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* updated with 0.9 version changes * updated to move the owasp resource images to the front of the owasp section * updated with 1.0 release info * 1.0 release * fixed date typo on changelog --------- Co-authored-by: Jason Ross <[email protected]>
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llm-top-10-governance-doc/LLM_AI_Security_and_Governance_Checklist-v05.pdf
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@@ -31,15 +31,20 @@ \chapter{Team} | |
\endfoot | ||
%%% TABLE DATA GOES HERE | ||
\hline | ||
Sandy Dunn & Heather Linn & John Sotiropoulos \\ | ||
Sandy Dunn & Heather Linn & \href{mailto:[email protected]}{John Sotiropoulos} \\ | ||
\hline | ||
Steve Wilson & Fabrizio Cilli & Aubrey King \\ | ||
\hline | ||
Bob Simonoff & David Rowe & Rob Vanderveer \\ | ||
Bob Simonoff & David Rowe & \href{mailto:[email protected]}{Rob Vanderveer} \\ | ||
\hline | ||
Emmanual Guilherme Junior & Andrea Succi & Jason Ross \\ | ||
\hline | ||
Talesh Seeparsan & Anthony Glynn & Julie Tao \\ | ||
\hline | ||
%%% TABLE DATA ENDS HERE | ||
\caption{OWASP LLM AI Security \& Governance Checklist v.0.5 Team} | ||
\caption{OWASP LLM AI Security \& Governance Checklist Team} | ||
\label{tab:team} | ||
\end{longtable} | ||
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This project is licensed under the terms of the | ||
\href{https://creativecommons.org/licenses/by-sa/4.0/}{Creative Commons Attribution-ShareAlike 4.0 International License} |
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% !TEX root = owasp-doc.tex | ||
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% ================================================ | ||
% LLM Strategy | ||
% ================================================ | ||
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\headerimage | ||
\chapter{Determining LLM Strategy} | ||
The acceleration of LLM applications has raised the visibility of all | ||
artificial intelligence applications' organizational use. Recommendations for | ||
policy, governance, and accountability should be considered holistically. | ||
The rapid expansion of Large Language Model (LLM) applications has heightened | ||
the attention and examination of all AI/ML systems used in business operations, | ||
encompassing both Generative AI and long-established Predictive AI/ML systems. | ||
This increased focus exposes potential risks, such as attackers targeting | ||
systems that were previously overlooked and governance or legal challenges that | ||
may have been disregarded in terms of legal, privacy, liability, or warranty | ||
issues. For any organization leveraging AI/ML systems in its operations, it's | ||
critical to assess and establish comprehensive policies, governance, security | ||
protocols, privacy measures, and accountability standards to ensure these | ||
technologies align with business processes securely and ethically. | ||
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Attackers, or adversaries, provide the most immediate and harmful threat to | ||
enterprises, people, and government agencies. Their goals, which range from | ||
financial gain to espionage, push them to steal critical information, disrupt | ||
operations, and damage confidence. Furthermore, their ability to harness new | ||
technologies such as AI and machine learning increases the speed and | ||
sophistication of attacks, making it difficult for defenses to stay ahead of | ||
attacks. | ||
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The immediate LLM threats are the use of online tools, browser plugins, | ||
third-party applications, the extended attack surface, and ways attackers can | ||
leverage LLM tools to facilitate attacks. | ||
The most pressing non-adversary LLM threat for many organizations stem from | ||
"Shadow AI": employees using unapproved online AI tools, unsafe browser | ||
plugins, and third-party applications that introduce LLM features via updates | ||
or upgrades, circumventing standard software approval processes. | ||
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\begin{figure}[h] | ||
\centering | ||
\includegraphics[width=\textwidth]{ai_implementation_strategy} | ||
\caption{Image of steps of LLM implementation} | ||
\label{fig:llm-implementation-strategy} | ||
\includegraphics[width=\textwidth]{ai_deployment_strategy} | ||
\caption{Image of options for deployment strategy} | ||
\label{fig:llm-deployment-strategy} | ||
\end{figure} | ||
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\clearpage | ||
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\section{Deployment Strategy} | ||
The scopes range from leveraging public consumer applications to training | ||
proprietary models on private data. Factors like use case sensitivity, | ||
capabilities needed, and resources available help determine the right balance | ||
of convenience vs. control. But understanding these five model types provides a | ||
framework for evaluating options. | ||
of convenience vs. control. However, understanding these five model types | ||
provides a framework for evaluating options. | ||
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\begin{figure}[h] | ||
\centering | ||
\includegraphics[width=\textwidth]{ai_deployment_strategy} | ||
\caption{Image of options for deployment strategy} | ||
\label{fig:llm-deployment-strategy} | ||
\end{figure} | ||
\includegraphics[width=\textwidth]{ai_deployment_types} | ||
\caption{Image of options for deployment types} | ||
\label{fig:llm-deployment-types} | ||
\end{figure} |
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