Singapore: Cyber Security Agency provides insight into managing cybersecurity risks in generative AI and large language models

In brief

In a recent article titled The Cybersecurity of Gen-AI and LLMs: Current Issues and Concerns, the Cyber Security Agency of Singapore (CSA) provides helpful thought leadership on the security and privacy challenges associated with generative artificial intelligence (Gen-AI) and large language models (LLMs). The article outlines issues such as accidental data leaks, vulnerabilities in AI-generated code and potential misuse of AI by malicious actors, before providing recommendations on the steps that technology companies can take to address these concerns.


Contents

In more detail

The rapid growth of Gen-AI and LLMs has led to significant security and privacy concerns, and the key issues highlighted by the CSA include the following:

  • Accidental data leaks: Gen-AI systems, particularly LLMs, are susceptible to accidental data leaks, which may occur through overfitting or inadequate data sanitization. Sensitive information may be exposed when employees use ChatGPT for coding. The growing integration of AI in personal devices also increases the risk of data being inadvertently transferred to the cloud.
  • Risks in AI-generated code: The use of AI in coding increases cybersecurity risk because, without supervision, such code may contain undetected security flaws. Human oversight remains essential to mitigate such risks.
  • Misuse of AI: Malicious actors may leverage LLMs to exploit vulnerabilities identified in common vulnerabilities and exposures (CVE) reports. Such risks are generally reduced when training data does not include CVE descriptions.
  • Mitigating privacy concerns: Tech companies are helping to address privacy issues by controlling data usage, for example, by providing options for users to delete stored information and to prevent data from being used to train models. Users are nevertheless advised to refrain from sharing sensitive data with AI platforms.

The CSA's list of best practices to address the privacy and security concerns associated with Gen-AI and LLMs includes the following:

  • Enhancing employee awareness and training on associated risks
  • Reviewing and updating IT and data loss prevention policies
  • Ensuring human supervision over Gen-AI systems and LLMs
  • Staying updated on developments in Gen-AI and associated risks

Key takeaways

The article demonstrates the CSA's cautiously optimistic outlook in the Gen-AI and LLM space, noting the sensitive balance required in developing responsible Gen-AI and LLMs. Understanding these realities and implementing the necessary guardrails will be critical for organizations keen on integrating Gen-AI and LLMs into their business processes.

* * * * *

LOGO_Wong&Leow_Singapore

© 2024 Baker & McKenzie.Wong & Leow. All rights reserved. Baker & McKenzie.Wong & Leow is incorporated with limited liability and is a member firm of Baker & McKenzie International, a global law firm with member law firms around the world. In accordance with the common terminology used in professional service organizations, reference to a "principal" means a person who is a partner, or equivalent, in such a law firm. Similarly, reference to an "office" means an office of any such law firm. This may qualify as "Attorney Advertising" requiring notice in some jurisdictions. Prior results do not guarantee a similar outcome.


Copyright © 2024 Baker & McKenzie. All rights reserved. Ownership: This documentation and content (Content) is a proprietary resource owned exclusively by Baker McKenzie (meaning Baker & McKenzie International and its member firms). The Content is protected under international copyright conventions. Use of this Content does not of itself create a contractual relationship, nor any attorney/client relationship, between Baker McKenzie and any person. Non-reliance and exclusion: All Content is for informational purposes only and may not reflect the most current legal and regulatory developments. All summaries of the laws, regulations and practice are subject to change. The Content is not offered as legal or professional advice for any specific matter. It is not intended to be a substitute for reference to (and compliance with) the detailed provisions of applicable laws, rules, regulations or forms. Legal advice should always be sought before taking any action or refraining from taking any action based on any Content. Baker McKenzie and the editors and the contributing authors do not guarantee the accuracy of the Content and expressly disclaim any and all liability to any person in respect of the consequences of anything done or permitted to be done or omitted to be done wholly or partly in reliance upon the whole or any part of the Content. The Content may contain links to external websites and external websites may link to the Content. Baker McKenzie is not responsible for the content or operation of any such external sites and disclaims all liability, howsoever occurring, in respect of the content or operation of any such external websites. Attorney Advertising: This Content may qualify as “Attorney Advertising” requiring notice in some jurisdictions. To the extent that this Content may qualify as Attorney Advertising, PRIOR RESULTS DO NOT GUARANTEE A SIMILAR OUTCOME. Reproduction: Reproduction of reasonable portions of the Content is permitted provided that (i) such reproductions are made available free of charge and for non-commercial purposes, (ii) such reproductions are properly attributed to Baker McKenzie, (iii) the portion of the Content being reproduced is not altered or made available in a manner that modifies the Content or presents the Content being reproduced in a false light and (iv) notice is made to the disclaimers included on the Content. The permission to re-copy does not allow for incorporation of any substantial portion of the Content in any work or publication, whether in hard copy, electronic or any other form or for commercial purposes.