartphoneorwristbands todeterrulebreakersandnaboffenders,who can besenttojail. 7
  • 8. Copyright ©Protegrity Corp. | Protegrity Confidential Identity Theft Reports • The USFEDERAL TRADE COMMISSION (FTC) received nearly three million complaints from consumers • The FTC received morethan 167,000 reports frompeople whosaid their information was misused on an existing account or to opena new credit cardaccount 8
  • 9. Copyright ©Protegrity Corp. | Protegrity Confidential Legal Compliance and Nation-State Attacks • Manycompanies have information that is attractive to governments andintelligence services. • Others worrythat litigation may result in a subpoena forall their data. Securosis, 2019 Multi-Cloud Data Privacyconsiderations Jurisdiction • Cloudservice providers redundancy is great for resilience, but regulatory concerns arises when moving data across regions which may have different laws and jurisdictions. 9
  • 10. Copyright ©Protegrity Corp. | Protegrity Confidential Securosis, 2019 Consistency • Most firmsarequite familiar with their on-premises encryption andkeymanagement systems, so they often prefer toleverage the same tool and skills across multiple clouds. • Firms often adopt a “best of breed”cloud approach. Examples ofHybrid Cloud considerations Trust • Some customers simply donot trusttheir vendors. Vendor Lock-in and Migration • A commonconcern is vendorlock-in, andan inabilitytomigratetoanothercloud serviceprovider. Google Cloud AWSCloud Azure Cloud Cloud Gateway S3 SalesforceData Analytics BigQuery 10
  • 11. Copyright ©Protegrity Corp. | Protegrity Confidential Current use or planto use: Spending byDeploymentModel, DigitalCommercePlatforms,Worldwide 11
  • 12. Copyright ©Protegrity Corp. | Protegrity Confidential Whichof thefollowing aspectsof dataprivacyare you particularlyconcernedabout? FTIConsulting- CorporateData Privacy Today,2020 12
  • 13. Copyright ©Protegrity Corp. | Protegrity Confidential Global Map Of PrivacyRights And Regulations 13
  • 14. Copyright ©Protegrity Corp. | Protegrity Confidential GDPR vs. CCPA 14
  • 15. Copyright ©Protegrity Corp. | Protegrity Confidential TrustArc Legal and Regulatory Risks Are Exploding 15
  • 16. Copyright ©Protegrity Corp. | Protegrity Confidential Encryption*and Tokenization Discover Data Assets Security by Design GDPR Security Requirements –Encryption and Tokenization 16
  • 17. Copyright ©Protegrity Corp. | Protegrity Confidential FindYour Sensitive Datain Cloudand On-Premise www.protegrity.com 17
  • 18. Copyright ©Protegrity Corp. | Protegrity Confidential PaymentApplication Payment Network Payment Data Policy, tokenization, encryption and keys Gateway Call Center Application PI*Data Salesforce Analytics Application DifferentialPrivacy AndK-anonymity PI*Data Microsoft ElectionGuard Election Data Homomorphic Encryption DataWarehouse PI*Data Vault-less tokenization Use-Cases of Some Data Privacy Techniques Voting Application Dev/testSystems Masking PI*Data Vault-less tokenization 18
  • 19. Copyright ©Protegrity Corp. | Protegrity Confidential A DataSecurityGateway Can Protect Sensitive Datain Cloud and On-premise 19
  • 20. Copyright ©Protegrity Corp. | Protegrity Confidential Big DataProtectionwith GranularField Level Protectionfor GoogleCloud 20
  • 21. Copyright ©Protegrity Corp. | Protegrity Confidential Use Case (Financial Services) - Compliance with Cross-Border and Other Privacy Restrictions 21
  • 22. Copyright ©Protegrity Corp. | Protegrity Confidential Use this shape toput copy inside (you can change the sizing tofit your copy needs) Protection ofdata in AWS S3 with Separation ofDuties • Applications can use de-identified data or data inthe clear based on policies • Protection of data inAWSS3 before landing in a S3 bucket Separation ofDuties • EncryptionKeyManagement • PolicyEnforcementPoint(PEP) 22
  • 23. Copyright ©Protegrity Corp. | Protegrity Confidential Examples of Data De-identification 23
  • 24. Copyright ©Protegrity Corp. | Protegrity Confidential Data protection techniques: Deployment on-premises, and clouds Data Warehouse Centralized Distributed On- premises Public Cloud Private Cloud Vault-based tokenization y y Vault-less tokenization y y y y y y Format preserving encryption y y y y y Homomorphic encryption y y Masking y y y y y y Hashing y y y y y y Server model y y y y y y Local model y y y y y y L-diversity y y y y y y T-closeness y y y y y y Privacy enhancing data de-identification terminology and classification of techniques De- identification techniques Tokenization Cryptographic tools Suppression techniques Formal privacy measurement models Differential Privacy K-anonymity model 24
  • 25. Copyright ©Protegrity Corp. | Protegrity Confidential 2-way HomomorphicEncryption (HE) K-anonymity Tokenization MaskingHashing 1-way Analytics andMachine Learning(ML) Different DataProtectionTechniques AlgorithmicRandom Computingon encrypteddata Format Preserving Fast Slow Very slow Fast Fast FormatPreserving DifferentialPrivacy (DP) Noise added FormatPreserving Encryption (FPE) 25
  • 26. Copyright ©Protegrity Corp. | Protegrity Confidential IS: International Standard TR: Technical Report TS: Technical Specification Guidelines to help comply with ethical standards 20889 IS Privacy enhancing de-identification terminology and classification of techniques 27018 IS Code of practice for protection of PII in public clouds acting as PII processors 27701 IS Security techniques - Extension to ISO/IEC 27001 and ISO/IEC 27002 for privacy information management - Requirements and guidelines 29100 IS Privacy framework 29101 IS Privacy architecture framework 29134 IS Guidelines for Privacy impact assessment 29151 IS Code of Practice for PII Protection 29190 IS Privacy capability assessment model 29191 IS Requirements for partially anonymous, partially un-linkable authentication Cloud 11 Published International Privacy Standards Framewor k Manageme nt Technique s Impact 19608 TS Guidance for developing security and privacy functional requirements based on 15408 Requirement s 27550 TR Privacy engineering for system lifecycle processes Process ISO Privacy Standards 26
  • 27. Copyright ©Protegrity Corp. | Protegrity Confidential Risk Reduction Source: INTERNATIONAL STANDARD ISO/IEC 20889 27
  • 28. Copyright ©Protegrity Corp. | Protegrity Confidential Reduction of Pain with New Protection Techniques 28
  • 29. Copyright ©Protegrity Corp. | Protegrity Confidential Personally Identifiable Information(PII) in compliance with the EUCross Border Data Protection Laws, specifically • Datenschutzgesetz 2000(DSG 2000)in Austria, and • Bundesdatenschutzgesetz inGermany. This requiredaccess to Austrianand German customer data to berestricted to onlyrequesters ineach respective country. • Achieved targeted compliance with EU Cross Border Data Security laws • Implemented country-specificdata access restrictions Datasources Case Study Amajor international bankperformed a consolidationofallEuropeanoperationaldatasources toItaly 29
  • 30. Copyright ©Protegrity Corp. | Protegrity Confidential Speed ofFine-GrainedProtection Methods 10000000- 1000000- 100000- 10000- 1000- 100- Transactions per second* I Format Preserving Encryption I AESCBC Encryption Standard I Vault-based Data Tokenization I Vaultless Data Tokenization 30
  • 31. Copyright ©Protegrity Corp. | Protegrity Confidential Significantly Different Tokenization Approaches 31
  • 32. Copyright ©Protegrity Corp. | Protegrity Confidential Lower Risk andHigher Productivity with More AccesstoMoreData 32
  • 33. Copyright ©Protegrity Corp. | Protegrity Confidential UlfMattsson Chief SecurityStrategist www.Protegrity.com Thank You!

Editor's Notes

  1. The 2014 Verizon Data Breach Investigations Report concluded that 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 concluded that less than 14% of breaches are detected by internal security tools. Detection by third party entities increased from approximately 10% to 25% during the last three years. Specifically theft of payment card information 99% of the cases that someone else told the victim they had suffered a breach. One reason is that our current approach with monitoring and intrusion detection products can't tell you what normal looks like in your own systems and SIEM technology is simply too slowly to be useful for security analytics. Big Data security analytics may help over time, but we don't have time to wait. Biggest hacks and security breaches of 2014 include eBay, Target, Sony and Microsoft, Celebrity iCloud, NSA, Heartbleed, Sony The successful attack on JP Morgan Chase surprised me most as the largest US bank lost personal information of 76 million households and it took several months to detect.
  2. GDPR definition personal data: “anything that relates to an identifiable, living individual whether it actually identifies them or makes them identifiable”. CCPA redefines ”Personal information” CCPA states that ”Personal information” means information that identifies, relates to, describes, is capable of being associated with, or could reasonably be linked, directly or indirectly, with a particular consumer or household
  3. *: PI Data (Personal information) means information that identifies, relates to, describes, is capable of being associated with, or could reasonably be linked, directly or indirectly, with a consumer or household according to CCPA
  4. Simply minimizing the data you collect doesn’t do anything to protect the information that’s left. This is something you should be doing no matter what, however…
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