Distinguishing GenAI Model Building From GenAI Model Usage
The distinction between GenerativeAI (GenAI) model building and GenAI model usage is pivotal in understanding the broader field of artificial intelligence. Both the production and consumption of GenAI models work in tandem to push the boundaries of what’s possible with AI, leading to transformative changes across numerous sectors.
While the operational efficiency of GenAI has clear potential in the enterprise, the challenges of GenAI—such as potential biases, hallucinations, and security vulnerabilities—necessitate strong governance policies. As we stand at the crossroads of these two worlds of GenAI, navigating the complexities with a clear vision for responsible innovation and usage is imperative.
Breaking down ‘Artificial Intelligence’ – what exactly do we mean?
The term “AI” has become a buzzword in recent years, often leading to misconceptions about its true nature and capabilities. Within this, we’ve also experienced the evolution of ‘GenAI’, and distinguishing this from its predecessor is vital.
What is traditional AI?
Traditional AI focuses on performing specific tasks using predefined algorithms and rules. It excels in pattern recognition and decision-making based on structured data, such as voice assistants like Siri or Alexa, recommendation engines on platforms like Netflix, and Google’s search algorithm. On the other hand, GenAI represents a leap toward creativity and innovation.
How does GenAI differ from traditional AI?
Unlike Traditional AI, which replicates human efficiency in specific tasks, GenAI aims to create entirely new content, whether it be text, images, or music, by leveraging machine learning techniques to autonomously generate outputs based on patterns in data.
The key difference between the two lies in their objectives: Traditional AI is about pattern recognition, while GenAI is about pattern creation.
Can AI and GenAI be used interchangeably?
It’s important to note that the terms “AI” and “GenAI” are not synonymous. GenAI is a specific category within the broader field of AI, and within GenAI itself, there are additional subdivisions, such as distinctions between those who build GenAI models and those who use them.
GenAI models: technology development
GenAI models are advanced, data-driven algorithms that are revolutionizing the field of artificial intelligence by their ability to generate content.
These models have the capability to analyze and understand complex patterns from large-scale data sources, including the web, encyclopedias, books, and image collections. With several well-known providers of GenAI models, including industry giants like OpenAI, Google, and Microsoft, GenAI models such as ChatGPT, Midjourney, and Copilot are becoming increasingly recognizable.
What are the advantages of GenAI models?
The advantages of GenAI models are significant, offering capabilities in creating diverse forms of content including text, audio, and images, which can be particularly beneficial for creative industries. They also facilitate data augmentation, expanding the scope and quality of datasets for machine learning. Moreover, they streamline translation and summarization processes, enhancing communication and comprehension across languages.
What are some of the challenges of GenAI model development?
However, these models come with their own set of challenges. Hallucinations (the generation of inaccurate content) and the potential for AI-generated bias can both be perpetuated through the training data. Models can also be tampered with – in other words, if the quality of data that enters the system has been compromised, the outcome will be explicitly impacted, resulting in errors and inaccuracies.
The complexity of training GenAI models requires considerable computational power and expertise, and there are inherent security vulnerabilities that need to be addressed to prevent misuse. In the evolving GenAI market, enterprises can access various tools that are specific to addressing the challenges that come with model creation, serving to protect the building process to ensure the integrity and reliability of resultant outputs.
Governance for creating GenAI models
Using GenAI without proper monitoring is like operating a home security system without cameras. AI governance is what ensures AI models are being used responsibly and ethically. It sets the policies, regulations, and criteria that direct the creation and use of AI while ensuring the technology is safe, equitable, and respects human rights.
Governance for GenAI model creation encompasses several critical aspects:
- Ensuring the validity of model outputs
- Addressing potential biases and maintaining algorithm transparency
- Safeguarding against vulnerabilities
- Mitigating the model’s attack surface and potential attack paths
- Careful consideration of the training data used to develop these models
GenAI model usage: technology consumption
With the rise of GenAI adoption, the integration of GenAI governance into existing network infrastructures is more important than ever. According to a recent McKinsey report, 65% of respondents reported that their organizations are regularly using GenAI. Further, it’s predicted that by 2026, over 80% of organizations will have adopted GenAI in some capacity.
What challenges does increased GenAI consumption pose for enterprises?
Many companies lack the proper setup to monitor GenAI usage among their employees effectively, and this is detrimental for the quality of the resultant output. Traditional security measures are falling short, leading to employees experimenting with GenAI without adequate training, or even consciously engaging in malpractices such as shadow AI.
Consequently, Security Operations Centers (SOCs) find themselves ill-equipped to handle incidents related to GenAI, leaving unanswered questions about current activities and past events.
Governance for consuming GenAI
Governance for consuming GenAI is centered around maintaining transparency and exerting control over AI usage, in order to maintain the integrity, accuracy, and quality of GenAI outputs.
This includes ensuring GenAI visibility by tracking all usage, whether officially sanctioned or not, to prevent the emergence of Shadow AI. It’s also about having sufficient GenAI observability measures in place, which allow enterprises to gain a comprehensive understanding of AI usage, covering the tools, prompts, intentions, and uploads.
Another critical consideration around consumption is GenAI risk management and GenAI data security, and enterprises must have a means of monitoring, measuring, and mitigating risks tied to GenAI usage. Enterprises must implement clear policy education measures, involving the dissemination of policy information to users and overseeing its distribution.
This includes implementing protocols for GenAI forensics, such as ensuring secure storage of prompts and attachments for periodic GenAI audits. Finally, network orchestration is implemented to curb GenAI-related risks.
Why is governance important for GenAI model building and usage?
Governance is not just a safeguard but a necessity. As GenAI use continually increases, the urgency for proper monitoring and governance mechanisms cannot be underestimated.
Once enterprises have demonstrated an understanding of the key differences between GenAI model building and usage, they will be able to take proactive steps in establishing frameworks to steer the development and application of AI toward a future that is secure, fair, and aligned with our collective values and rights.
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