{"id":8971,"date":"2025-03-17T17:45:57","date_gmt":"2025-03-17T09:45:57","guid":{"rendered":"https:\/\/www.salesforce.com\/?p=8971"},"modified":"2025-03-17T17:45:58","modified_gmt":"2025-03-17T09:45:58","slug":"5-step-guide-to-trusted-ai","status":"publish","type":"post","link":"https:\/\/www.salesforce.com\/ap\/blog\/5-step-guide-to-trusted-ai\/","title":{"rendered":"The IT Leader\u2019s 5-Step Guide to Trusted AI"},"content":{"rendered":"\n

The opportunity of AI<\/a> is undeniable for organisations: it can fuel workflows, creativity, and innovation at unprecedented levels. Unsurprisingly, nearly 84% of CIOs report that AI will be as significant to their business as the internet.<\/a><\/p>\n\n\n\n

But with great potential comes great responsibility<\/a>. AI presents challenges tied to trust, security, and ethics, all of which extend well beyond the present moment. Here are five key steps IT leaders can take to make the most of trusted AI<\/a> while staying transparent and secure.\u00a0\u00a0\u00a0\u00a0<\/p>\n\n\n\n

Step 1. Trusted AI starts with quality data<\/h2>\n\n\n\n

The effectiveness of any AI model starts and ends with the quality of data it uses. Generative AI relies on training datasets to interpret data accurately, and poor-quality data can result in biased, irrelevant, or even harmful outputs.<\/p>\n\n\n\n

To ensure data quality<\/a>, it\u2019s important to include a variety of data points that represent different perspectives, scenarios, and contexts relevant to your use case.\u00a0<\/p>\n\n\n\n

This helps minimise the risks of bias or misleading results. Additionally, cleaning and normalising data is equally important to reduce noise and ensure that both low- and high-quality data contribute effectively to the model. Tools like Privacy Center<\/a> help manage the surplus of data from multiple sources, especially when it comes to handling duplicate records.\u00a0<\/p>\n\n\n\n

Remember that your datasets need to adapt over time to stay relevant to evolving trends. Prioritising data quality lays the groundwork<\/a> for building generative AI systems that provide reliable and trusted outputs, setting a strong standard for ethical AI practices<\/a>.<\/p>\n\n\n\n

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Step 2. Define ethical boundaries and data privacy guidelines<\/h2>\n\n\n\n

Respecting customer privacy and protecting data throughout AI processes are essential for building and maintaining user trust. <\/p>\n\n\n\n

As AI systems, including generative AI and agents, increasingly interact with sensitive data, including personally identifiable information (PII), IT leaders must establish strong data protection policies<\/a>. Here are some key actions to prioritise data privacy and ethics:<\/p>\n\n\n\n