![© 2017 BLUETALON, INC. ALL RIGHTS RESERVED.
[1] Policies are Defined by Business within a Context
“Patient information and exams are sensitive data”
“Our contracts prohibit the use of machine diagnostics data
to redesign products”
“Service managers should be able to see only their fleet data”
10](https://image.slidesharecdn.com/2017-170223074613/85/Security-of-IoT-Data-Implementing-Data-Centric-Security-and-User-Access-Strategy-10-320.jpg)
![© 2017 BLUETALON, INC. ALL RIGHTS RESERVED.
[1] Policies Applied at the Data Layer Enables Business
Blocking
Enabling
11
“Patient information and exams are sensitive data”
“Our contracts prohibit the use of customer data outside west coast”](https://image.slidesharecdn.com/2017-170223074613/85/Security-of-IoT-Data-Implementing-Data-Centric-Security-and-User-Access-Strategy-11-320.jpg)
![© 2017 BLUETALON, INC. ALL RIGHTS RESERVED.
[1] Controlling Access for Different Users & Needs
Row filtering
Field level
Cell level
Sub-cell level masking
12
Joyce looks up
her data
Her manager looks
up Joyce’s data](https://image.slidesharecdn.com/2017-170223074613/85/Security-of-IoT-Data-Implementing-Data-Centric-Security-and-User-Access-Strategy-12-320.jpg)
![© 2017 BLUETALON, INC. ALL RIGHTS RESERVED.
[1] Example of Controls Directly Applied On Data
• Access to client account is conditional, based on zipcode
• Data is partially masked
Results
Rules on Data
13](https://image.slidesharecdn.com/2017-170223074613/85/Security-of-IoT-Data-Implementing-Data-Centric-Security-and-User-Access-Strategy-13-320.jpg)
![© 2017 BLUETALON, INC. ALL RIGHTS RESERVED.
[1] Attributes Bring Context to Policies
type
location
title / role
group
function
clearance level
LOB
user session
location
timestamp
application
connection type
data
sensitivity
clearance required
action requested
# of rows returned
data source
• Context helps assess whether the data request is legitimate
14](https://image.slidesharecdn.com/2017-170223074613/85/Security-of-IoT-Data-Implementing-Data-Centric-Security-and-User-Access-Strategy-14-320.jpg)
![© 2017 BLUETALON, INC. ALL RIGHTS RESERVED.
[2] Why Centralizing Policy Decisions, Distributing Enforcement?
• IoT Platforms and Predix are hybrid environments that make
use of multiple modern data management platforms:
– RDBMS
– Hadoop
– Spark
– Cassandra
– Cloud repositories
16
Cloud On-prem](https://image.slidesharecdn.com/2017-170223074613/85/Security-of-IoT-Data-Implementing-Data-Centric-Security-and-User-Access-Strategy-16-320.jpg)
![© 2017 BLUETALON, INC. ALL RIGHTS RESERVED.
[2] Centralize Decisions to Manage Polices from One Place
17
Authorization — what a user or a role can do with the data
Decision — against all rules, can a user see a data element
Enforcement — apply the decision at the time of user’s request](https://image.slidesharecdn.com/2017-170223074613/85/Security-of-IoT-Data-Implementing-Data-Centric-Security-and-User-Access-Strategy-17-320.jpg)
![© 2017 BLUETALON, INC. ALL RIGHTS RESERVED. 20
[3] Visibility Leverage the Same Enforcement Points](https://image.slidesharecdn.com/2017-170223074613/85/Security-of-IoT-Data-Implementing-Data-Centric-Security-and-User-Access-Strategy-20-320.jpg)
Uploaded byAltoros
Security of IoT Data: Implementing Data-Centric Security and User Access Strategy
The document outlines key requirements for data security in Predix, emphasizing the importance of business context in policy definitions, automated enforcement across platforms, and maintaining user experience. It also discusses the rise of enforcement actions and regulatory trends that impact industrial businesses, highlighting the need for centralized policy decisions and visibility over data activities. Bluetalon's approach focuses on data-centric security and consistent policy application to improve efficiency and compliance.
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Security of IoT Data: Implementing Data-Centric Security and User Access Strategy
- 1. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. 1
- 2. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. Outline 2 • Introduction and Context – Key requirements for data security in Predix – Managing data rights across platforms • Principles applied (BlueTalon + Predix Team) 1. Bring business context to policies 2. Distribute enforcement, centralize policy decisions 3. Get visibility over data activities and actions requested • How BlueTalon Works
- 3. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. Predix: PaaS for the Industrial Internet 3
- 4. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. 4 Authorization Encryption Authentication Auditing Masking Security within PaaS for the Industrial Internet Data Network Application Endpoint IaaS
- 5. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. BlueTalon: Data-Centric Security on Predix PaaS 5 Cloud management Edge enforcement
- 6. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. 6 • Security applied one system at a time – Inconsistency, duplication, chaos • Security applied with a central policy – Consistency, efficiency, simplicity BlueTalon: Data-Centric Security Across Data Platforms
- 7. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. Security and Regulatory Trends that Affect Industrial Businesses EAR violations criminal fines increased in excess of 5,000% YTY in 2014 and almost 1,000% in administrative penalties (Source: US DOC BIS) Enforcement is on the Rise ITAR violation risks • Significant Accrual of Fines • Denial of Export Privileges • Mandatory Increase in Staffing • Regular External Audits • Loss of, or Completely Damaged, Public Reputation Due to Facebook European Privacy Violations and NSA practices on Oct 6, 2015 European Court of Justice declares Safe Harbor invalid Facebook is facing daily fines of €250,000 in Belgium after a data protection court ruling for illegally tracking data from non members Results of the 2013 Data Breach • CEO steps down • 46% drop in profits due to reputational damage with $1.47 negative affect on EPS • $100M Cost to upgrade affected systems • 2014 SEC filing – Total expenses $252M • On going settlement costs Fines are Higher Data Breaches Affect the Bottom Line 5
- 8. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. Data Security Requirements in Predix 1. Data owners and regulatory entities define policies of use - Definition of policies must be owned by business stakeholders 2. Automated enforcement of policies across data platforms - Policies managed by business owners must be enforceable independent of data platform - Enforcement of these policies must be demonstrable to auditors - Enforcement must distributed and consistent 3. Preserving end user experience is paramount - End users of the data must be able to use any tool they want - Policy management processes/applications must be focused on business users 8
- 9. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. Outline 9 • Introduction and Context – Key requirements for data security in Predix – Managing data rights across platforms • Principles applied (BlueTalon + Predix Team) 1. Bring business context to policies 2. Distribute enforcement, centralize policy decisions 3. Get visibility over data activities and actions requested • How BlueTalon Works
- 10. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. [1] Policies are Defined by Business within a Context “Patient information and exams are sensitive data” “Our contracts prohibit the use of machine diagnostics data to redesign products” “Service managers should be able to see only their fleet data” 10
- 11. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. [1] Policies Applied at the Data Layer Enables Business Blocking Enabling 11 “Patient information and exams are sensitive data” “Our contracts prohibit the use of customer data outside west coast”
- 12. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. [1] Controlling Access for Different Users & Needs Row filtering Field level Cell level Sub-cell level masking 12 Joyce looks up her data Her manager looks up Joyce’s data
- 13. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. [1] Example of Controls Directly Applied On Data • Access to client account is conditional, based on zipcode • Data is partially masked Results Rules on Data 13
- 14. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. [1] Attributes Bring Context to Policies type location title / role group function clearance level LOB user session location timestamp application connection type data sensitivity clearance required action requested # of rows returned data source • Context helps assess whether the data request is legitimate 14
- 15. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. Outline 15 • Introduction and Context – Key requirements for data security in Predix – Managing data rights across platforms • Principles applied (BlueTalon + Predix Team) 1. Bring business context to policies 2. Distribute enforcement, centralize policy decisions 3. Get visibility over data activities and actions requested • How BlueTalon Works
- 16. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. [2] Why Centralizing Policy Decisions, Distributing Enforcement? • IoT Platforms and Predix are hybrid environments that make use of multiple modern data management platforms: – RDBMS – Hadoop – Spark – Cassandra – Cloud repositories 16 Cloud On-prem
- 17. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. [2] Centralize Decisions to Manage Polices from One Place 17 Authorization — what a user or a role can do with the data Decision — against all rules, can a user see a data element Enforcement — apply the decision at the time of user’s request
- 18. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. Example from Spark on Edge with BlueTalon on Predix 18
- 19. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. Outline 19 • Introduction and Context – Key requirements for data security in Predix – Managing data rights across platforms • Principles applied (BlueTalon + Predix Team) 1. Bring business context to policies 2. Distribute enforcement, centralize policy decisions 3. Get visibility over data activities and actions requested • How BlueTalon Works
- 20. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. 20 [3] Visibility Leverage the Same Enforcement Points
- 21. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. Visibility into Data Activity • Complete audit trail of data usage with contextual information • Key to detection of unusual data access patterns • Tracks policy changes to ensure compliance What policy was triggered Original and modified queriesWhat they tried to do 21
- 22. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. Feedback Loop with Visibility and Control 22 DATADataVisibility Control
- 23. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. 23 Examples of Data Security Visibility Reports
- 24. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. Outline 24 • Introduction and Context – Key requirements for data security in Predix – Managing data rights across platforms • Principles applied (BlueTalon + Predix Team) 1. Bring business context to policies 2. Distribute enforcement, centralize policy decisions 3. Get visibility over data activities and actions requested • How BlueTalon Works
- 25. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. How BlueTalon Delivers Data-Centric Security Security Admins Hadoop RDBMS Business Users, Data Scientists, Developers BlueTalon Enforcement Points Any Application Data Repositories BlueTalon Policy Engine BlueTalon Audit Engine CloudSpark NoSQL 25
- 26. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. 26 High Performance with BlueTalon Single digit (<3%) overhead. Unnoticeable by end users! 6.9 125.55 7.03 124.98 Teragen Terasort Files (1TB, mins) Without BlueTalon With BlueTalon Queries Tested in EMC lab Tested on GE Predix platform
- 27. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. How BlueTalon Works Data Repositories Applications Business Users, Data Scientists, Developers BlueTalon Enforcement Points BlueTalon Policy Engine Active Directory USER REQUEST 2 3 USER REQUEST 4 MODIFIED, COMPLIANT REQUEST COMPLIANT RESULTS5 6 BlueTalon Auditing Security Admins 1 BlueTalon Policy Console Security Admins 27
- 28. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. Outline 28 • Introduction and Context – Key requirements for data security in Predix – Managing data rights across platforms • Principles applied (BlueTalon + Predix Team) 1. Bring business context to policies 2. Distribute enforcement, centralize policy decisions 3. Get visibility over data activities and actions requested • How BlueTalon Works
- 29. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. “Organizations expecting to implement big data projects should consider BlueTalon wherever sensitive data is or may be exposed.” Merv Adrian, Gartner Group, “Cool Vendors in DBMS”, 2016 29
- 30. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. Contact use today! 30 • What’s your use case? – Contact us today at 1-888-534-7154 or info@bluetalon.com • Download BlueTalon today! • http://pages.bluetalon-security.com/SecureAccess-for-WebHDFS
- 31. © 2017 BLUETALON,INC. ALL RIGHTS RESERVED. 31
Editor's Notes
- #4 Characteristics of Predix that inform security decisions Multiple data and analytics platforms provided as a single service Each service contains data storage and analytics tools integrated seamlessly Diverse user population with different needs Differentiated access to a variety of data elements at any level Highly regulated Industrial data Security controls need to accommodate a variety of regulations
- #5 Characteristics of Predix that inform security decisions Multiple data and analytics platforms provided as a single service Each service contains data storage and analytics tools integrated seamlessly Diverse user population with different needs Differentiated access to a variety of data elements at any level Highly regulated Industrial data Security controls need to accommodate a variety of regulations
- #6 Characteristics of Predix that inform security decisions Multiple data and analytics platforms provided as a single service Each service contains data storage and analytics tools integrated seamlessly Diverse user population with different needs Differentiated access to a variety of data elements at any level Highly regulated Industrial data Security controls need to accommodate a variety of regulations
- #8 Characteristics of Predix that inform security decisions Multiple data and analytics platforms provided as a single service Each service contains data storage and analytics tools integrated seamlessly Diverse user population with different needs Differentiated access to a variety of data elements at any level Highly regulated Industrial data Security controls need to accommodate a variety of regulations