Meeting Summary
The meeting covered various topics, starting with CSA Announcements and Events,
focusing on recent publications, open peer reviews, and upcoming events like the
RSA conference. Ryan highlighted recent CSA publications and upcoming events.
The Perl Working Group Update included discussions on shortlisting members and
seeking support. The AI Transparency Framework discussion explored implementing
transparency using model cards and challenges in enterprise settings. The
Sevilla Crew Progress highlighted progress in utilizing AI in offensive
security. The introduction of AI Capability into Vulnerability Assessment Space
was discussed to enhance security testing processes. Third-Party Vendor
Assessment for Gen AI Models emphasized the importance of thorough assessments.
Shared LLM Model and Data Privacy Concerns discussed risks and data privacy in
shared models. The Shared Responsibility Model for LLM Use focused on
establishing responsibilities for training LLMs. Data Privacy and Model Training
Challenges addressed challenges in data privacy and GDPR compliance. Data
Privacy and Trust in AI Research highlighted the importance of data ownership
and trust in AI research. Blogging as a Tool for Idea Assembly suggested using
blogs for idea sharing and engagement. Lastly, the discussion on AI Content for
Blogs emphasized human-written blogs over synthetically generated content for
maintaining humanity in writing.
Topics & Highlights
2. Perl Working Group Update
Satish provided an update on the Perl working group, mentioning the ongoing process of shortlisting people and seeking assistance from other team
members.
* Project Status | Satish updated on the Perl working group progress,
focusing on shortlisting people and seeking support from team members for
the project.
3. AI Transparency Framework
Discussion on implementing AI transparency using model cards, data sheets, and nutrition labels for AI transparency. Exploring the challenges of enabling practitioners to deploy model cards in enterprise settings.
* Innovation and Feedback | The discussion includes the idea of combining
model cards with risk cards to create a comprehensive framework. Mention
of startups automating the creation of model card reports. Emphasis on
the importance of a holistic approach beyond just model cards.
4. Update on Sevilla Crew Progress
The discussion focused on the progress of the Sevilla crew in utilizing artificial intelligence in offensive security. They are working on a 10 to 15-page paper format and are currently in the phase of adding content and debating ideas. The team has settled on 80% of the definition of offensive security and is moving towards finalizing sections and ordering ideas.
* Project Status | The Sevilla crew is working on a 10 to 15-page paper
format for utilizing artificial intelligence in offensive security. They
have settled on 80% of the definition of offensive security and are in
the phase of adding content, debating ideas, and ordering sections.
5. Introduction of AI Capability into Vulnerability Assessment Space
The discussion focused on exploring the introduction of automated AI capability into vulnerability assessment tool sets to enhance output and automate the testing process for technology infrastructure security.
* Innovation and Feedback | The team discussed the exploration of
introducing automated AI capability into vulnerability assessment tool
sets to produce enhanced output and automate the testing process for
technology infrastructure security. They emphasized the importance of
transparency in communication and sharing directives received from higher
levels to ensure alignment within the work groups.
6. Third-Party Vendor Assessment for Gen AI Models
Discussion on the importance of creating a thorough third-party vendor assessment for Gen AI models, including the need for proper questions addressing data usage and privacy concerns.
* Innovation and Feedback | Emphasized the need for a
comprehensive third-party vendor assessment for Gen AI models,
highlighting the importance of asking the right questions regarding data
usage and privacy concerns. The discussion revolved around the challenges
faced in ensuring vendors handle data appropriately and the necessity of
thorough assessments to address these issues.
7. Shared LLM Model and Data Privacy Concerns
Discussion on the risks associated with shared LLM models provided by SaaS providers and the importance of maintaining data privacy when integrating
external AI capabilities.
* Project Status |highlighted the risks associated with shared
LLM models in the context of SaaS providers and emphasized the need to
maintain data privacy when integrating external AI capabilities. The
discussion focused on the challenges of ensuring data ownership and
privacy in shared models, highlighting the importance of forking data to
maintain control over updates.
8. Shared Responsibility Model for LLM Use
Discussion on the need to establish a shared responsibility model for training Large Language Models (LLMs) provided by SaaS providers, including
considerations for data de-identification and hybrid training models.
* Innovation and Feedback | Proposed the idea of developing a
shared responsibility model for LLM use, similar to the shared
responsibility model in cloud services. The discussion included
considerations for data de-identification, hybrid training models, and
the need to define roles for consumers and providers in training LLMs.
* Innovation and Feedback | Supported the idea of investigating
the shared responsibility model for LLM use, highlighting the challenges
posed by the distributed nature of foundational LLM models across
multiple data centers and the need for data protection strategies.
9. Data Privacy and Model Training Challenges
Discussion on the challenges related to data privacy, validation of vendor
claims, and the removal of data from trained models, particularly in the
context of GDPR compliance and the need for guidance on data management
techniques.
* Identified Challenges | Raised concerns about the need for
validation of vendor claims regarding data privacy and the challenges of
removing data from trained models, especially in GDPR-regulated
environments. Discussed historical difficulties in continuous
audit and compliance monitoring and the ongoing research on data removal
techniques.
10. Data Privacy and Trust in AI Research
The discussion revolved around the importance of data privacy, data chain
of custody, and trust in AI research. Highlighted the challenges
of maintaining visibility of data ownership as it moves across models,
geographies, and cloud environments. The need for further research in data
provenance beyond enterprise applications was emphasized.
* Identified Challenges | Discussed the challenges related to
maintaining data ownership visibility as data moves across models,
geographies, and cloud environments. The concept of data chain of custody
and trust in AI research was highlighted, emphasizing the need for
further exploration beyond traditional data provenance.
11. Blogging as a Tool for Idea Assembly
The conversation focused on using blogs as a platform for assembling and
organizing ideas before committing to formal research. Shared
insights on using LinkedIn as a medium for sharing ideas and receiving
feedback. The idea of posting a blog on CSA was discussed as a means to
gather thoughts and engage with the community.
* Innovation and Feedback | Proposed using blogs, particularly
on LinkedIn and CSA, as tools for idea assembly and engagement. The
discussion highlighted the benefits of sharing ideas through blogs to
gather feedback and refine thoughts before formal research.
12. Discussion on AI Content for Blogs
The discussion revolved around creating a separate space reserved for
blogging across all four workgroups, with a pipeline of 18 blog topics.
There was a preference expressed for human-written blogs over synthetically
generated content due to concerns about maintaining humanity in the
writing.
* Innovation and Feedback | Creating a separate space reserved for blogging across all four workgroups and
mentioned having a pipeline of 18 blog topics. There was a request to
follow up on the possibility of this space being a reality.
* Innovation and Feedback | preference for
human-written blogs over content generated synthetically, citing concerns
about the loss of humanity in the writing over time with
machine-generated content. This preference was agreed upon by other
speakers.