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Cloud Security Eirik Thormodsrud UiO, 28.03.2019

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Page 1: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

Cloud Security

Eirik ThormodsrudUiO, 28.03.2019

Page 2: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

• Cloud aspects

• Cloud security in general

• Compliance

• Software architecture in cloud

Agenda

Page 3: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

Cloud aspects and security

Page 4: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

NIST Special Publication 800-145: Definition of Cloud Computing:

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computingresources (e.g., networks, servers, storage, applications, and services) that can be

rapidly provisioned and released with minimal management effort or service provider interaction.

Cloud definition

Page 5: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

Cloud models

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Cloud models

https://blogs.technet.microsoft.com/kevinremde/2011/04/03/saas-paas-and-iaas-oh-my-cloudy-april-part-3/

SaaS

PaaS

IaaS

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Tenancy

Single tenancy Multi tenancy

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Tenancy

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Redundancy

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Location issues

https://azure.microsoft.com/en-us/global-infrastructure/regions/

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Location issues

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Location issues

https://azure.microsoft.com/en-us/blog/the-network-is-a-living-organism/

Page 13: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

Compliance - Azure

Page 14: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

• Forumlate an exit strategy from the start

• Solution design must support exit strategy

• Assess vendor, solutions and components regularly

• Consider backup with anotherprovider (or on premise)

• Everything based on risk assessments

Exit strategi

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• Literature:• Cloud Security Alliance: https://cloudsecurityalliance.org/

• https://www.ncsc.gov.uk/guidance/implementing-cloud-security-principles

• NIST Cloud Computing Reference Architecture 500-292

• NIST Definition of Cloud Computing 800-145

• Tools:• Cloud Service Providers tools and documentation

• Azure: https://azure.microsoft.com/en-us/solutions/architecture/

• AWS: https://media.amazonwebservices.com/architecturecenter/

• Netflix (Amazon AWS)

• Security monkey: https://netflix.github.io/

• Spotify (Google Cloud)

• Google Cloud Security Toolbox: https://labs.spotify.com/

Useful tools

Page 16: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

In short:

Risk assessments arenecessary!

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Software architecture

Cloud

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Example application

VPC - production

Elastic Load Balancing

(ELB)

Database

CodeDeploy

API Gateway

Amazon Simple Storage Service (S3)

Users

VPC - dev

Application

VPC - management

AWS Management Console

Page 19: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

• The identity of all users accessing protected content must be ascertained

• Authentication mechanism strength should match risk level• Web application users might use only U + P

• Admins, developers etc use MFA

• Federate with other organizations• Business partners Active Directory or similar

• Google, Facebook etc for end users

• Store local identities securely• Use built in mechanisms – writing your own can be dangerous

Security mechanisms: Authentication

Security goal: Properly authenticate all entitieswhich communicate with the solution

Page 20: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

• All operations from users must be subject to authorizationchecks• Ensure that all API calls etc are properly protected

• Usual access schemes are based on roles and/or attributes

• RBAC – user is in administrators, web users group etc

• ABAC – information which can be attributed to the user is considered when giving access, e.g.• Organisational level

• Physical location

• Device type

• RBAC

Security mechanisms: Authorization

Security goal: All access to information shall be permitted on a principle of least privilege

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• All information in the system must be identified

• Identified information must be classified according to value

• Access to data must be subject to proper authorizationdepending on value and need for exposure

• Not all data needs to be exposed, consider tokenization ifpossible/handy

• Tokenization is the process of creating a non-sensitive reference to sensitive data.• Reference (token) can be more freely distributed

• Token can be sent to system containing sensitive information for verification

Security mechanisms: Information control

Security goal: All information shall be classified and and protected according to value

Page 22: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

• Only expose parts of the application you mean to expose

• Controll all API calls and methods• E.g. limit HTTP methods to only allowed methods per API call etc

• Ensure only public API calls are exposed outside of system

• Protect all sensitive APIs with proper authentication and authorization checks

• Ensure network segmentation• Most cloud providers micro segment all services

• Verify your exposure through both:• Built in management tools

• Scan and test your system from external addresses

Security mechanisms: Limit exposure

Security goal: The system shall only expose necessaryfunctionality

Page 23: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

• Two main areas of coverage:• Protection of information in transit

• Protection of information at rest

• Transit protection most commonly used• E.g. TLS, SSH etc.

• Protects against eavesdroppers

• At rest:• Used for high risk objects such as phones, laptops etc

• More and more use in cloud for storage

• Protect against access from administrator

Security mechanisms: Encryption

Security goal: Protect information againstunauthorized access and disclosure

Page 24: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

• Main challenges are:• Choice of algorithms and modes

• Protection of keys

• Algorithms and modes should be chosen based on industryrecommendations• Use known implementations, e.g. Tink from Google

• Check against Enisa or similar sources

• Protecting keys are vitally important, both from unauthorizedaccess to loosing the keys• Consider HSMs or other key storage mechanisms

• Most (all) cloud providers support HSM through either SW or HW

Security mechanisms: Encryption part 2

Security goal: Protect information againstunauthorized access and disclosure

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• Secrets are:• username/passwords

• Certificates

• API keys

• Encryption keys

• etc

• Secrets are very often hardcoded into code or included in config files

• Parameterize secrets in code

• Store secrets in central protected repositories to avoidexposure

Security mechanisms: Protection of secrets

Security goal: Protect credentials and encryption keysfrom unauthorized access

Page 26: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

• Enforce required security configuration through technicalmechanisms such as policies/templates

• Ensure that all environments are properly protected accordingto sensitivity and criticality

• Automate the enforcement of policies/templates

• Automate deployment to production – don’t open up for direct changes

• Establish security patterns• Normal application flow with desired security controls

Security mechanisms: Standardization and automation

Security goal: The system shall be based on componentsand solutions which can be automated and standardized

Page 27: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

• Log all actions in the system• Both user access and admin/dev actions

• Log to a central repository• Log what you need for the required length of time

• Limit access to sensitive details in logs such as PII etc

• Leverage cloud capabilities such as machine learning and analytics for log analysis

• Use built in compliance checks to ensure that no breach ofpolicies are undetected

Security mechanisms: Audit

Security goal: All actions in the system shall be logged in order to ensure tracebility

Page 28: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

Example application - again

CodeDeploy

VPC - dev

Users Database

VPC - production

Elastic Load Balancing

(ELB)API Gateway

Simple Storage Service (S3)

Application

VPC - management

Management Console

Page 29: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

Example application - authentication and authorization

CodeDeploy

VPC - dev

Users Database

VPC - production

Elastic Load Balancing

(ELB)API Gateway

Simple Storage Service (S3)

Application

VPC - management

Management Console

Cognito Identity and Access

Management (IAM)

Page 30: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

Example application - information control

CodeDeploy

VPC - dev

Users Database

VPC - production

Elastic Load Balancing

(ELB)API Gateway

Simple Storage Service (S3)

Application

VPC - management

Management Console

Cognito Identity and Access

Management (IAM)Macie

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Example application - limit exposure

CodeDeploy

VPC - dev

Users Database

VPC - production

Elastic Load Balancing

(ELB)API Gateway

Simple Storage Service (S3)

Application

VPC - management

Management Console

Cognito Identity and Access

Management (IAM)Macie

Firewall Manager

Shield

WAF

Page 32: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

CodeDeploy

VPC - dev

Users Database

VPC - production

Elastic Load Balancing

(ELB)API Gateway

Simple Storage Service (S3)

Application

VPC - management

Management Console

Cognito Identity and Access

Management (IAM)Macie

Firewall Manager

Shield

WAF

Example application - encryption

CloudHSMKey Management Service Certificate Manager

Page 33: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

CodeDeploy

VPC - dev

Users Database

VPC - production

Elastic Load Balancing

(ELB)API Gateway

Simple Storage Service (S3)

Application

VPC - management

Management Console

Cognito Identity and Access

Management (IAM)Macie

Firewall Manager

Shield

WAF

Example application – protection ofsecrets

CloudHSMKey Management Service Certificate ManagerSecrets Manager

Page 34: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

CodeDeploy

VPC - dev

Users Database

VPC - production

Elastic Load Balancing

(ELB)API Gateway

Simple Storage Service (S3)

Application

VPC - management

Management Console

Cognito Identity and Access

Management (IAM)Macie

Firewall Manager

Shield

WAF

Example application –standardization and automation

CloudHSMKey Management Service Certificate ManagerSecrets Manager

Systems Manager CloudFormation

Page 35: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

CodeDeploy

VPC - dev

Users Database

VPC - production

Elastic Load Balancing

(ELB)API Gateway

Simple Storage Service (S3)

Application

VPC - management

Management Console

Cognito Identity and Access

Management (IAM)Macie

Firewall Manager

Shield

WAF

Example application – audit

CloudHSMKey Management Service Certificate ManagerSecrets Manager

Systems Manager CloudFormation

CloudTrailGuardDuty Inspector

Security Hub

Page 36: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

Reference architecture – AWS - PCI DSS

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Reference architecture – Azure - PCI DSS

Page 38: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

To summarize:

Risk assessments arenecessary!

Page 39: Cloud Security - Universitetet i oslo · •Limit access to sensitive details in logs suchas PII etc •Leverage cloud capabilities such as machine learning and analytics for log

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