Data Breaches Gone Mad. Straight Away! Wednesday September 28 th, 2011

Transcription

1 Data Breaches Gone Mad Learn how to Secure your Data Warehouse Straight Away! Wednesday September 28 th, 2011

2 Martin Willcox Director Product & Solutions Marketing Teradata Europe, Middle East & Africa

3 Ulf Mattsson CTO Protegrity The Tokenization Experts

4 Some of you have already met Yuri. 4 4 Source: protegrity

5 Last year he and his anonymous friends hacked AT&T. 5 5 Source: protegrity

6 Security vulnerability in a Website used by ipad customers 100,000 addresses and ipad identification numbers were exposed, including: New York Mayor FBI and NASA US Departments of Defense Executives from Google, Microsoft, Amazon and Goldman Sachs 6 Source 2010: protegrity

7 This year they hacked Sony and bought BMW M5s. 7 Source: protegrity

8 Data including passwords and personal details were stored in clear text Attacks were not coordinated and not advanced 8 Majority of attacks were SQL Injection dumps and Distributed Denial of Service (DDoS) protegrity

9 Next month Yuri plans to hit a major telco with the keys provided by a disgruntled employee. 9 Source: protegrity

10 Then Yuri is going to buy a private jet. 10 Source: protegrity

11 Hospitality Retail Financial Services Government Tech Services Manufacturing Transportation Media Healthcare Business Services *: Number of breaches % 11 Source: 2011 Data Breach Investigations Report, Verizon Business RISK team and USSS protegrity

12 12 Source: Trustwave Global Security Report 2011 protegrity

13 So how does Yuri do it? 13 Source: protegrity

14 Hacking Malware Physical Error Misuse Social *: Number of records % 14 Source: 2011 Data Breach Investigations Report, Verizon Business RISK team and USSS protegrity

15 Usually, I just need one disgruntled employee. Just one. 15 Source: protegrity

16 Attackers stole information about SecurID two-factor authentication 60 different types of customized malware Advanced Persistent Threat (APT) malware tied to a network in Shanghai A tool written by a Chinese hacker 10 years ago protegrity 16

17 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 detection Brag or blackmail by perpetrator Third party monitoring service *: Number of breaches % 17 Source: 2011 Data Breach Investigations Report, Verizon Business RISK team and USSS protegrity

18 Some issues have stayed constant: Threat landscape continues to gain sophistication Attackers will always be a step ahead of the defenders Different motivation, methods and tools today: We are fighting highly organized, well-funded crime syndicates and nations Move from detective to preventative controls needed Source: Forrester and protegrity 18

19 19 Payment card data Personal information Usernames, passwords Intellectual property Bank account data Medical records Classified information System information Sensitive organizational data *: Number of records Source: 2011 Data Breach Investigations Report, Verizon Business RISK team and USSS % protegrity

20 20 Firewalls Encryption/Tokenization for data at Anti-virus & anti-malware solution Encryption for data in motion Access governance systems Identity & access management systems Correlation or event management Web application firewalls (WAF) Endpoint encryption solution Data loss prevention systems (DLP) Intrusion detection or prevention Database scanning and monitoring ID & credentialing system IDS DAM DLP WAF Client encryption % *: Cost effective solutions for PCI DSS. Source: PCI DSS Compliance Survey, Ponemon Institute 20 protegrity

21 21 protegrity

22 Jim Browning Senior Security Engineer Teradata Labs

23 Teradata Protegrity Partnership Strategic partnership since 2004 Advocated solution for data protection on Teradata Databases Design and development of Protegrity data security platform for Teradata Proven parallel and scalable data protection for Teradata MPP platforms Collaboration on forward-looking roadmaps New and advanced data protection options Integration with new Teradata Database features Seamless operation on large data warehouse systems World-class customers 23

24 Teradata Protegrity Customers by Industry Transportation Manufacturing Utilities Telecommunications Retail Government Healthcare Financial 24

25 Types of Data Requiring Protection Credit Card Information Credit Card Numbers (PAN) Service Codes Expiration Dates Personal Identifying Information Social Security Numbers Tax Identifiers Drivers License Numbers Date of Birth Consumer Financial Data Account Numbers PINs Protected Health Information Identifiable Patient Data Medical Record Numbers Corporate Financial Data Non-public Information Human Resources Data Payroll Information Performance Ratings Customer and Prospect Data Trade Secrets and Intellectual Property 25

26 Protegrity Data Protection for Teradata A comprehensive data protection solution for Teradata Databases Provides additional separation of duties through a separate Security Manager interface for creation and maintenance of security policies Includes a patented key management system for secure key generation and protection of keys when stored Supports multiple data protection options including strong encryption and tokenization Supports multiple cryptographic algorithms and key strengths Automates the process of converting clear text data to cipher text 26

27 Protegrity Data Protection for Teradata A comprehensive data protection solution for Teradata Databases Provides additional access controls to protect sensitive information (even DBC can not see unencrypted data unless specifically authorized by the Security Manager) Includes additional auditing separate from database audit logs (such as the Access Log) Designed to fully exploit Teradata Database parallelism and scalability Enterprise-wide solution that works with most major databases and operating systems (not just Teradata) 27

28 Protegrity Data Protection for Teradata Architecture Enterprise Security Administrator (ESA) Policy Management Audit Logs Policy Clique Node Log Proxy Server Deployment Server PEP Server Data Protection Operations AMP AMP AMP AMP Policy Enforcement Agent (UDF / UDT) Protected Data Key Management Node Audit Management PEP Server Data Protection Operations AMP AMP AMP AMP 28

29 Data Protection Methods Strong Encryption AES(128,256) / 3DES DTP2 Data Type Preserving Encryption 2 Hashing HMAC SHA-1 DAM Data Activity Monitoring Masking Tokenization Strong Encryption Symmetric encryption Encrypted value can be used in database for joins, etc. Data Type Preserving Encryption 2 Preserves the data type and length of a protected column Hashing One way can not be decrypted Hashed value can be used in database for joins Data Activity Monitoring (DAM) Monitors access to sensitive columns without encrypting or hashing Can be used as a compensating control Masking Replaces sensitive characters in a string of data to render the data secure Customizable mask patterns Tokenization Provides inert values that can replace sensitive data in databases Can be used as a compensating control 29

30 Data Protection Considerations Performance Storage Security Transparency 30

31 Data Protection Methods Data Protection Methods Performance Storage Security Transparency System without data protection Monitoring + Blocking + Masking Format Controlling Encryption Strong Encryption Tokenization Hashing Best Worst 31

32 Replace Sensitive Data With Fake Data = Random number Data Token 32

33 Replace Sensitive Data with Fake Data Tokenization De-tokenization Applications & Databases : Data Token Unprotected sensitive information: Protected sensitive information: 33

34 What is Tokenization and What is the Benefit? Tokenization Tokenization is process that replaces sensitive data in systems with inert data called tokens which have no value to the thief Tokens resemble the original data in data type and length Benefit Result Greatly improved transparency to systems and processes that need to be protected Reduced remediation Reduced need for key management Reduce the points of attacks Reduce the PCI DSS audit costs for retail scenarios 34

35 Complexity when Using Basic Tokenization Clique Node AMP Large footprint becomes larger Replication becomes more complex Solution may be unmanageable and expensive Token Server Protegrity Agent AMP AMP AMP Node AMP Protegrity Agent AMP AMP AMP Credit Card Number Social Security Number Passport Number 35

36 Protegrity Tokenization for Teradata Architecture Clique Node Small footprint Small static token tables Protegrity Agent Tokenization Operations AMP AMP AMP AMP High availability High scalability High performance No replication required No chance of collisions Node Protegrity Agent Tokenization Operations AMP AMP AMP AMP 36

37 Performance Comparison Basic Tokenization 5 tokens per second (outsourced) 5000 tokens per second (in-house) Protegrity Tokenization 200,000 tokens per second (Protegrity) Single commodity server with 10 connections. Will grow linearly with additional servers and/or connections 9,000,000+ tokenizations per second (Protegrity /Teradata) 37

38 Protegrity Tokenization Differentiators Basic Tokenization Footprint Large, Expanding Small, Static Protegrity Tokenization High Availability, Disaster Recovery Distribution Complex, expensive replication required Practically impossible to distribute geographically No replication required Easy to deploy at different geographically distributed locations Reliability Prone to collisions No collisions Performance, Latency, and Scalability Will adversely impact performance & scalability Little or no latency. Fastest industry tokenization Extendibility Practically impossible Unlimited Tokenization Capability 38

39 Why Tokenization? No masking needed No encryption/decryption when using No key management across enterprise Why Protegrity Tokenization? Better small footprint Faster high performance Lower total cost of ownership 39

40 Flexibility for Different Forms of Data Type of Data Input Token Comment Token Properties Credit Card Numeric Medical ID 29M2009ID 497HF390D Alpha-Numeric Date 10/30/ /25/2034 Date Address Alpha Numeric, delimiters in input preserved SSN Delimiters Numeric, delimiters in input Credit Card Numeric, Last 4 digits exposed 40

41 Tokenization Case Studies Customer 1: Extensive enterprise End-to-End credit card data protection switching to Protegrity Tokenization Performance Challenge: Initial tokenization Vendor Lock-In: What if we want to switch payment processor? Performance Challenge: Operational tokenization (SLAs) Customer 2: Desired single vendor to provide data protection including tokenization Combined use of tokenization and encryption Looking to expand tokens beyond CCN to PII Customer 3: Reduce compliance cost. 50 million Credit Cards, 700 million daily transactions Performance Challenge: Initial tokenization End-to-End Tokens: Started with the EDW and expanding to stores 41

42 Case Study Large Chain Store By segmenting cardholder data with tokenization, a regional chain of 1,500 local convenience stores is reducing its PCI audit from seven to three months We planned on 30 days to tokenize our 30 million card numbers. With Protegrity Tokenization the whole process took about 90 minutes Qualified Security Assessors had no issues with the effective segmentation provided by Tokenization With encryption, implementations can spawn dozens of questions There were no such challenges with tokenization 42

43 Case Study Large Chain Store Faster PCI audit Half that time Lower maintenance cost Do not have to apply all 12 requirements of PCI DSS to every system Better security Ability to eliminate several business processes such as generating daily reports for data requests and access Strong performance Rapid processing rate for initial tokenization Sub-second transaction SLA 43

44 Protegrity in the ETL Process Sources Transformation Targets SQL Server DB2 AS/400 Mainframe ETL Platform Informatica Data Stage Cleansing Integration Transformation Teradata Load Processes Teradata EDW Protegrity Policy Role Based Access Control Original Value No Access Token Mask Hash Test Data Oracle 44

45 Protegrity Data Security Platform in Action Secure Collection POS e-commerce Branch Secure Distribution Policy Audit Log Tokenization Database Protector Security Administrator Application Protector File System Protector 45

46 Why Protegrity? Protegrity s Tokenization allows compliance across: PCI PII PHI Innovative: Pushing data protection with industry leading innovation such as out patented database protection system and the Protegrity Tokenization Proven: Proven platform currently protects the worlds largest companies Experienced: Experienced staff will be there with support along the way to complete data protection 46

47 Q&A Contacts: Protegrity: Teradata:

48 Thank you! Data Breaches Gone Mad Learn how to Secure your Data Warehouse Straight Away!