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Sama Bali
Sama Bali is product marketing manager, machine learning and AI services, AWS.
AWS BugBust Challenges Developers to Set World Record for Bug Busting at re:Invent 2021
November 3, 2021
|
AWS
,
BugBust
,
custom software development
,
re:Invent
Software bugs are irksome, time-consuming and can be one of the most expensive software development costs for any company on an ongoing basis. Software Testing News cited a study that found the ...
Sama Bali
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Security in AI
Step
1
of
7
14%
How would you best describe your organization's current stage of securing the use of generative AI in your applications?
(Required)
We don’t have or use generative AI in our applications
Nothing significant yet - We have or will have generative AI in our applications but haven’t yet researched or explored the possibility of securing it.
Starting - We have begun to work on securing generative AI in our applications.
Developing - We have started to implement some security measures for generative AI.
Average - We have some policies or tools in place.
Advanced - We have some enhanced security protocols specifically tailored for generative AI in our applications.
Very advanced - We have comprehensive, mature, and proactive security solutions for generative AI in our applications.
Have you implemented, or are you planning to implement, zero trust security for the AI your organization uses or develops?
(Required)
Already implemented
Planning to implement
Considering or scoping to implement
Not considering zero trust for AI
Do not use zero trust at all
Don’t know
What are the three biggest challenges your organization faces when integrating generative AI into applications or workflows? (Select up to three)
(Required)
Potential impacts on performance or scalability of the applications
Access security
Intellectual property protection
Security of prompts or responses
Lack of expertise or experience
Integration capabilities of current systems
Potential impacts on performance or scalability of the AI
Cost to implement
Data security or privacy
Compliance with standards and regulations
How does your organization secure proprietary information used in AI training, tuning, or retrieval-augmented generation (RAG)? (Select all that apply)
(Required)
Encryption at rest
Encryption in transit
Data access control, monitoring
Data anonymization
Data/information isolation
Data masking/tokenization
Data governance policies
Zero trust / continuous authentication and authorization
No specific measures in place
Which of the following kinds of tools are you currently using to secure your organization’s use of generative AI? (select all that apply)
(Required)
Native cloud service tools (AWS Guardrails, e.g.)
Third-party tools
Custom in-house tools
No specific tools
How valuable do you think it would it be to have a solution that classifies and quantifies risks associated with generative AI tools?
(Required)
Extremely valuable
Very valuable
Somewhat valuable
Not valuable
What are, or do you think would be, the most important reasons for implementing generative AI security measures? (Select up to three)
(Required)
Regulatory compliance
Protecting intellectual property
Mitigating potential data breaches
Maintaining customer trust
Competitive advantage
Improved risk management
Increased operational efficiency
Don’t know
Δ
×
