Recent data shows that 70% of consumers want to understand how companies use their personal information. People are moving beyond passively experiencing digital spaces and are paying more attention. They’re curious, more cautious, and asking simple but important questions: What data are you collecting? How are you using it? And why does it matter?
The challenge is that many organizations are still approaching this from a compliance mindset. Privacy policies are often written with regulators in mind, not real people. Disclosures may check the right legal boxes, but they don’t always build trust. And when it comes to explaining how algorithms influence decisions, the details are often thin or hard to follow. Compliance is important as it reduces risk, but it doesn’t automatically create confidence. What’s changing now is the expectation. Trust will come from being clear, open, and proactive about how personal data is collected, used, and shared in AI-driven experiences. As AI plays a bigger role in shaping experiences, the companies that stand out will be the ones that are upfront about who they are, how they use data, and what their technology is actually doing.
Identity is at the center of AI trust
At the center of every AI-driven experience is identity. More specifically, identity systems govern how personal data is accessed, used, and shared across these interactions. It’s what makes these systems work, helping authenticate users, tailor interactions, assess risk, flag potential fraud, determine access, and deliver relevant recommendations. This information can range from basic details like a name or age to more complex inputs like behavior patterns or transaction history. Together, it allows AI to turn expectations into experiences that feel timely and personal.
But with that comes a shared responsibility for security and identity leaders. On one hand, this data must be carefully protected from breaches and misuse. On the other, the decisions AI makes using that data need to be understandable and justifiable. When people can’t see how their information is shaping outcomes, whether it’s an extra authentication step, a fraud alert, a pricing change, or even the content they’re shown, trust starts to slip. From a consumer perspective, the expectation is simple: clarity and control over how their data is being used in real time. In an age where AI acts continuously, trust can no longer rely on a one-time login. It must be evaluated in the moment. Transparency here extends beyond disclosure, focusing instead on clarity and usability for the end user.
That means organizations need to be more upfront about how identity data influences experiences. They should explain why certain authentication steps are triggered, offer visibility into how data is shared (especially with third parties), and put strong guardrails around the AI systems tied to identity – enforcing least privilege access so agents can only operate within clear, defined boundaries. In an AI-driven world, identity powers personalization while also holding systems accountable and helping to keep consumers and organizations safe.
Move beyond personalization to explainability
For years, personalization has been a competitive differentiator. But personalization without explanation can quickly feel invasive rather than helpful. As AI becomes more embedded in digital experiences, explainability must become a core design principle. Consumers should be able to see when AI is influencing outcomes. Adaptive authentication steps should come with clear, contextual explanations. And users should have access to intuitive dashboards that allow them to manage their data preferences and permissions.
Achieving this requires collaboration across security, privacy, product, and customer experience teams. AI-driven decisions must be auditable internally, defensible to regulators, and understandable to the people they affect. Because AI systems act continuously, this requires continuous governance where every action is evaluated and recorded in real time. This also reinforces core privacy principles such as data minimization, purpose limitation, and user visibility into how their data is applied. Explainability is a governance imperative.
The agentic era: when AI becomes the consumer
The next big shift is already taking shape with AI agents acting on behalf of consumers. Because these agents operate autonomously, they require explicit delegation rather than simply impersonating the human user. These agents will handle everything from browsing to purchasing and subscriptions, and both consumers and their agents will need to be trusted. That trust will increasingly depend on how transparently organizations handle data on behalf of both humans and their agents. This shift also changes what loyalty looks like.
For CISOs and CDOs, it raises a new set of challenges. How will these agents judge whether your brand is trustworthy? Will your systems reflect fairness and consistency? And can you demonstrate responsible AI practices in ways that machines, not just humans, can recognize and verify? It’s no longer enough to say you’re trustworthy. Trust has to be built into how your systems operate, something that can be measured, proven, and reinforced over time.
What security and identity leaders should do now
Getting ahead of this shift means taking intentional steps now, and you can start by building transparency directly into your identity architecture. This includes making data usage visible, understandable, and controllable for end users. It also means treating AI agents as first-class identities, managing both human and non-human actors under a unified identity framework. Adaptive authentication and risk scoring should be explainable in plain terms. And people should have easy access to controls that reflect their identity preferences.
Next, think about what it means to operate in a world where AI agents are part of the customer journey. How will those agents evaluate your APIs, your pricing logic, or your identity verification processes? System-to-system trust requires transparency that is structured, consistent, and machine-readable.
Finally, move beyond one-time disclosures and toward ongoing proof. Transparency is demonstrated over time through consistent, visible actions. That could mean regularly updating your commitments, sharing how your governance works in practice, and showing accountability as decisions happen, not after the fact. By moving identity and security enforcement into runtime, organizations can ensure every AI action is accountable at the exact moment of execution.
As data is continuously collected and acted upon, transparency becomes the mechanism through which organizations demonstrate responsible stewardship. In a world where technology acts on behalf of the consumer, trust becomes the ultimate differentiator. Organizations that embed transparency into how they operate will lead in the AI era, where it serves as the foundation for trust and long-term progress.
