Generative AI: Key Considerations in Development and Deployment
The recently released GAO report titled "Artificial Intelligence: Generative AI Training, Development, and Deployment Considerations" highlights the rapid rise of generative AI technologies and explores how commercial developers navigate both opportunities and challenges. Generative AI models, which are capable of producing text, images, code, and audio based on user prompts, have grown in prominence, with some models surpassing 200 million weekly active users. This expansion has prompted increased scrutiny over issues of safety, reliability, and privacy. The GAO report discusses industry practices designed to support responsible AI development, while also addressing the risks and limitations inherent in these technologies.
Developers of generative AI models employ several strategies to promote trust and mitigate risks. Common practices include benchmark testing, red teaming, and the use of interdisciplinary teams. Benchmark tests evaluate model performance across various dimensions such as reasoning and factuality. Developers use results from these tests to compare their models to competitors and to refine outputs. Additionally, red teaming plays a critical role in identifying vulnerabilities by simulating potential attacks, thereby ensuring that developers can address flaws before public deployment. Furthermore, interdisciplinary teams comprising subject matter experts, cybersecurity specialists, and legal professionals assess the ethical implications of AI outputs to minimize harmful content.
Despite these efforts, developers acknowledge that no model is entirely reliable. Generative AI systems are prone to producing inaccurate information, often referred to as “hallucinations,” which can mislead users. Moreover, models can exhibit unintended biases, particularly when training data lack diversity. Developers stress the importance of monitoring deployed models, although these efforts may not always prevent harmful outputs or safeguard against misuse. The GAO report notes that some developers are transparent about limitations, advising users to approach AI outputs with caution. However, public documentation often emphasizes model capabilities over these constraints, leaving gaps in user awareness.
The report outlines several risks that arise from malicious actors exploiting generative AI systems. Prompt injection attacks, for example, manipulate inputs to bypass safety protocols, leading to unintended outputs. In extreme cases, attackers can employ “jailbreak” techniques, tricking models into ignoring built-in safeguards. Developers counter these risks through continuous monitoring and by banning users who violate usage policies. However, the evolving nature of these threats requires ongoing adjustments. Reinforcement learning and filtering mechanisms are among the techniques recommended to strengthen model defenses against malicious use.
The issue of data poisoning also emerges as a significant concern in the report. This technique involves manipulating training data to alter a model’s behavior, with attackers injecting misleading or harmful information into datasets. Generative AI models, which often rely on public data scraped from the internet, are particularly vulnerable to such attacks. The GAO highlights that even subtle modifications, such as purchasing expired domains to alter web content, can compromise training integrity. To mitigate this risk, developers are encouraged to sanitize data regularly, implement access controls, and educate users about potential threats.
Transparency in the use of training data presents another challenge for commercial developers. The GAO report indicates that many companies collect data from a variety of sources, including publicly available information, licensed datasets, and user-generated inputs. However, specifics about these datasets are often kept confidential, making it difficult to assess whether privacy or copyright laws are being violated. Although some developers provide high-level descriptions in model documentation, such as “model cards,” they generally avoid disclosing proprietary processes for data curation. This lack of transparency complicates efforts to ensure compliance with privacy and ethical standards.
The GAO emphasizes that safeguarding sensitive data requires a multi-faceted approach. Developers must undertake privacy evaluations at various stages of the development process to filter out personally identifiable information (PII) and prevent data leaks. Red teaming efforts also assess whether models inadvertently memorize or disclose sensitive content. Advanced security architectures, such as isolating data interactions and blocking abusive prompts, help maintain the integrity of AI systems. Despite these precautions, the report warns that attackers are continuously seeking new methods to exploit vulnerabilities, underscoring the need for vigilant monitoring and timely updates.
As generative AI continues to evolve, the report points out that developers and regulators must adapt to emerging challenges. The GAO highlights the importance of collaborative efforts, such as industry-wide standards and public-private partnerships, to address issues of trust, safety, and accountability. In future reports, the GAO plans to explore the broader societal and environmental impacts of generative AI, as well as the federal government’s role in adopting and regulating these technologies.
The information provided in this blog is intended for general informational purposes only and is not guaranteed to be accurate. While every effort has been made to ensure accuracy, this content does not constitute legal advice, nor does it replace professional guidance on AI-related regulations and compliance.