tokenization on the node - data protection for security and compliance

Tokenization on the Node - Data Protection for Security and Compliance

Ulf Mattsson, CTO

Protegrity

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What Is Tokenization on the Node ?

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• Strategic partnership since 2004

• Advocated solution for data protection on Teradata Databases

• 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

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Teradata and Protegrity

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

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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)

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Select Protegrity Customers

Select Protegrity Customers

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Data Breaches Gone Mad - Learn how to Secure your Data Warehouse Straight Away!

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www.protegrity.com

Who Are The Hackers and What Are They Doing?

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Some of you have already met Yuri.

10Source: http://www.youtube.com/user/ProtegrityUSA

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Last year he and his “anonymous” friends hacked AT&T.

11Source: http://www.youtube.com/user/ProtegrityUSA

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This year they hacked Sony and boughtBMW M5s.

Source: http://www.youtube.com/user/ProtegrityUSA

• Data including passwords and personal details were stored in clear text

• Attacks were not coordinated and not advanced

• Majority of attacks were SQL Injection dumps and Distributed Denial of Service (DDoS)

The Sony Breach

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

Source: http://www.youtube.com/user/ProtegrityUSA14

Then Yuri is going to buy a private jet.

Source: http://www.youtube.com/user/ProtegrityUSA15

*: Number of breaches

Source: 2011 Data Breach Investigations Report, Verizon Business RISK team and USSS

Business ServicesHealthcare

MediaTransportationManufacturingTech Services

GovernmentFinancial Services

RetailHospitality

0 5 10 15 20 25 30 35 40 45 %

Who Is The Next Target For Yuri?*

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Source: Trustwave Global Security Report 2011

Where is Yuri?

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So how does Yuri do it?

Source: http://www.youtube.com/user/ProtegrityUSA18

%

SocialMisuse

ErrorPhysicalMalwareHacking

0 20 40 60 80 100

*: Number of records

Source: 2011 Data Breach Investigations Report, Verizon Business RISK team and USSS

What Attack Methods Did Yuri Use?*

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“Usually, I just need one disgruntled employee. Just one.”

Source: http://www.youtube.com/user/ProtegrityUSA20

• 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

The Attack On RSA Security

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%

Third party monitoring service

Brag or blackmail by perpetrator

Internal fraud detection

Internal security audit or scan

Reported by employee

Unusual system behavior

Reported by customer/partner effected

Notified by law enforcement

Third party fraud detection

0 10 20 30 40 50*: Number of breaches

Source: 2011 Data Breach Investigations Report, Verizon Business RISK team and USSS

Do You Know If Yuri Hacked You?*

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Why Should I Care?

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• 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 http://www.csoonline.com/article/602313/the-changing-threat-landscape?page=2

Yuri Changed The Threat Landscape

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How Can We Secure The Sensitive Data

Flow?

We Need To Protect The Data Flow

Protected sensitive information

Unprotected sensitive information:

: Enforcement point

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What Has Industry Done

To Protect Itself?

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Source: PCI DSS Compliance Survey, Ponemon Institute

ID & credentialing system

Database scanning and monitoring (DAM)

Intrusion detection or prevention systems

Data loss prevention systems (DLP)

Endpoint encryption solution

Web application firewalls (WAF)

Correlation or event management systems

Identity & access management systems

Access governance systems

Encryption for data in motion

Anti-virus & anti-malware solution

Encryption/Tokenization for data at rest

Firewalls

0 10 20 30 40 50 60 70 80 90

WAF

DLP

DAM

%

What is Cost Effective Data Protection?

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AccessRight Level

Risk

Data Tokens

TraditionalAccessControl

IHigh

ILow

High –

Low -

Old and flawed:Minimal access levels so people can only carry out their jobs

New:CreativityHappens

At the edge

Source: InformationWeek Aug 15, 2011

Can New Data Security Help Creativity?

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What has Industry Done To

Protect Databases?

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How Did Data Security Evolve?

Year Event

2010 Memory Data Tokenization introduced as a fully distributed model

2005

Centralized Data Tokenization introduced with hosted payment service

DTP (Data Type Preserving encryption) used by in commercial databases

Attack on SHA-1 hash announcedDES was withdrawn

2001 AES (Advance Encryption Standard) accepted as a FIPS-approved algorithm

1988 IBM AS/400 used tokenization in shadow files1975 DES (Data Encryption Standard) draft submitted by IBM

1900 BC Cryptography used in Egypt

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123456 777777 1234

123456 123456 1234

aVdSaH 1F4hJ 1D3a

!@#$%a^///&*B()..,,,gft_+!@4#$2%p^&*Hashing -

Strong Encryption -

Alpha -

Numeric -

Partial -

Clear Text Data -

Intrusiveness (to Applications and Databases)

I

Original

!@#$%a^.,mhu7/////&*B()_+!@

666666 777777 8888Tokenizing or

FormattedEncryption

Data

Length

Sta

ndar

dE

ncry

ptio

n

How Can We Limit Changes to Applications?E

ncod

ing

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What Is The Next Step In Data Protection?

The Promise Of A Better World

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Replace Sensitive Data With Fake Data

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1234 5678 1234 5678

Random number

DataToken

Applications & Databases

: Data TokenProtected sensitive

information:

Unprotected sensitive information:

De-tokenization Tokenization

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Replace Sensitive Data With Data Tokens

Yuri Hates Tokens!

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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– Greatly improved transparency to systems and processes that need to be

protected• Result

– Reduced remediation– Reduced need for key management– Reduce the points of attacks– Reduce the PCI DSS audit costs for retail scenarios

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Tokens For PCI, PII & PHI

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Tokens Can Be More Flexible Than Encryption

Type of Data Input Token Comment

Token Properties

Credit Card 3872 3789 1620 3675 8278 2789 2990 2789 Numeric

Medical ID 29M2009ID 497HF390D Alpha-Numeric

Date 10/30/1955 12/25/2034 Date

E-mail Address ulf.mattsson@protegrity.com empo.snaugs@svtiensnni.snk Alpha Numeric, delimiters in input preserved

SSN Delimiters 075-67-2278 287-38-2567 Numeric, delimiters in input

Credit Card 3872 3789 1620 3675 8278 2789 2990 3675 Numeric, Last 4 digits exposed

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What Is The Impact On Performance And Scalability

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10 000 000 -

1 000 000 -

100 000 -

10 000 -

1 000 -

100 -

Transactions per second (16 digits)

I

Format

Preserving

Encryption

Speed of Different Protection Methods

I

Data

Type

Preservation

I

Modern

Data

Tokenization

I

AES CBC

Encryption

Standard

I

Basic

Data

Tokenization

Encryption

*: Speed will depend on the configuration41

I

Format

Preserving

Encryption

I

Data

Type

Preservation

I

Modern

Data

Tokenization

I

AES CBC

Encryption

Standard

I

Basic

Data

Tokenization

High

Low

Security

Level

Encryption

*: Speed will depend on the configuration42

Security of Different Protection Methods

Data Protection Methods

Data Protection Methods Performance Storage Security Transparency

System without data protection

Monitoring + Blocking + Masking

Data Type Preservation

Strong Encryption

Tokenization

Hashing

Best Worst

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The next step in data protection; Tokenization

How does Tokenization on Teradata Work?

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Token Server

Clique

Node

Node

Protegrity Agent

Protegrity Agent

AMP

AMP

AMP

AMP

AMP

AMP

AMP

AMP

The Bottleneck when Using Old Basic Tokenization

Credit CardNumber

Social Security Number

PassportNumber

Large footprint becomes larger

Replication becomes more complex

Solution may be unmanageable and expensive

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Modern Tokenization for Teradata Architecture

Clique

Node

Node

Protegrity Agent

Protegrity Agent

AMP

AMP

AMP

AMP

AMP

AMP

AMP

AMP

TokenizationOperations

TokenizationOperations

Small footprint

Small static token tables

High availability

High scalability

High performance

No replication required

No chance of collisions

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The World’s

Smallest & Fastest Tokenizer

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Performance Comparison

• Basic Tokenization– 5 tokens per second (outsourced)– 5000 tokens per second (in-house)

• Modern 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)

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What Is The Customer

Experience?

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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

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Faster PCI audit • Half that time• Qualified Security Assessors had no issues with the effective segmentation provided by Tokenization

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

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Case Study – Large Chain Store

How does Protegrity on Teradata Work?

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Protegrity Data Protection for TeradataClique

Policy Enforcement Agent

(UDF / UDT)

Node

Node

PEP Server

DeploymentServer

PEP Server

Log ProxyServer

Da

ta P

rote

ctio

nO

pe

ratio

ns

AMP

AMP

AMP

AMP

Da

ta P

rote

ctio

nO

pe

ratio

ns

AMP

AMP

AMP

AMP

Audit Logs

Policy

Enterprise Security Administrator (ESA)Enterprise Security Administrator (ESA)

Policy Management

Policy Management

Key Management

Key Management

Audit Management

Audit Management

Protected Data

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Protegrity in the ETL Process

SQL Server

ETL PlatformInformaticaData Stage

• Cleansing• Integration• Transformation

Sources TargetsTransformation

Teradata

EDW

Teradata Load P

rocessesAS/400

DB2

Original ValueNo AccessTokenMaskHash

Proteg

rity Policy R

ole B

ase

d A

ccess Control

Test Data

Oracle

Mainframe

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Data Masking is Not

Effective

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SystemType

Risk

Data Tokens

Data display Masking

IProduction

ITest / dev

Data Masking is Not Secure

High –

Low -

Data at rest Masking

IIntegration

testing

ITrouble

shooting

Exposure:Data in clear

before masking

Exposure:Data is only obfuscated

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Who Is

Protegrity?

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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

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Database Protector

File System Protector

Tokenization

Application Protector

Security Administrator

SSL Channel

Secure Distribution

AuditLog

Policy

Secure Collection POS e-commerce Branch

How To Securing The Sensitive Data Flow

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How Will This Improve My Life?

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Why Tokenization?

1. No masking needed

2. No encryption/decryption when using

3. No key management across enterprise

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Why Modern Tokenization?

1. Better – small footprint

2. Faster – high performance

3. Lower total cost of ownership

Tokenization Differentiators

Basic Tokenization Modern TokenizationFootprint Large, Expanding Small, Static

High Availability, Disaster Recovery

Complex, expensive replication required

No replication required

Distribution Practically impossible to distribute geographically

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

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Thank you!

Got Tokens?Meet Yuri at the

Protegrity booth #201

Q&Aulf.mattsson@protegrity.com

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