Government regulations and frameworks regarding AI are beginning to emerge, so be aware of the specific regulations in relevant jurisdictions. As the use of AI continues to raise questions about ethics and responsibility, new regulations may be introduced in response to changing public sentiment towards the use of AI. However, in general, you should prepare for the following main types of risks:
Regulation. AI poses legal risks because it can expose organizations to litigation over copyrighted content, information, and data. Regulations are changing rapidly, so be aware of local and jurisdictional AI regulations to ensure compliance with governing policies. Also be aware of industry-specific regulations such as life sciences and financial services.
Good reputation. AI can amplify bias and create “black boxes,” or AI systems whose inputs and interactions cannot be seen by users. Vendors that do not provide transparency into their training datasets risk producing harmful output. Untested AI services can also pose risks by making poor decisions or performing tasks. Organizations must build robust guardrails to prevent loss of intellectual property and customer data when building or purchasing generative AI services.
competency. AI requires unique skill sets, which must be sourced intentionally through upskilling existing talent or from academia and startups. Skills in areas such as agile engineering and responsible AI will be in high demand in the near term.
As AI threats and breaches (malicious and benign) are ongoing and constantly evolving, AI governance, trustworthiness, fairness, trustworthiness, robustness, effectiveness, and privacy principles and Set policies. Organizations that don’t do so are much more likely to experience negative outcomes and breaches from AI. The model does not work as intended, resulting in security and privacy failures, economic and reputational losses, and personal harm.
The Gartner AI TRISM (Trust, Risk, and Security Management) Framework includes solutions, technologies, and solutions for model interpretability and explainability, privacy, model operations, and resistance to adversarial attacks for customers and enterprises. Contains processes. To get the best results from any AI initiative, we build dedicated cross-functional teams or task forces that include legal, compliance, security, IT, data analytics teams and sales personnel. I am advocating.