Trust and transparency in AI are undoubtedly critical to doing business. As AI-related threats escalate, security leaders are increasingly faced with the urgent task of protecting their organizations from external attacks while establishing responsible practices for internal AI use.
Vanta 2024 State of Trust Report Recent alarming increases in AI-driven malware attacks and identity fraud illustrate this growing urgency. Despite the risks posed by AI, only 40% of organizations conduct regular AI risk assessments and only 36% have a formal AI policy.
AI safety hygiene aside, establishing transparency around an organization’s use of AI has become a top priority for business leaders. This makes sense. Generally speaking, companies that prioritize accountability and openness are better positioned for long-term success.
Transparency = good business
AI systems operate using massive data sets, complex models, and algorithms that often lack visibility into their inner workings. This opacity can lead to results that are difficult to explain, defend or challenge, raising concerns about bias, fairness and accountability. For businesses and public agencies that rely on AI for decision-making, a lack of transparency can undermine stakeholder confidence, introduce operational risks and heighten regulatory scrutiny.
Transparency is non-negotiable because it:
- build trust: When people understand how AI makes decisions, they are more likely to trust and accept it.
- Improve accountability: Clear documentation of data, algorithms, and decision-making processes helps organizations detect and correct errors or biases.
- Ensure compliance: In highly regulated industries, transparency is necessary to explain AI decisions and maintain compliance.
- Help users understand: Transparency makes AI easier to use. When users can see how it works, they can confidently interpret its results and take action.
All this shows that transparency is a fact good for business. Case in point: Recent Gartner research suggests organizations will embrace AI transparency by 2026 Anticipated 50% increase in adoption and improved business results. MIT Sloan Management Review findings also show Companies focused on AI transparency outperform their peers by 32% in customer satisfaction.
Create a transparency blueprint
At its core, AI transparency is about creating clarity and trust through demonstration how and Why Artificial intelligence makes decisions. It’s about breaking down complex processes so that anyone, from data scientists to frontline workers, can understand what’s going on behind the scenes. Transparency ensures that AI is not a black box but a tool that people can rely on with confidence. Let’s explore the key pillars that make AI more explainable, approachable, and accountable.
- Prioritize risk assessment: Before launching any AI project, take a step back and identify the potential risks to your organization and customers. Address these risks proactively from the outset to avoid unintended consequences later. For example, banks building AI-driven credit scoring systems should put safeguards in place to detect and prevent bias and ensure fair and equitable outcomes for all applicants.
- Security and privacy built from the ground up: Security and privacy need to be a priority from day one. Use techniques like federated learning or differential privacy to protect sensitive data. As AI systems evolve, ensure these protections evolve too. For example, if healthcare providers use AI to analyze patient data, they will need strong privacy measures to keep personal records secure while still providing valuable insights.
- Control data access with secure integration: Be smart about who and what has access to your data. Instead of feeding customer data directly into AI models, use secure integrations like APIs and formal data processing agreements (DPAs) to take control of the situation. These protections ensure your data remains secure and under your control, while still providing your AI with the functionality it needs to perform.
- Make AI decision-making transparent and accountable
When it comes to trust, transparency is everything. Teams should know how AI makes decisions, and they should be able to clearly communicate this to customers and partners. Tools like explainable artificial intelligence (XAI) and explainable models can help transform complex output into clear, easy-to-understand insights. - Put your customers in control: Customers should know when AI is being used and how it affects them. Adopting an informed consent model—where customers can opt in or out of AI features—puts them in the driver’s seat. Easily accessing these settings can help people feel in control of their data, build trust, and align your AI strategy with their expectations.
- Continuously monitor and audit AI: Artificial intelligence is not a one-and-done project. It requires regular inspection. Conduct frequent risk assessments, audits and monitoring to ensure your systems remain compliant and effective. Align with industry standards such as NIST AI RMF, ISO 42001, or frameworks such as the EU Artificial Intelligence Act to enhance reliability and accountability.
- Leading the way with in-house AI testing: If you’re going to ask your customers to trust your AI, trust yourself first. Use and test your own AI systems internally to identify issues early and make improvements before rolling them out to users. Not only does this demonstrate your commitment to quality, but it also creates a culture of responsible AI development and continuous improvement.
Trust is not built overnight, but transparency is the foundation. By adopting clear, explainable, and accountable AI practices, organizations can create systems that work for everyone—building confidence, reducing risk, and driving better outcomes. When AI is understood, it can be trusted. When it is trusted, it becomes an engine.
