Investments in AI, particularly generative AI, are accelerating rapidly as companies look to leverage all the benefits the technology has to offer. Unfortunately, with irresponsible AI deployments making headlines, concerns about data privacy, security risks, and the ethical implications of AI have also started to rise.
According to a report by DeloitteOnly about half of the companies surveyed (49%) currently have AI ethics policies in place, and only 37% are close to deploying them. Not only does the lack of clear guidelines on the ethical use of AI make organizations more vulnerable to AI bias or security risks, it also limits their ability to derive significant value from AI. In fact, Gartner found that by 2026, organizations’ AI models that operationalizing AI transparency, trust and security will achieve a 50% improvement in adoption, business goals and user acceptance.
Without proper protocols, organizations are also more likely to fall victim to shadow 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 use are far-reaching, from biased algorithms that perpetuate discrimination to opaque data practices that compromise user privacy. This is not to mention the challenges posed by shadow AI that hallucinates and produces factually incorrect answers.
To address these potential pitfalls, organizations must create effective policies and training protocols—on topics such as knowledge management, rapid engineering, and training AI systems on specific data—to ensure that AI goals align with the organization’s ethical and compliance guidelines, making AI safe to use and deploy at scale.
Mitigating data risks
While AI systems need data to improve, it’s critical that organizations fully understand how customer and business data is applied in AI models, while respecting data privacy and consent principles. Knowing the audit trail of who, when, and what data is used by next-gen AI capabilities is critical to ensuring safe AI deployments.
To maintain transparency, governance mechanisms must be implemented at every stage of model development to ensure data security and integrity. Enterprises should invest in solutions that provide users with administrative control over sensitive and harmful data that could be sent to extended language models (LLMs), as well as role-based policies to manage appropriate developer access to generative AI features.
Effective governance protocols and their implementation originate 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 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 these deployments meet regulatory requirements and industry standards.
AI Best Practices Training
Everyone in the company needs to be on the same page when it comes to AI rules, especially when it comes to limiting the use of shadow AI. Training programs and interactive workshops and simulations can be effective in ensuring company-wide learning and promoting a culture of responsible AI use.
Just as AI can only be effective if the data that goes into it is good, employee training is only as good as what it covers. These programs, which are the baseline level of employee training, should cover a wide range of topics, 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. In addition, 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 current as AI continues to evolve. Regular updates and refresher courses should be provided to keep employees informed of best practices and regulatory requirements for AI governance, and organizations should make these employee learning opportunities engaging. As an example, one UiPath customer implemented “Build a Bot” Sessionsthat are designed to show employees how automation can be an ally in their work lives, and have helped the client develop new automation use cases. The same type of out-of-the-box thinking should be used to enable employees to learn as AI implementations grow. By collaborating with HR and learning development teams when creating these programs, organizations can ensure they are well-designed, accessible, and integrated into employees’ professional development paths.
As the use of AI continues to grow in organizations, proactive steps must be taken to develop and implement effective AI governance frameworks that foster trust, ethics, and innovation in AI development and deployment. By prioritizing responsible AI practices, organizations can mitigate the risks associated with the use of shadow-generated AI, promote transparency in data use, and ensure reliable, accurate, and more valuable use of models.
