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Home » Cybersecurity » Events » USENIX Enigma 2021 – Hyrum Anderson’s ‘The Practical Divide Between Adversarial ML Research And Security Practice: A Red Team Perspective’

USENIX Enigma 2021 – Hyrum Anderson’s ‘The Practical Divide Between Adversarial ML Research And Security Practice: A Red Team Perspective’

by Marc Handelman on March 25, 2021

Many thanks to USENIX Enigma 2021 for publishing these outstanding conference videos on the YouTube USENIX Channel; don’t miss this erudite 27 video information & cybersecurity event.

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*** This is a Security Bloggers Network syndicated blog from Infosecurity.US authored by Marc Handelman. Read the original post at: https://www.youtube-nocookie.com/embed/RWx_DitGF7A

March 25, 2021March 25, 2021 Marc Handelman Conferences, Cyber Security, education, Information Security, USENIX, USENIX Enigma 2021

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Security in AI

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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)

Security of prompts or responses

Integration capabilities of current systems

Lack of expertise or experience

Access security

Compliance with standards and regulations

Data security or privacy

Potential impacts on performance or scalability of the AI

Intellectual property protection

Cost to implement

Potential impacts on performance or scalability of the applications

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

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