Securing Data Today  and in the Future            Ulf Mattsson           CTO Protegrity ulf . mattsson [at] protegrity . com
Ulf Mattsson   20 years with IBM Development & Global Services   Inventor of 22 patents – Encryption and Tokenization   Co...
03
Data Breaches04
Best Source of Incident Data             “It is fascinating that the top threat events                 in both 2010 and 20...
Data Breaches – Mainly Online Data Records  900+ breaches  900+ million compromised records:                              ...
Compromised Data Types - # Records         Payment card data        Personal information     Usernames, passwords         ...
Industry Groups Represented - # Breaches        Hospitality             Retail Financial Services      Government     Tech...
Breach Discovery Methods - # Breaches     Third party fraud detection    Notified by law enforcement  Reported by customer...
PCI DSS010
Example of How the Problem is Occurring – PCI DSS     Encrypt     Data on                                                 ...
PCI DSS - Ways to Render the PAN* Unreadable   Two-way cryptography with associated key management   processes   One-way c...
Protecting the Data Flow - Example                                            : Enforcement point       Unprotected sensit...
Use of        Enabling      Technologies014
Current, Planned Use of Enabling Technologies            Access controls             1%                                   ...
Current Use of Enabling Technologies, by Maturity Class
Positioning Different Protection Options        Evaluation Criteria        Strong     Formatted     Data                  ...
Securing Data Fields – Impact of Different Methods              Intrusiveness     (to Applications and Databases)         ...
Oracle Domain Index
Data      Tokenization020
Hiding Data in Plain Sight – Data Tokenization                                                       Y&SFD%))S(           ...
Token Flexibility for Different Categories of DataType of Data     Input                        Token                     ...
Example: HIPAA – 18 Direct Identifiers 1. Names 2. Geographic subdivisions smaller than a state, including 3. All elements...
Visa Best Practices for Tokenization Version 1Published July 14, 2010.Token Generation                                    ...
Tokenization Use Case Example A leading retail chain    • 1500 locations in the U.S. market Simplify PCI Compliance    • 9...
Different Approaches for Tokenization      Traditional Tokenization            • Dynamic Model or Pre-Generated Model     ...
Tokenization Summary                              Traditional Tokenization                                  Memory Tokeniz...
Cloud028
“Cloud – Like a Parking Garage”
Risks Associated with Cloud Computing Handing over sensitive data to a           third party     Threat of data breach or ...
Amazon Cloud & PCI DSS  Just because AWS is certified doesnt mean you are     • You still need to deploy a PCI compliant a...
Guidance from Cloud Security Alliance
“Pass Security Before Entering The Cloud”                                               User                              ...
Data Tokens in a Cloud Environment – Integration Example         990-23-1013      4000 0012 3456 7899                     ...
Data Tokens in a Cloud Environment – Integration Example                                                                  ...
Data Tokenization at the Gateway Layer                              User                                            User  ...
Data Tokenization at the Gateway Layer                             User                                            User   ...
Data Tokenization at the Application Layer                             User                                         Securi...
Data Tokenization at the Database Layer                             User                                         Security ...
Securing Encryption Keys      User                                             Encryption Key                             ...
Positioning of        Enabling      Technologies041
Risk Management and PCI – Security Aspects Different data security methods and algorithms Policy enforcement implemented...
Risk Management and PCI – Security Aspects Integration at different system layers Different data security methods and al...
Evaluating Field Encryption & TokenizationEvaluation Criteria                              Strong Field    Formatted     T...
Vendors/Products Providing Database Protection          Feature               3rd Party   Oracle 9   Oracle 10   Oracle 11...
Column Encryption Solutions – Some Considerations                   Area of Evaluation                      3rd    Oracle ...
Choose Your Defenses – Cost Effective PCI DSS                                          Firewalls     Encryption/Tokenizati...
Deploy DefensesMatching Data Protection Solutions with Risk Level                                 Risk Level          Solu...
Choose Your Defenses – Total Cost of OwnershipCost       Cost of Aversion –                      Expected Losses       Pro...
Best Practices - Data Security Management                                                          Policy                 ...
About Protegrity    Proven enterprise data security software and innovation leader        •   Sole focus on the protection...
Please contact me for more information    Ulf Mattsson, CTO Protegrity   Ulf . Mattsson [at] protegrity . com
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Securing data today and in the future - Oracle NYC

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NYOUG - New York Oracle Users Group:
- Risks Associated with Cloud Computing
- Data Tokens in a Cloud Environment
- Data Tokenization at the Gateway Layer
- Data Tokenization at the Database Layer
- Risk Management and PCI

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Securing data today and in the future - Oracle NYC

  1. 1. Securing Data Today and in the Future Ulf Mattsson CTO Protegrity ulf . mattsson [at] protegrity . com
  2. 2. Ulf Mattsson 20 years with IBM Development & Global Services Inventor of 22 patents – Encryption and Tokenization Co-founder of Protegrity (Data Security) Research member of the International Federation for Information Processing (IFIP) WG 11.3 Data and Application Security Member of • Cloud Security Alliance (CSA) • PCI Security Standards Council (PCI SSC) • American National Standards Institute (ANSI) X9 • Information Systems Security Association (ISSA) • Information Systems Audit and Control Association (ISACA)
  3. 3. 03
  4. 4. Data Breaches04
  5. 5. Best Source of Incident Data “It is fascinating that the top threat events in both 2010 and 2011 are the same and involve external agents hacking and installing malware to compromise the confidentiality and integrity of servers.” Source: 2011 Data Breach Investigations Report, Verizon Business RISK team Source: Securosis, http://securosis.com/
  6. 6. Data Breaches – Mainly Online Data Records 900+ breaches 900+ million compromised records: % Source: 2010 Data Breach Investigations Report, Verizon Business RISK team and USSS
  7. 7. Compromised Data Types - # Records Payment card data Personal information Usernames, passwords Intellectual property Bank account data Medical records Classified information System informationSensitive organizational data 0 20 40 60 80 100 120 % Source: Data Breach Investigations Report, Verizon Business RISK team and USSS
  8. 8. Industry Groups Represented - # Breaches Hospitality Retail Financial Services Government Tech Services Manufacturing Transportation Media Healthcare Business Services 0 10 20 30 40 %50 Source: Data Breach Investigations Report, Verizon Business RISK team and USSS
  9. 9. Breach Discovery Methods - # Breaches Third party fraud detection Notified by law enforcement Reported by customer/partner… Unusual system behavior Reported by employee Internal security audit or scan Internal fraud detectionBrag or blackmail by perpetrator Third party monitoring service 0 10 20 30 40 50 % Source: Data Breach Investigations Report, Verizon Business RISK team and USSS
  10. 10. PCI DSS010
  11. 11. Example of How the Problem is Occurring – PCI DSS Encrypt Data on Attacker SSL Public Public Network Networks (PCI DSS) Private Network Clear Text Data Application Clear Text Data Database Encrypt Data OS File At Rest System (PCI DSS) Storage System Source: PCI Security Standards Council, 2011
  12. 12. PCI DSS - Ways to Render the PAN* Unreadable Two-way cryptography with associated key management processes One-way cryptographic hash functions Index tokens and pads Truncation (or masking – xxxxxx xxxxxx 6781)* PAN: Primary Account Number (Credit Card Number)
  13. 13. Protecting the Data Flow - Example : Enforcement point Unprotected sensitive information: Protected sensitive information
  14. 14. Use of Enabling Technologies014
  15. 15. Current, Planned Use of Enabling Technologies Access controls 1% 91% 5%Database activity monitoring 18% 47% 16% Database encryption 30% 35% 10%Backup / Archive encryption 21% 39% 4% Data masking 28% 28% 7% Application-level encryption 7% 29% 7% Tokenization 22% 23% 13% Evaluating Current Use Planned Use <12 Months
  16. 16. Current Use of Enabling Technologies, by Maturity Class
  17. 17. Positioning Different Protection Options Evaluation Criteria Strong Formatted Data Encryption Encryption Tokens Security & Compliance Total Cost of Ownership Use of Encoded Data Best Worst
  18. 18. Securing Data Fields – Impact of Different Methods Intrusiveness (to Applications and Databases) Hashing - !@#$%a^///&*B()..,,,gft_+!@4#$2%p^&* Standard Encryption Strong Encryption - !@#$%a^.,mhu7/////&*B()_+!@ Alpha - aVdSaH 1F4hJ 1D3a Tokenizing orEncoding Numeric - 666666 777777 8888 Formatted Encryption Partial - 123456 777777 1234 Clear Text - 123456 123456 1234 Original Data Data I I Length Original Longer
  19. 19. Oracle Domain Index
  20. 20. Data Tokenization020
  21. 21. Hiding Data in Plain Sight – Data Tokenization Y&SFD%))S( Tokenization Gateway 4000 0012 3456 7899 Data Token 40 12 3456 7890 7899 Application Cloud Database Environment : Data Transformer Unprotected sensitive information:021 Protected sensitive information:
  22. 22. Token Flexibility for Different Categories of DataType of Data Input Token Comment Token PropertiesCredit Card 3872 3789 1620 3675 8278 2789 2990 2789 NumericMedical ID 29M2009ID 497HF390D Alpha-NumericDate 10/30/1955 12/25/2034 DateE-mail Address bob.hope@protegrity.com empo.snaugs@svtiensnni.snk Alpha Numeric, delimiters in input preservedSSN delimiters 075-67-2278 287-38-2567 Numeric, delimiters in inputCredit Card 3872 3789 1620 3675 8278 2789 2990 3675 Numeric, Last 4 digits exposed Policy MaskingCredit Card 3872 3789 1620 3675 clear, encrypted, tokenized at rest Presentation Mask: Expose 1st 3872 37## #### #### 6 digits
  23. 23. Example: HIPAA – 18 Direct Identifiers 1. Names 2. Geographic subdivisions smaller than a state, including 3. All elements of dates (e.g., date of birth, admission) 4. Telephone numbers 5. Fax numbers 6. E-mail addresses 7. Social Security numbers 8. Medical record numbers 9. Health plan beneficiary numbers 10. Account numbers 11. Certificate/license numbers 12. Vehicle identifiers and serial numbers, including license plate numbers 13. Device identifiers and serial numbers 14. Web universal locators (URLs) 15. IP address numbers 16. Biometric identifiers, including fingerprints and voice prints 17. Full-face photographic images and any comparable images 18. Other unique identifying numbers, characteristics or codes
  24. 24. Visa Best Practices for Tokenization Version 1Published July 14, 2010.Token Generation Token Types Single Use Token Multi Use TokenAlgorithm andKey Reversible Known strong algorithm (NIST Approved)  - Unique Sequence Number  One way Hash Secret per Secret perIrreversibleFunction transaction merchant Randomly generated value  
  25. 25. Tokenization Use Case Example A leading retail chain • 1500 locations in the U.S. market Simplify PCI Compliance • 98% of Use Cases out of audit scope • Ease of install (had 18 PCI initiatives at one time) Tokenization solution was implemented in 2 weeks • Reduced PCI Audit from 7 months to 3 months • No 3rd Party code modifications • Proved to be the best performance option • 700,000 transactions per days • 50 million card holder data records • Conversion took 90 minutes (plan was 30 days) • Next step – tokenization server at 1500 locations
  26. 26. Different Approaches for Tokenization Traditional Tokenization • Dynamic Model or Pre-Generated Model • 5 tokens per second - 5000 tokenizations per second Next Generation Tokenization • Memory-tokenization • 200,000 - 9,000,000+ tokenizations per second • “The tokenization scheme offers excellent security, since it is based on fully randomized tables.” * • “This is a fully distributed tokenization approach with no need for synchronization and there is no risk for collisions.“ * *: Prof. Dr. Ir. Bart Preneel, Katholieke University Leuven, Belgium
  27. 27. Tokenization Summary Traditional Tokenization Memory TokenizationFootprint Large, Expanding. Small, Static. The large and expanding footprint of Traditional The small static footprint is the enabling factor that Tokenization is it’s Achilles heal. It is the source of delivers extreme performance, scalability, and expanded poor performance, scalability, and limitations on its use. expanded use.High Complex replication required. No replication required.Availability, Deploying more than one token server for the Any number of token servers can be deployed withoutDR, and purpose of high availability or scalability will require the need for replication or synchronization between theDistribution complex and expensive replication or servers. This delivers a simple, elegant, yet powerful synchronization between the servers. solution.Reliability Prone to collisions. No collisions. The synchronization and replication required to Memory Tokenizations’ lack of need for replication or support many deployed token servers is prone to synchronization eliminates the potential for collisions . collisions, a characteristic that severely limits the usability of traditional tokenization.Performance, Will adversely impact performance & scalability. Little or no latency. Fastest industry tokenization.Latency, and The large footprint severely limits the ability to place The small footprint enables the token server to beScalability the token server close to the data. The distance placed close to the data to reduce latency. When placed between the data and the token server creates in-memory, it eliminates latency and delivers the fastest latency that adversely effects performance and tokenization in the industry. scalability to the extent that some use cases are not possible.Extendibility Practically impossible. Unlimited Tokenization Capability. Based on all the issues inherent in Traditional Memory Tokenization can be used to tokenize many Tokenization of a single data category, tokenizing data categories with minimal or no impact on footprint more data categories may be impractical. or performance.
  28. 28. Cloud028
  29. 29. “Cloud – Like a Parking Garage”
  30. 30. Risks Associated with Cloud Computing Handing over sensitive data to a third party Threat of data breach or loss Weakening of corporate network security Uptime/business continuity Financial strength of the cloud computing providerInability 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
  31. 31. Amazon Cloud & PCI DSS Just because AWS is certified doesnt mean you are • You still need to deploy a PCI compliant application/service and anything on AWS is still within your assessment scope PCI-DSS 2.0 doesnt 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 doesnt do this for you • You need to implement key management, rotation, logging, etc. If you deploy a server instance in EC2 it still needs to be assessed by your QSA (PCI auditor) • Organizations assessment scope isnt necessarily reduced Tokenization can reduce your handling of PAN data Source: Securosis, http://securosis.com/
  32. 32. Guidance from Cloud Security Alliance
  33. 33. “Pass Security Before Entering The Cloud” User 123456 123456 1234 Security Check Point 123456 123456 1234 Sensitive data 123456 999999 1234 Secured data Cloud Unprotected sensitive information: Protected sensitive information
  34. 34. Data Tokens in a Cloud Environment – Integration Example 990-23-1013 4000 0012 3456 7899 123-45 -1013 40 12 3456 7890 7899 Tokenization Gateway 123-45 -1013 40 12 3456 7890 7899 Application Databases Cloud Environment : Data Token Unprotected sensitive information:034 Protected sensitive information
  35. 35. Data Tokens in a Cloud Environment – Integration Example Security Admin UserTokenization Tokenization Gateway Gateway Application Databases Cloud Environment : Data Token Unprotected sensitive information:035 Protected sensitive information
  36. 36. Data Tokenization at the Gateway Layer User User Application Application Tokenization Cloud Gateway Environment Database Database : Data Token Unprotected sensitive information:036 Protected sensitive information
  37. 37. Data Tokenization at the Gateway Layer User User Application Application Tokenization Gateway Cloud Environment Database Database : Data Token Unprotected sensitive information:037 Protected sensitive information
  38. 38. Data Tokenization at the Application Layer User Security Admin Application Token Server Database Cloud : Data Token Unprotected sensitive information:038 Protected sensitive information
  39. 39. Data Tokenization at the Database Layer User Security Admin Application Token Server Database Cloud : Data Token Unprotected sensitive information:039 Protected sensitive information
  40. 40. Securing Encryption Keys User Encryption Key Administration An entity that uses a given key should not SaaS be the entity that stores that key PaaS IaaS Encryption Keys Cloud Source: http://csrc.nist.gov/groups/SNS/cloud-computing/040
  41. 41. Positioning of Enabling Technologies041
  42. 42. Risk Management and PCI – Security Aspects Different data security methods and algorithms Policy enforcement implemented at different system layers Data Security Method Hashing Formatted Strong Data Encryption Encryption Tokenization System Layer Application Database Column Database File Storage Device Best Worst
  43. 43. Risk Management and PCI – Security Aspects Integration at different system layers Different data security methods and algorithms Data Security Method Hashing Formatted Strong Data Encryption Encryption Tokenization System Layer Application Database Column Database File Storage Device : N/A Best Worst
  44. 44. Evaluating Field Encryption & TokenizationEvaluation Criteria Strong Field Formatted Tokenization Encryption Encryption (distributed)Disconnected environmentsDistributed environmentsPerformance impact when loading dataTransparent to applicationsExpanded storage sizeTransparent to databases schemaLong life-cycle dataUnix or Windows mixed with “big iron” (EBCDIC)Easy re-keying of data in a data flowHigh risk dataSecurity - compliance to PCI, NIST Best Worst
  45. 45. Vendors/Products Providing Database Protection Feature 3rd Party Oracle 9 Oracle 10 Oracle 11 IBM DB2 MS SQL Database file encryptionDatabase column encryptionColumn encryption adds 32-52 bytes (10.2.0.4, 11.1.0.7) Formatted encryption Data tokenizationDatabase activity monitoring Multi vendor encryption Data masking Central key management HSM support (11.1.0.7)Re-key support (tablespace) Best Worst
  46. 46. Column Encryption Solutions – Some Considerations Area of Evaluation 3rd Oracle Oracle Party 10 TDE 11 TDE Performance, manage UDT or views/triggers Support for both encryption and replication Support for Oracle Domain Index for fast search Keys are local; re-encryption if moving A -> B Separation of duties/key control vector Encryption format specified Data type support Index support beyond equality comparison HSM (hardware crypto) support (11.1.0.6 ) HSM password not stored in file Automated and secure master key backup procedure Keys exportable Best Worst
  47. 47. Choose Your Defenses – Cost Effective PCI DSS Firewalls Encryption/Tokenization for data at rest Anti-virus & anti-malware solution Encryption for data in motion Access governance systems Identity & access management systems Correlation or event management systems Web application firewalls (WAF) WAF Endpoint encryption solution Data loss prevention systems (DLP) DLP Intrusion detection or prevention systems Database scanning and monitoring (DAM) DAM ID & credentialing system Encryption/Tokenization 0 10 20 30 40 50 60 70 80 90 %Source: 2009 PCI DSS Compliance Survey, Ponemon Institute
  48. 48. Deploy DefensesMatching Data Protection Solutions with Risk Level Risk Level Solution Data Risk Field Level Low Risk Monitor Credit Card Number 25 (1-5)Social Security Number 20 CVV 20 At Risk Monitor, mask, Customer Name 12 (6-15) access control Secret Formula 10 limits, format Employee Name 9 controlEmployee Health Record 6 encryption High Risk Replacement, Zip Code 3 (16-25) strong encryption
  49. 49. Choose Your Defenses – Total Cost of OwnershipCost Cost of Aversion – Expected Losses Protection of Data from the Risk Total Cost Optimal Risk X Risk I I Level Strong Weak Protection Protection
  50. 50. Best Practices - Data Security Management Policy File System Protector Database Protector Audit Log Application Protector Enterprise Data Security Administrator Tokenization Secure Server Archive050 : Encryption service
  51. 51. 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) – HIPAA • State and Foreign Privacy Laws, Breach Notification Laws • High Cost of Information Breach ($4.8m average cost), immeasurable costs of brand damage , loss of customers • Requirements to eliminate the threat of data breach and non-compliance Cross-industry applicability • Retail, Hospitality, Travel and Transportation • Financial Services, Insurance, Banking • Healthcare • Telecommunications, Media and Entertainment • Manufacturing and Government
  52. 52. Please contact me for more information Ulf Mattsson, CTO Protegrity Ulf . Mattsson [at] protegrity . com
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