Trusted AI for IT Leaders in 5 Steps

The opportunity of AI is undeniable for organizations: it can fuel workflows, creativity, and innovation at unprecedented levels. Unsurprisingly, nearly 70% of senior IT leaders consider AI a top business priority. 

But with great potential comes great responsibility. 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 while staying transparent and secure.    

Step 1. Trusted AI starts with quality data

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.

To ensure data quality, it’s important to include a variety of data points that represent different perspectives, scenarios, and contexts relevant to your use case. 

This helps minimize the risks of bias or misleading results. Additionally, cleaning and normalizing 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 help manage the surplus of data from multiple sources, especially when it comes to handling duplicate records. 

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

Step 2. Define ethical boundaries and data privacy guidelines

Respecting customer privacy and protecting data throughout AI processes are essential for building and maintaining user trust. 

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. Here are some key actions to prioritize data privacy and ethics:

• Create guidelines that prioritize data privacy: At every stage – from collection to processing and storage – adopt a secure and compliant approach throughout the data lifecycle (pro tip: Privacy Center supports retention policies to manage the complete lifecycle of AI-related data used)     
• Implement a data minimization strategy: Collect and process only the data needed to meet  specific objectives and retain it only for as long as necessary     
• Encrypt sensitive data and limit access: This includes authorized personnel and systems to minimize the risk of unauthorized exposure or breaches
• Form ethical AI teams to oversee AI practices: Ensure compliance with ethical standards and protect the organization from legal, brand, or financial risks     

Transparency in how data is collected, processed, and used strengthens stakeholder trust and helps mitigate the risks of data misuse.

Step 3. Establish a practice of regular auditing

Even with strong ethical policies in place, AI can still produce unintended or harmful results, such as inaccuracies, biases, or misinformation. 

These risks become even more pronounced when AI agents are involved in critical decision-making. A robust auditing process is key to addressing these challenges and preventing future issues.     

The first line of defense in AI audits involves using automated tools to scan AI outputs for compliance with ethical standards and organizational policies. However, it’s just as important to gather feedback directly from end users who engage with the AI on a daily basis. 

Regular surveys and interviews offer a practical, ground-level view into how the AI is performing, what it produces, and how it integrates (or doesn’t) with existing teams and workflows. This approach helps organizations identify and address risks, making sure AI systems operate responsibly and effectively.      

Step 4. Monitor and protect your AI processes

Any valuable AI system will interact with sensitive data, raising security concerns — especially for organizations in highly regulated industries. This is why both The White House and Congress have called for independent AI audits to protect the public from AI misuse. In Europe, the EU Artificial Intelligence Act serves a similar purpose in regulating AI practices.           

To fully trust the AI you’re using, rigorous monitoring and protection measures are essential. Start by building clear boundaries around what data the AI can access and what must remain off-limits.      

Once these guardrails are set, the next step is defining strict access controls, making sure only authorized users can interact with the AI systems. Tools like Security Center make managing user permissions and org configurations for data used in and by AI processes easier.     

Ongoing security management is also critical to maintaining a trustworthy AI framework. Have a dedicated security review of your AI systems that manage testing (end-user testing, quality control testing, penetration testing, etc.). 

You might also want to consider Event Monitoring, which simplifies this process with advanced features like transaction security. This allows you to set up alerts or block unintended actions within your AI processes, maintaining a trusted environment.     

Step 5. Prioritize transparency and be open to feedback to build trusted AI

A lack of transparency around AI can lead to serious ethical concerns — so much so that only 42% of customers trusted businesses to use AI ethically in 2024, a drop of 16% from the previous year.

This trend highlights a key lesson: customers and users are paying attention and expect businesses to be open about how and where AI is used.     

IT leaders should make sure its use is never a mystery when deploying AI. One of the first steps to achieving clarity is labeling AI-generated content. Organizations should be explicit about when AI-generated content, insights, or media allows end users to evaluate and interpret this information appropriately.      

In addition, documenting processes and providing transparency about the datasets, models, and algorithms that produce AI outputs is important. Being open about your auditing and security processes further strengthens customer trust. 

And make sure to actively solicit feedback on the relevance, quality, and ethical implications of AI-generated content. This not only creates opportunities for improvement but also ensures that you align with your organization’s values. 

Over time, you can create a collaborative environment where AI evolves through continuous feedback and iteration.

Trusted AI is a process, not a given

Building trusted AI isn’t something that happens automatically – it’s a journey that requires ongoing effort. 

As AI continues to reshape modern enterprises, ensuring trusted AI means taking a proactive approach to data quality, privacy protection, regular audits, and transparency. Platforms like Agentforce are designed to support you through each step, from policy creation to agent implementation.      

By putting these measures in place, you help build a trusted framework that upholds the integrity and reliability of AI processes, all while supporting innovation in a secure environment.