Generative AI and LLMs: How to reap the benefits while guarding against the risks

[ServicesExperts]

How can businesses gain the benefits that Generative AI will provide while avoiding the repercussions that may come with it, such as compromising data and impacting the environment?  Here are four key areas to consider.

By Iveta Lohovska, Chief Technologist and Principal Data Scientist, Worldwide AI, Data and Supercomputing Practice, HPE Services

Generative AI is unusual as a technology in the sense that it is "growing up" in full public view. Today’s new artificial intelligence era is shaped by the urgency to harness its power to improve your business. In particular, using large language model (LLM) capabilities—a category of Generative AI—promises to redefine the boundaries of innovation and productivity. But as businesses scramble to integrate these technologies, they risk damaging data privacy, long-term competitiveness, and environmental sustainability.

The stakes are high. Failing to manage these risks can result in severe consequences—from reputational damage to failure to meet corporate ESG (environmental, social, and governance) goals.

Risks and rewards: The duality of AI and large language models

LLMs are machine learning models that can comprehend and generate human language text. LLMs are revolutionizing how businesses operate, offering automated content generation, customer interaction, and decision-making support. Their impact is felt across sectors, heralding a future of unprecedented efficiency and innovation.

However, the race to adopt AI and LLMs can result in relinquishing control over sensitive data and increasing operational carbon footprints. Without adequate safeguards, the very innovation that gives businesses a competitive edge today could be their undoing tomorrow.

Striking the right balance

IT leaders should pay close attention to these four areas when building their AI and LLM initiatives:

Protecting data and privacy
Due to the scale needed to build the AI models, many companies are focused on leveraging someone else’s model while sharing their own data. However, in the case of generative AI, for example, the risks in the exposure of data (either in context or even as part of the prompts used to ask questions) can be significant. In the excitement to try these new technologies, businesses need to be aware of data privacy concerns and learn what is—and is not—appropriate to share with external Generative AI services (vs. bringing that work in-house for the more sensitive data). That’s why the first line of defense needs to be a robust data and security strategy when developing and adopting your company’s specific Generative AI technology plans.

Intellectual property concerns

The power of Generative AI and LLMs lies in their ability to generate content across various domains, be it textual data, images, or even more complex solutions. While this is their biggest strength, it could also potentially become a double-edged sword, particularly when it comes to intellectual property concerns.

The generative capabilities of these AI models are so advanced that they can occasionally produce outputs eerily similar to existing proprietary content owned by organizations. Given that these models are trained on vast datasets that may include publicly available information, there's a risk that they could recreate or closely emulate content that is supposed to be confidential or protected by copyright and patents.

Sustainability and energy efficiency

The swift adoption of Generative AI calls for deliberate planning around eco-friendly technological advancements. Although AI has the potential to be instrumental in mitigating and understanding climate issues, the priority is to ensure that it takes into account environmental impacts. This necessitates an eco-centric focus at every phase of AI deployment, from inference, down to the algorithm level and size.

Ensuring transparency

The power of AI extends beyond corporate corridors—it has societal implications. Transparency from businesses about their AI usage and environmental impact is essential. Public awareness and education will also play crucial roles in shaping an AI-driven future that is both empowering and responsible.

Embracing a new era

Generative AI and LLM solutions offer a future brimming with opportunities, but also significant challenges. Businesses that act responsibly in their adoption of these technologies stand to gain enormously in terms of business advantages, operational efficiencies, and public trust. The risks of getting it wrong are entirely avoidable through careful planning, ethical decision-making, and a commitment to social and environmental responsibility. As we charge ahead into this new frontier, the choices we make today will define the world we live in tomorrow.

Find out more:

HPE AI and data transformation services

Service Briefs:

HPE Artificial Intelligence Transformation Workshop

Generative AI and LLM Build and Deploy Service

Iveta Lohovska is a Chief Technologist and Principal Data Scientist with HPE Services’ Worldwide AI, Data and Supercomputing Practice. Iveta’s recent roles at HPE included Chief Technologist for AI, Data, and Supercomputing, as well as Principal Data Scientist for AI, and Architect for Global AI and Data Center of Excellence. She has a Masters's Degree in Business informatics, as well as a Masters's Degree in Artificial Intelligence from Columbia University. Contact Iveta on LinkedIn: Iveta Lohovska

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