Companies are no longer just collecting data; they are also deciding how it’s used. This shift in power dynamics reflects a change in how people evaluate businesses and, ultimately, a significant impact on a company’s bottom line.
- 75% of consumers say they won’t buy from companies they don’t trust with their data.
- More than half have already changed providers due to privacy concerns.
- 78% expect AI to be used responsibly.
Recent findings from Prosper Insights & Analytics reinforce this sentiment. When asked about concerns related to AI, 39% of adults said the technology needs more human oversight. Another 32% pointed to a lack of transparency, and more than a quarter were concerned about AI making incorrect decisions.
- Cybersecurity risks are emerging as AI systems become more advanced.
- Vulnerabilities like model inversion, adversarial prompts, and data poisoning create entry points for attackers.
- Appknox security reviews found issues ranging from weak network configurations to lax authentication and insufficient privacy protections.
Internally, IT teams are feeling pressure as they weigh the risks of adoption against the demands of innovation. A ShareGate survey of 650 professionals across North America and Europe showed that 57% of those exploring or deploying Microsoft Copilot identified security and access management as top concerns.
| Survey Findings | Percentage of Respondents |
|---|---|
| Security and access management | 57% |
| Data retention and quality | 57% |
Customers are paying attention to how companies approach this. Cisco’s research shows that awareness of privacy laws has grown significantly in recent years. More than half of consumers say they are now familiar with their data rights.
“AI is evolving fast, but trust moves slower. Businesses need to meet regulatory expectations today while building systems flexible enough to meet tomorrow’s.” – Bill Hastings, CISO, Language I/O
Prosper Insights & Analytics data further reinforces this, with 59% of respondents reporting that they are either extremely or very concerned about their privacy being violated by AI systems. These findings reflect a deep emotional undercurrent that companies must take seriously if they want customers to stay engaged and confident in their use of AI-enabled services.
Industry-Specific Concerns
In healthcare, the importance of trust becomes even more pronounced. A recent Iris Telehealth survey found that 70% of respondents had concerns about how their mental health data would be protected when using AI-powered tools.
- Clear explanations
- Strong encryption
- Collaboration with licensed professionals
- Systems that make it easy to shift from AI assistance to human care
The case of Amazon’s AI recruiting tool, which was found to disadvantage female applicants due to biased training data, remains a cautionary example. The company ultimately pulled the system, but the incident left a lasting impression of what happens when organizations overlook the importance of oversight and transparency.
Building Trust
Responsible AI should reflect how companies see their role in the broader ecosystem of data, ethics, and service. Customers are forming opinions based on whether companies appear to handle information responsibly, communicate honestly and design technology in ways that respect the people who use it.
- Minimizing data storage
- Embedding privacy-by-design principles into development cycles
- Producing clear AI usage policies
- Providing transparency reports
- Internal education
The EU’s AI Act introduces new requirements around transparency and risk management, especially for high-impact systems. In the US, emerging privacy laws are raising expectations across sectors. These legal changes reflect a growing belief that companies need to be more deliberate about how AI systems are developed and deployed.
The Future of Trust
“Securing AI starts with visibility,” added Hastings. “You can’t protect what you don’t fully understand, so begin by mapping where AI is being used, what data it touches and how decisions are made. From there, build in access controls, auditing and explainability features from day one. Trust grows when systems are designed to be clear, not just clever.”
Doing this well often requires cross-functional coordination. Security, legal, product, and compliance teams must work together from the start, not just at review points. Vendor evaluation processes need to include questions about AI ethics and security posture. Technical audits should examine how models behave under real-world conditions, including how they handle edge cases or unexpected inputs.
Businesses that take the time to explain what their AI systems do, how decisions are made and how information is protected are showing customers they deserve their trust. These are the companies that build deeper loyalty and differentiate themselves in markets where products and services can otherwise feel interchangeable.
Trust builds slowly through a pattern of responsible choices, clear communication, and consistent follow-through. AI is a powerful tool, but it works best in the hands of teams that treat security and ethics as shared values, not as checklists.
