As AI continues to transform industries and everyday life, one question keeps coming up: can we trust it? Not just whether it works—but whether it respects our privacy, treats us fairly, and protects sensitive information.

Many companies are turning to Privacy-Enhancing Technologies (PETs)—a group of tools designed to help organizations build AI systems that promise to not only be powerful but also responsible. And while the tech behind them can be complex, the reasons they matter aren’t. At their core, PETs can help bridge the gap between innovation and integrity.

Here are five ways PETs help build trust in AI systems today, based on insights from the Centre for Information Policy Leadership’s 2025 report on PETs in AI.

They Let Data Do Its Job - Without Exposing It

Whether it’s customer behavior, medical records, or financial details, AI often needs real data to learn and make predictions. But PETs like federated learning and homomorphic encryption allow that learning to happen without exposing the actual data. That means companies can train accurate models while keeping sensitive information private - even from their own developers.

Why it builds trust: Users don’t have to trade privacy for personalization or better services.

They Can Open Doors to Collaboration Without Compromise

Many AI breakthroughs require pooling data across organizations - think hospitals sharing medical images or financial institutions fighting fraud. Secure Multi-Party Computation (SMPC) and Trusted Execution Environments (TEEs) allow different parties to collaborate without giving up control or revealing proprietary information.

Why it builds trust: Collaboration becomes safer and more inclusive, even in sensitive industries.

They Can Make AI Fairer and More Inclusive

PETs can also help improve data quality and reduce bias. For example, synthetic data can be used to fill gaps in underrepresented groups or edge cases without risking real individuals’ privacy. This can lead to AI systems that work better for more people.

Why it builds trust: Fairer models mean fewer surprises and better outcomes across diverse populations.

They Support Regulatory Compliance (and Show You Care)

Using PETs shows that an organization is serious about data protection by design. In some regions, PETs like differential privacy are even recommended by regulators to meet compliance standards. Even when not required, they demonstrate a proactive approach to responsible AI.

Why it builds trust: Transparency and accountability go a long way in building long-term relationships with users and regulators alike.

They Help Keep the Human in the Loop

Some PETs are designed to make data processing more transparent and explainable. They support auditability and can be part of systems that allow individuals to exercise control over their data—even if it’s been anonymized or transformed.

Why it builds trust: Empowering users fosters confidence and ensures AI systems are not just technically secure, but ethically aligned.

Real-World PETs in Business and Policy

For companies building or deploying AI, PETs aren’t just a technical layer - they're a signal of values. Investing in these technologies shows customers, partners, and regulators that privacy isn’t an afterthought. It’s part of the blueprint.

This isn’t just theory. Many organizations are already putting PETs to work. For example, financial institutions have used federated learning and synthetic data to train fraud detection models without sharing sensitive customer data. In healthcare, multiple hospitals collaborated to train cancer detection models using federated learning combined with secure enclaves, protecting both patient privacy and institutional confidentiality. Meanwhile, technology companies like Google, Apple, and IBM are incorporating techniques like differential privacy and homomorphic encryption into products that millions use daily.

Governments are stepping up, too. Singapore’s PDPC has published a proposed guide on synthetic data, offering practical steps for safe generation and use. Across Europe, regulators are exploring risk-based standards for anonymization using PETs, while countries like Norway and Malaysia are piloting AI sandboxes to support responsible experimentation with privacy-protective tools.

These examples show that PETs are more than emerging: they're becoming expected.

Final Thoughts

PETs aren’t just privacy tools, they’re trust tools. As we move toward a future where AI systems play a bigger role in our lives, it’s not enough for them to be smart. They need to be safe, fair, and worthy of the trust we place in them.