COMPX527 Lecture 6.1
Assignment 2
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Cloud Data Security
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Assignment 2
• Cloud Based Application Development
• Live Demo and Presentation
• Report
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Assignment 2
• Work in a group
• Start early
• Project Management is Important
– Trello Board
– Shared workspace, google drive, github
• Communication is also important
– Set up regular meetings
• Document what you are doing and often.
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Example
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Using Open Street Maps and road fatality data from
NZTA to provide safest route between source and
destination
Solution Architecture
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Limit Your Expenditure
• Each group will be given a fixed budget
– You need to stay within your budget
• Monitor your services
– Set up cloudwatch alarms
• Only switch the services on when being used
• Do not leave services on for long period of times
– If we see a service not being used we will terminate it at our end which may result in loss
of data on your end.
• You may be allowed extra budget but you will need to make a case for it.
– For example, an expensive service that is critical to the project outcome
• Monitor your budget
– If you go over your budget and have no reasonable justification you may not
be allowed more credits thus affecting the project and your grade
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Security Considerations
• Identity and Access Management
– Team members/Employees
– Applications
– Users
• Data Security
– Application Data
– User Data
• Other appropriate security considerations
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Data
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All computing systems are built to
consume, serve and / or manipulate data
Data
• User data
– Financial Data
– Personally Identifiable Information
– Etc.
• Employee Data
– Accounting data
– Personally Identifiable Information
– Etc.
• Business Data
• Operational Data
• Security Data
– Keys,
– Information used for MFA
– Etc.
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Data in various cloud service models
• Data in IaaS
– Volume Storage
• Volumes attached to IaaS instances, usually as a virtual hard drive.
Examples Amazon EBS, VMware VMFS
– Object Storage
• Object storage also referred as file storage. Instead of virtual hard
drives, object storage is like share file accessed via APIs or web
interface
– Raw Storage
• Includes physical media where data is stored. May be mapped for
direct access in certain private cloud configuration.
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Data in various service models
• Data in PaaS
– Structured Data (Database as a Service)
• A multitenant database architecture that is directly
consumable as a service. Databases may be relational,
flat, or any other common structure. Example AWS
RDS, Azure MSSQL, Oracle offerings
– Unstructured Data(Big Data as a Service)
• Data is typically stored in Object Storage or another
distributed file system. Data typically needs to be close
to the processing environment. Example Google Big
Table
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Data in various service models
• Data in SaaS
– Information Storage and Management
• Data entered into the system using a web interface or
APIs. This data may further be stored on other PaaS or
IaaS data storages. Example Gmail etc.
– Content/File Storage
• File-based content is stored within the SaaS application
(reports, image files and documents). Example Dropbox
etc.
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What data to protect?
• How do I know what data to protect?
– Laws and Industry Regulation for Compliance
– GDPR, PCI-DSS, Privacy Act 2020, HIPPAA(US)
– Threat Model your business/application
– STRIDE, DREAD etc.
– Data Inventory and Classification
• High-level description of important information
categories.
• Label information into categories according to
sensitivity and value to the organisation
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What data to protect?
– Information Management Policies
• Policies to define what activities are allowed for
different information types
– Location and Jurisdiction Policies
• Where data may be geographically located, which also
has important legal and regulatory ramifications
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Data Security Life Cycle
Store
Share
Use Archive
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Create Destroy
Data Security Life Cycle
• Creation
– Creation is the generation of new digital content,
or the alteration/updating of existing content.
– This phase can take place in the cloud or can be
external to the cloud
– Classify data according to
• Sensitivity
• Value to the organisation
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Data Security Life Cycle
Store
Share
Use Archive
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Create Destroy
Data Security Life Cycle
Store
Share
Use Archive
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Create Destroy
Data at Rest
Data Security Life Cycle
• Data at Rest
– Data is stored after creation or is archived after
leaving active use
– Data spends most of its time in this phase
– Data should be protected in accordance to its
classification
– Is data securely stored? How are the keys managed?
Security from malicious insiders? Tamper protection?
– Controls such as encryption, integrity control,
monitoring, and backup mechanisms should be
implemented.
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Is data securely stored? How are the keys managed? Is data secure from
malicious insiders? Tamper protection?
Data Security Life Cycle
Store
Share
Use Archive
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Create Destroy
Data in Motion
Data Security Life Cycle
• Data in use/processing
– Data is being viewed, processed or otherwise being
used in some sort of activity.
– Data is most vulnerable at this stage
• Some security controls may need to be turned off for data to
be used
• Data may have been transported to unsecure locations for
processing
– Computation guarantee? Information leakage?
– Data should be monitored for checking malicious
activity and audit purposes
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Computation guarantee? Information leakage? Unauthorized access?
Data Security Life Cycle
Store
Share
Use Archive
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Create Destroy
Data in Processing
Data Security Life Cycle
• Data in motion
– Data is being transported between clouds or between
cloud and the user
– Data is being shared
– Data integrity? Information rights management? SLA
compliance?
– Secure channels must be established before data is
put in motion (in accordance with the classification
level)
– Mechanisms for maintaining data integrity should be
implemented
– Information/digital rights must be managed
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Secure Transmission? Data integrity? Information rights management? SLA
compliance?
Data Security Life Cycle
• Destroy
– Data ceases to be available for use
– This can mean different things based on the usage
of data, data content and its application
– Data destruction can mean
• Logically erasing pointers
– Is the data truly deleted? Is it required to be truly deleted?
• Physically permanent data deletion
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