2026: Privacy, AI, and the new rules of trust

In 2025, artificial intelligence began reshaping the foundations of business. End-user adoption accelerated across industries, many use cases moved from experimentation to production, and expectations expanded rapidly (and sometimes unreasonably) for efficiency and business impact. Most importantly, AI drove a rethink across every part of the enterprise, from product development to customer engagement.

Privacy programs felt the impact of these shifts almost immediately. AI introduced new questions around data use, accountability, and transparency in each and every project that touched personal data. Small privacy teams and fragmented tooling struggled to absorb this additional volume and complexity of projects, driving backlogs and inconsistent decision-making.

The pace of change has never been so fast – and it will never be this slow again. AI doesn't just change data governance- it redefines what it means to trust technology. 

These shifts will have an enormous impact on trust between consumers and companies. In 2025, the question for many companies was “Should we use AI?”. 

In 2026, that question shifts to “How do we make sure we can trust the AI we use?”

If you can explain in a few clear sentences what data you collect, why, how AI uses it, and who is accountable, you’re not just compliant. You’re building a durable trust advantage. 

Trust becomes an operational necessity, and privacy leaders must ensure they have a seat at the table. The challenge for all is that the rules of trust have themselves shifted with the evolution of AI: Do we know what to trust? Do we even know why to trust?  

To explore these new rules of trust, I recently moderated a panel with two long-time industry experts, Lindsay Hohler, Partner, Risk Advisory Services at Grant Thornton Advisors LLC and Serena Tejani, Cyber Transformation Partner at KPMG Canada. Our goal was to move from problem to solution, and surface practical steps teams can take to get started.

We discussed how, without clear frameworks, governance fragments and accountability fades, particularly in machine-to-machine interactions where ownership feels abstract. Shadow AI fills the gaps while teams wrestle with where responsibility sits, how oversight works, and how to build confidence without slowing progress. At the same time, people across the organization question their role, worry about displacement, and look for clarity on how AI fits into their work. The challenge spans people, process, and technology – and privacy sits at the center of that tension.

To tackle these complex topics, our panel focused on four areas where many organizations struggle to put the right controls in place for responsible AI use: identity, data, consent, and decision-making.

New rules for identity

AI agents are moving from the lab to mainline business activities. Every agent needs an identity, owner, authentication, and least privilege, just like for a human. The business needs to understand the same things about AI agent activities as it does human beings: Who took this action, why, when, and what data did it touch?

In 2026, privacy leaders must form a tight partnership with IT around identity to collaboratively define where agents are allowed to operate, what systems they touch, what data they access, and how the organization reviews their performance over time.

The new rules for identity must support exponentially more activities, many of which may be driven by AI agents and not directly tied to a human. 

New rules for data

AI raises the bar on data discipline. Organizations need a clear map of what data they hold, where it flows, which models and third parties touch it, and how AI uses it. Classification, lineage, and minimization increase in importance as part of the controls framework to stop teams from copying sensitive data to new places and training models on data they shouldn’t.

Data governance is challenging no matter what, and because many organizations grow through acquisition and/or operate in silos, their data estates are highly fragmented. In 2026, privacy leaders, again, must become partners with data teams to ensure there is visibility and appropriate controls for the most sensitive and risky personal data sets. 

The new rules for data must identify and remediate not only new vectors of data loss, but also new vectors of data misuse, and both at high volume. 

New rules for consent

Consent is central to privacy. Organizations must provide individuals with clarity on what personal data gets collected and for what purpose. Individuals must be able to withdraw consent and move their data when they end a relationship with an organization. 

If AI is operating on personal data sets for the purpose of, for example, improving personalization and consent already exists from the individual for that purpose, additional consent may not be required. But if AI models are trained on that personal data, then that data might end up being used for other purposes and the organization will not be able to stop using the data when the individual withdraws consent. In 2026, privacy teams must know if consented data is being used for AI model training and have controls in place to prevent inappropriate use. 

The new rules for consent must operate effectively in a fast-changing regulatory and technology environment, provide clearer notice language, stronger preference handling across channels, and tighter linkage between consent and downstream processing. 

New rules for decision-making

Explainability and accountability are difficult problems in the world of AI. Organizations struggle to explain how an AI system reached an outcome and what human in the organization is accountable for its results and actions.

Decisions should be driven by facts. But when it comes to AI systems, as mentioned earlier, do we know what to trust? Do we even know why to trust? In 2026, privacy teams must have a seat at the table when decisions are being made about how to use personal information in an AI system and what level of trust exists for that system.

The new rules for decision-making must account for the ambiguity of thinking and potentially autonomous systems, while minimizing that ambiguity through appropriate monitoring, review paths, and escalation when outputs are wrong. Formal governance ensures that organizations do not confuse regulatory alignment with actual control over how AI behaves in practice.

Trust breaks when you go fast, cannot explain decisions, and cannot name an owner. 

What privacy leaders can do next

Preparing for 2026 starts with action. Privacy programs that scale focus on leadership, accountability, context, and openness, while resisting the temptation to treat governance as a checklist. 

• Become AI-literate: Privacy leadership starts with setting clear priorities and reinforcing them consistently from the top of the organization. When leaders demonstrate a realistic understanding of AI risk and embed privacy into ethics programs and codes of conduct, teams gain clarity on what responsible use looks like in practice. This requires that privacy leaders become AI-literate.
• Formalize accountability and risk tolerance: Clear ownership of data and AI risk removes ambiguity when decisions move fast. Defining risk appetite, monitoring adherence, and assigning operational responsibility across executives and experts creates a shared model that supports coordination rather than silos.
• Stay ahead of what’s next: Effective governance requires designing for change. What organizations put in place today must adapt as AI capabilities advance, public expectations shift, and regulatory frameworks mature over the coming years. Privacy leaders stay ahead by treating governance as a living system that evolves with technology and risk, rather than a fixed set of controls.
• Do not mistake compliance for risk management: Meeting regulatory requirements does not guarantee sound risk decisions. Privacy programs stay credible when they resist checkbox approaches and keep open dialogue with the business to reflect how expectations evolve over time.

AI is forcing organizations to move faster but trust, transparency, and privacy-by-design are now the conditions for permission to innovate.

Programs build influence when governance scales alongside the business, decisions remain explainable, and accountability holds steady under pressure.  In 2026, privacy leadership is not about compliance checklists - it's about shaping how AI earns and keeps trust. 

To dive deeper into the topics above, watch the on-demand webinar 2026: Privacy, AI, and the new rules of trust with experts from Grant Thornton Advisors LLC and  KPMG Canada.

Key questions privacy leaders are asking about AI in 2026