How to Bridge the AI Trust Gap: 5 Data-Centric Tactics for IT Leaders

How to Bridge the AI Trust Gap: 5 Data-Centric Tactics for IT Leaders

Here's how you can build trust into your AI efforts and set your organization up for success.

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79 percent of IT professionals say business leadership is increasingly pressuring them to implement artificial intelligence (AI), and it's no wonder. Generative AI is revolutionizing our work processes and sparking efficiency and innovation across sales, service, marketing, and commerce. However, there is an AI trust gap that IT professionals need to bridge.

As an IT leader, you want to honor every generative AI request and enable your business with the right technologies at all times. But is your data set up for success with AI? Can your team and your infrastructure support the demand for AI? For 88% of IT professionals, the answer is no.

What is the AI trust gap?

Simply put: IT leaders continue to grapple with a trust gap in AI. A gap that stems from the pressure to adopt AI quickly without adequate support structures to support data protection and compliance.

Our research shows that 48% of IT leaders worry their security infrastructure can't keep up with innovation demands. And that's because AI systems hold and use massive amounts of sensitive data that impacts both individuals and businesses. IT leaders must be able to trust the AI models, secure their data, and maintain compliance with regulations simultaneously - no small feat.

The road to trusted AI: 5 key data security and privacy tactics for IT leaders

By addressing the security and privacy roots of this AI trust gap, you can foster a safer, more transparent AI landscape. Here are six ways you can overcome the AI trust gap and enable your organizations with trusted AI capabilities.

1. Check that your data is accurate and up-to-date

For AI, data quality is key. Avoid "garbage in, garbage out," by cleaning your data sets and deleting any data you don't need, particularly personal information. If you want to keep old data but remove it from active AI use, consider archiving it. Tools like Privacy Center will help you store inactive data securely, improving AI performance and restricting access to sensitive information.

2. Harmonize your data

Now, if you're like most organizations, you likely have data in multiple, siloed systems - making it difficult to completely trust any single source, let alone all of them. To fuel your AI applications with the most complete and accurate data, you will need to harmonize your data sets from internal or external systems into a single source.

Data Cloud, our hyperscale data engine built into Salesforce, lets you do just that. Specifically, Data Cloud identity resolution uses matching and reconciliation rules to link data about people or accounts into unified profiles - giving you holistic views of your customers that you can trust. That's the kind of data accuracy you want to drive your AI applications.

3. Apply the right data privacy protections

Once your data is accurate and harmonized, apply an additional layer of data privacy protection, starting first and foremost, with customer consent. Get your customers' consent to use data, whether that's through preference management or updates to your privacy policy statements. Additionally, provide customers with a way to both opt out of AI usage and to exercise their data subject rights, like the Right to Be Forgotten and Data Subject Access requests.

Salesforce makes it easy to create these rules in any of its customer relationship management (CRM) applications. Upon successful configuration, Salesforce Privacy Center automatically tracks and records all activities related to consent, preferences, data retention, and individual rights - streamlining all aspects of privacy management.

4. Support compliance and privacy

Protecting customer data isn't just important - it's the foundation of trustworthy AI development. At Salesforce, your data is never our product, and we support the privacy of data in two key ways.

Our Einstein Trust Layer keeps personal or sensitive data hidden whenever it's used in an AI prompt. Additionally, Privacy Center can permanently get rid of data that's no longer needed, either by hiding specific details or deleting it completely. This way, you can protect data from getting exposed or from falling into the wrong hands throughout its lifecycle.

5. Create and maintain data access and governance policies for AI applications

Finally, prevent unauthorized people from accessing, downloading, or removing data from your AI systems. Create (and continually update) policies around how your data can be accessed by your AI applications - and by whom. This will help you mitigate threats by stopping or alerting on unauthorized or undesired activity. With Salesforce Event Monitoring, it's as simple as configuring transaction security blocks and alerts to stop malicious actions and notify the right people, at the right time.

Bridge the AI trust gap: empower the business

As AI continues to become a core part of our tech world, security and privacy protections are essential for building and maintaining trust. By cleaning up your data, harmonizing it, making policies clear, and strengthening security protocols, you can bridge the AI trust gap quickly and support the ever-increasing demands for trusted AI with confidence.

The above is intended for informational purposes. Please do not rely on this information in making your purchasing decisions. The development, release, and timing of any products, features or functionality not currently available remain at the sole discretion of Salesforce and are subject to change. The above is being provided for informational purposes only. Nothing herein is or should be construed as investment, legal or tax advice, a recommendation of any kind or an offer to sell or a solicitation of an offer to buy any security. This presentation does not purport to be complete on any topic addressed. Certain information in this presentation has been obtained from third party sources and, although believed to be reliable, has not been independently verified and its accuracy or completeness cannot be guaranteed.

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Marla Hay is the vice president of product management for security, privacy, and data protection. She has previously held product leadership roles at Janrain, acquired by Akamai, and product and engineering leadership positions at Mentor Graphics. She received a B.S. in computer science from Cornell... Read More University and an M.S. in computer science from Johns Hopkins University.