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AI investments, particularly in generative AI, are accelerating rapidly as organizations seek to take advantage of all the benefits the technology can provide. Unfortunately, with instances of irresponsible AI deployments making headlines, concerns regarding the data privacy, security risks, and ethical implications of AI have also started to increase.
According to a report by Deloitte, only about half of companies surveyed (49%) currently have AI ethics policies in place, and only 37% are about ready to roll them out. Not only does a lack of clear guidelines for ethical AI use make organizations more susceptible to AI bias or security risks, but it also limits their ability to derive significant value from AI. In fact, Gartner found that by 2026, AI models from organizations that operationalize AI transparency, trust, and security will achieve a 50% improvement in terms of adoption, business goals, and user acceptance.
Without proper protocols in place, organizations are also more likely to fall victim to shadow gen AI, which is characterized by the unauthorized or unregulated deployment of AI technologies within organizations and poses significant risks to data privacy, security, and ethical integrity. The consequences of unchecked AI usage are far-reaching, from biased algorithms perpetuating discrimination to opaque data practices compromising user privacy. This is all before accounting for challenges with gen AI hallucinating and producing factually incorrect responses.
To solve for these potential pitfalls, organizations need to create effective policies and training protocols — on things like knowledge management, prompt engineering, and the training of AI systems on specific data — to ensure AI objectives align with organizational ethical and compliance guidelines, making AI safe to use and deploy widely.
Mitigating data risks
While AI systems need data to improve, it is critical that organizations comprehensively understand how customer and company data is applied in AI models, respecting data privacy and consent principles. Knowing the audit trail of who, when, and which data was used by gen AI features is crucial to ensuring safe AI deployments.
To maintain transparency, governance mechanisms must be implemented at each stage of model development to ensure the security and integrity of data. Companies need to invest in solutions that provide users with administrative control over sensitive and harmful data that could be sent to Large Language Models (LLMs), as well as role-based policies to manage appropriate developer access to gen AI features.
Effective governance protocols and execution come from the top and should be led by the executive team with support from IT, security, and compliance teams. Each team plays an important role in ensuring the safety and security of AI deployments. IT teams provide technical expertise in implementing monitoring tools and enforcing security protocols, while cybersecurity teams assess and mitigate risks associated with AI deployments, and compliance teams ensure that these deployments adhere to regulatory requirements and industry standards.
Training on AI best practices
Everyone across the company must be on the same page when it comes to AI rules, particularly when it comes to limiting shadow usage. Training programs and interactive workshops and simulations can be effective in ensuring company-wide learning and promoting a culture of responsible AI usage.
Just as AI is only as good as the data fed into it, the quality of the education employees receive from training programs is only as good as what they cover. These programs, which act as the baseline level of education for employees, should cover myriad topics, ranging from data governance in AI deployments, to AI ethics policies and compliance guidelines, to techniques for ensuring data privacy and understanding how consent principles are respected in AI models. Further, they should help employees understand how to recognize and mitigate risks associated with irresponsible AI deployment, such as biased algorithms and opaque data practices.
Interactive workshops and simulations can be effective tools to reinforce learning and ensure employees stay up to date as AI continues to evolve. Regular updates and refresher courses should be provided to keep employees informed about best practices and regulatory requirements in AI governance, and organizations should make these opportunities for employee learning engaging. As one example, one UiPath customer implemented “Build a Bot” sessions, which are designed to show employees how automation can be an ally in their working lives, and helped the customer develop new automation use cases. The same sort of original thinking should be used to get employees learning as AI implementations expand. By collaborating with HR and learning development teams when creating these programs, organizations can ensure that they are well-designed, accessible, and integrated into employees’ professional development pathways.
As AI use continues to expand across organizations, proactive measures must be taken to develop and implement effective AI governance frameworks, fostering trust, ethics, and innovation in AI development and deployment. By prioritizing responsible AI practices, organizations can mitigate risks associated with shadow gen AI usage, promote transparency in data usage, and ensure reliable, accurate, and more valuable model usage.
