The good, the bad and the ugly of the target data breach

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The landscape of threats to sensitive data is rapidly changing.  New technologies bring with them new vulnerabilities, and organizations like Target are failing to react properly to the shifts around them. What's needed is an approach equal to the persistent, advanced attacks companies face every day.  The sooner we start adopting the same proactive thinking hackers are using to get at our data, the better we will be able to protect it. 

This webinar will cover:
Data security today, the landscape, etc.
Discuss a few recent studies and changing threat landscape
The Target breach and other recent breaches
The effects of new technologies on breaches
Shifting from reactive to proactive thinking
Preparing for future attacks with new techniques

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The good, the bad and the ugly of the target data breach

  1. 1. The Good, The Bad and The Ugly of The TargetThe Good, The Bad and The Ugly of The Target Data Breach Ulf Mattsson CTO, Protegrity Ulf.Mattsson@protegrity.com
  2. 2. Working with the Payment Card Industry Security Standards Council (PCI SSC): • PCI SSC Tokenization Task Force - Guidelines • PCI SSC Encryption Task Force • PCI SSC Point to Point Encryption Task Force • PCI SSC Risk Assessment SIG Ulf Mattsson & PCI Data Security Standards • PCI SSC eCommerce SIG • PCI SSC Cloud SIG • PCI SSC Virtualization SIG • PCI SSC Pre-Authorization SIG • PCI SSC Scoping SIG • PCI SSC 2013 – 2014 Tokenization Task Force – Technical Standard 2
  3. 3. Data security today The Target breach New environments bring new vulnerabilities Topics New environments bring new vulnerabilities Thinking like a hacker - proactive data security New technologies & approaches to properly secure data 3
  4. 4. DATA SECURITY TODAYTODAY 4 How have the methods of attack shifted?
  5. 5. Worries of 800 IT Pros 5 Source: 2014 Trustwave Security Pressures Report
  6. 6. Data Loss Worries IT Pros Most 6 Source: 2014 Trustwave Security Pressures Report
  7. 7. “It’s clear the bad guys are winning at a faster rate than the good guys are winning, and we’ve The Bad Guys are Winning 7 Source: searchsecurity.techtarget.com/news/2240215422/In-2014-DBIR-preview-Verizon-says-data-breach-response-gap-widening are winning, and we’ve got to solve that.” - 2014 Verizon Data Breach Investigations Report
  8. 8. We Are Losing Ground “…Even though security is improving, things are getting worse faster, so we're losing ground 8 we're losing ground even as we improve.” - Security expert Bruce Schneier Source: http://www.businessinsider.com/bruce-schneier-apple-google-smartphone-security-2012-11
  9. 9. Organizations are Not Protected Against Cyberattacks “Cyber attack fallout could cost the global economy $3 trillion by 2020.” 9 Source: McKinsey report on enterprise IT security implications released in January 2014. 2020.” - McKinsey & Company report Risk & Responsibility in a Hyperconnected World: Implications for Enterprises
  10. 10. TARGET DATA BREACHBREACH 10 What can we learn from the Target breach?
  11. 11. Target Data Breach, U.S. Secret Service & iSIGHT Target CIO Beth Jacob resigned 11
  12. 12. Memory Scraping Malware – Target Breach Payment Card Terminal Point Of Sale Application Memory Scraping Malware Authorization, Settlement … Web Server Memory Scraping Malware Russia 12
  13. 13. Credentials were stolen from Fazio Mechanical in a malware- injecting phishing attack sent to employees of the firm by email • Resulted in the theft of at least 40 million customer records containing financial data such as debit and credit card information • In addition, roughly 70 million accounts were compromised that included addresses and mobile numbers The data theft was caused by the installation of malware on How The Breach at Target Went Down the firm's point of sale machines The subsequent file dump containing customer data is reportedly flooding the black market • Starting point for the manufacture of fake bank cards, or provide data required for identity theft. Source: Brian Krebs and www.zdnet.com/how-hackers-stole-millions-of-credit-card-records-from-target-7000026299/ 13
  14. 14. The FTC is probing the massive hack of credit card information Target could face federal charges for failing to protect its customers' data from hackers When you see a data breach of this size with clear harm to consumers, it's clearly something that the Target May Face Federal Suit Over Privacy Fumble harm to consumers, it's clearly something that the FTC would be interested in looking at," said Jon Leibowitz, a former FTC chairman Sen. Richard Blumenthal, a Connecticut Democrat, urged the FTC to investigate the Target hack soon after it became public in December Source: Bloomberg Businessweek 14
  15. 15. WHO IS THE NEXT TARGET?TARGET? 15
  16. 16. Who Is The Next Target? 16
  17. 17. It’s not like other businesses are using some special network security practices that Target doesn’t know about. They just haven’t been hit yet. No number of traps, bars, or alarms will keep out the determined thief Source: www.govtech.com/security 17
  18. 18. Who is the Next Target? Services Retailers 18 Healthcare Government
  19. 19. BEWARE MALWAREBEWARE MALWARE 19
  20. 20. FBI uncovered 20 cyber attacks against retailers in the past year that utilized methods similar to Target incident Believe POS malware crime will continue to grow over the near term Despite law enforcement and security firms' actions to mitigate it FBI Memory-Scraping Malware Warning mitigate it Report: “Recent Cyber Intrusion Events Directed Toward Retail Firms” Source: searchsecurity.techtarget.com/news/2240213143/FBI-warns-of-memory-scraping- malware-in-wake-of-Target-breach 20
  21. 21. 21
  22. 22. New Malware Source: mcafee.com/us/resources/reports/rp-quarterly-threat-q3-2013.pdf 22
  23. 23. Total Malicious Signed Malware Source: mcafee.com/us/resources/reports/rp-quarterly-threat-q3-2013.pdf 23
  24. 24. Targeted Malware Topped the Threats 24 Source: 2014 Trustwave Security Pressures Report
  25. 25. US - Targeted Malware Top Threat 25 Source: 2014 Trustwave Security Pressures Report
  26. 26. BIG DATA PROBLEMSPROBLEMS What effect, if any, does the rise of “Big Data” have on breaches? 26
  27. 27. Has Your Organization Already Invested in Big Data? 27 Source: Gartner
  28. 28. Holes in Big Data… 28 Source: Gartner
  29. 29. Many Ways to Hack Big Data 29 Hackers & APT Rogue Privileged Users Unvetted Applications Or Ad Hoc Processes
  30. 30. Many Ways to Hack Big Data MapReduce (Job Scheduling/Execution System) Pig (Data Flow) Hive (SQL) Sqoop ETL Tools BI Reporting RDBMS Avro(Serialization) Zookeeper(Coordination) Hackers Unvetted Applications Or Ad Hoc Processes Source: http://nosql.mypopescu.com/post/1473423255/apache-hadoop-and-hbase 30 HDFS (Hadoop Distributed File System) Hbase (Column DB) Avro(Serialization) Zookeeper(Coordination) Privileged Users
  31. 31. Big Data (Hadoop) was designed for data access, not security Security in a read-only environment introduces new challenges Massive scalability and performance requirements Big Data Vulnerabilities and Concerns Sensitive data regulations create a barrier to usability, as data cannot be stored or transferred in the clear Transparency and data insight are required for ROI on Big Data 31
  32. 32. THINKING LIKE A HACKERHACKER How can we shift from reactive to proactive thinking? 32
  33. 33. How do hackers think? Like a business. Go where the money is Thinking Like A Hacker Multiple touches to get in Easier targets = Higher ROI
  34. 34. The Modern Day Bank Robber 34
  35. 35. COMPLIANCE VS. SECURITYSECURITY 35
  36. 36. Target was certified as meeting the standard for the payment card industry in September 2013 Compliance can protect us from liability, but whether it actually protects us from loss of business and loss of data is not so clear Compliance is a minimal deterrent that everyone Target Breach Lesson: PCI Compliance Isn't Enough Compliance is a minimal deterrent that everyone has to have in place If you're driving a car, you're expected to have a driver's license. That doesn't make you a safe driver Source: TechNewsWorld 36
  37. 37. Protection of cardholder data in memory Clarification of key management dual control and split knowledge Recommendations on making PCI DSS business-as- usual and best practices Security policy and operational procedures added PCI DSS 3.0 Security policy and operational procedures added Increased password strength New requirements for point-of-sale terminal security More robust requirements for penetration testing 37
  38. 38. TURNING THE TIDE 38 What new technologies and techniques can be used to prevent future attacks?
  39. 39. What if a Social Security number or Credit Card NumberCredit Card Number in the Hands of a Criminal was Useless? 39
  40. 40. Coarse Grained Security • Access Controls • Volume Encryption • File Encryption Fine Grained Security Evolution of Data Security Methods Time Fine Grained Security • Access Controls • Field Encryption (AES & ) • Masking • Tokenization • Vaultless Tokenization 40
  41. 41. Old and flawed: Minimal access levels so people can only carry Access Control Risk High – can only carry out their jobs 41 Access Privilege Level I High I Low Low –
  42. 42. Applying the Protection Profile to the Structure of each Sensitive Data Fields allows forSensitive Data Fields allows for a Wider Range of Granular Authority Options 42
  43. 43. Risk High – Old: Minimal access levels – Least New : Much greater The New Data Protection - Tokenization Access Privilege Level I High I Low Low – levels – Least Privilege to avoid high risks Much greater flexibility and lower risk in data accessibility 43
  44. 44. Examples: De-Identified Sensitive 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 44
  45. 45. 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 45 Personally Identifiable Information
  46. 46. Tokenization Research Tokenization Gets Traction Aberdeen has seen a steady increase in enterprise use of tokenization for protecting sensitive data over encryption Nearly half of the respondents (47%) are currently using tokenization for something other than cardholder data Tokenization users had 50% fewer security-related incidents than tokenization non-users 46 Source: http://www.protegrity.com/2012/08/tokenization-gets-traction-from-aberdeen/
  47. 47. Security of Different Protection Methods High Security Level I Format Preserving Encryption I Vaultless Data Tokenization I AES CBC Encryption Standard I Basic Data Tokenization 47 Low
  48. 48. Fine Grained Data Security Methods Tokenization and Encryption are Different Used Approach Cipher System Code System Cryptographic algorithms Cryptographic keys TokenizationEncryption 48 Cryptographic keys Code books Index tokens Source: McGraw-HILL ENCYPLOPEDIA OF SCIENCE & TECHNOLOGY
  49. 49. 10 000 000 - 1 000 000 - 100 000 - 10 000 - Transactions per second* Speed of Different 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 49
  50. 50. Different Tokenization Approaches Property Dynamic Pre-generated Vaultless Vault-based 50
  51. 51. Protecting Enterprise Data Flow 123456 123456 1234 CCN/SSN Social Media Blogs Smart Phones Meters Sensors Web Logs Trading Systems GPS Signals Stream 051 123456 999999 1234 Protecting Data Flows – Reducing Attack Surface Big Data (Hadoop) Aquisition Analytics & Visualization Enterprise Data Warehouse
  52. 52. Current Breach Discovery Methods 52 Verizon 2013 Data-breach-investigations-report & 451 Research
  53. 53. You must assume the systems will be breached. Once breached, how do you know you've been compromised? You have to baseline and understand what 'goodness' looks like and look for deviations from goodness McAfee and Symantec can't tell you what normal looks like in your own systems. Only monitoring anomalies can do that CISOs say SIEM Not Good for Security Analytics Only monitoring anomalies can do that Monitoring could be focused on a variety of network and end-user activities, including network flow data, file activity and even going all the way down to the packets Source: 2014 RSA Conference, moderator Neil MacDonald, vice president at Gartner 53
  54. 54. Use Big Data to Analyze Abnormal Usage Pattern Payment Card Terminal Point Of Sale Application Memory Scraping Malware Authorization, Settlement … Web Server Memory Scraping Malware Moscow, Russia FireEye Malware?
  55. 55. Trend - Open Security Analytics Frameworks 55 Source: Emc.com/collateral/white-paper/h12878-rsa-pivotal-security-big-data-reference-architecture Enterprise Big Data Lake
  56. 56. Conclusions Changing threat landscape & challenges to secure data: • Attackers are looking for not just payment data – a more serious problem. • IDS systems are lacking context needed to catch data theft • SIEM detection is too slow in handling large amounts of events. What happened at Target? • Modern customized malware can be very hard to detect 56 • They were compliant, but not secure How can we prevent what happened to Target and the next attack against our sensitive data? • Assume that we are under attack - proactive protection of the data itself • We need Big Data event information analysis & context to catch modern attackers • Use security methods that require less cleartext in use, such as tokenization
  57. 57. Thank you! Questions? Please contact me for more information Ulf.Mattsson@protegrity.com
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