Wipro’s Ivana Bartoletti on AI Governance and Privacy

As AI transforms industries, data has become its most valuable fuel, powering innovation, automation and predictive insight.

It can be reused, copied and repurposed endlessly, making it both an unparalleled asset and a source of mounting complexity.

The prevailing model of data extractivism — collecting and exploiting vast amounts of information — has led to growing ethical and environmental concerns.

From algorithmic bias and privacy breaches to the soaring energy demands of data centres, AI’s reliance on raw data is under increasing scrutiny.

Enter synthetic data: artificially generated datasets designed to replicate the patterns of real-world information without exposing sensitive details.

For AI development, it offers a promising solution, enabling safe, scalable model training while mitigating privacy risks and reducing environmental impact.

While not without challenges, synthetic data is emerging as a critical enabler of responsible AI, balancing innovation with transparency, ethics and long-term sustainability.

Ivana Bartoletti is the Global Chief Privacy and AI Governance Officer at Wipro.

In this role, she guarantees strong privacy, legal safeguards and security remain at the core of both Wipro's internal AI initiatives and those it offers for its clients. 

She is the author of An Artificial Revolution: On Power, Politics and AI and the co-founder of the Women Leading in AI Network.

Ivana is also an adviser to the Council of Europe on gender rights and AI. 

Wipro continues to craft innovative solutions that tackle its clients' complex digital transformation needs.

The global technology services and consulting company has a workforce of more than 230,000 people and partnerships across 65 countries.

“At its core, the work of my team is about embedding ethical principles into the heart of technology,” she says.

In this Q&A, Ivana highlights how synthetic data supports safe AI innovation, stressing the need for strong governance, transparency and safeguards to balance progress with responsibility.

With such a mass volume of data generated daily, how should organisations respond to the associated environmental and ethical challenges?

The fundamental question organisations should ask is: do we truly need all this data? Simply generating vast volumes of information is not of inherent value. 

Organisations need to critically evaluate their data collection practices and recognise the environmental impact of data storage.

Equally important is the adoption of eco-conscious practices, such as investing in energy-efficient hardware, deploying deduplication tools to eliminate redundant files and important not only for reducing environmental harm but also for upholding ethical standards in data management.

In light of increasing concerns over data breaches, profiling and lack of transparency in data collection, how can companies rebuild public trust and ensure individuals have meaningful control over their personal data?

Transparency is key.

If someone is shown a targeted advertisement or denied a loan, they should be informed of the reasoning behind these outcomes, rather than being left in the dark by opaque algorithms. 

Organisations must go beyond simply complying with privacy regulations, they need to communicate clearly with people and be open about how their data is being used to make decisions that impact their lives.

Expecting people to manage every aspect of their personal data is increasingly unrealistic in our highly connected world. Who genuinely reads all the terms and conditions? Yet that doesn’t mean people shouldn’t feel confident in the systems and applications they rely on.  Quite the opposite. Organisations should go above and beyond helping users understand what they need to and empowering  — rather than overwhelming — them.

In an age where privacy, security, ethics and innovation intersect, companies that embed these values into their operations not only safeguard their users, they also earn trust and gain a meaningful competitive edge.

How does synthetic data work in practice and what are the potential benefits of it in enhancing privacy, reducing environmental impact and improving data availability?

Synthetic data is artificially generated using models trained on real datasets to replicate their patterns, structures and statistical properties. 

When done well, synthetic data should yield similar analytical outcomes to the original data without revealing any real individual’s information. 

Its effectiveness hinges on how accurately it reflects the original dataset while removing identifiable elements.

In the context of machine learning, synthetic data is playing an increasingly vital role. 

From a privacy standpoint, it offers significant advantages: it allows organisations to develop and test models without compromising personal data, which is particularly valuable in highly regulated or sensitive sectors.

Synthetic data can also help reduce environmental impact by lessening the need for repeated data collection, which is often energy and resource intensive. 

It also enhances data availability, enabling innovation in situations where real data is scarce, sensitive, or costly to obtain.

Challenges remain in ensuring high enough data quality, avoiding the risk of re-identification and balancing the trade-offs between privacy, accuracy and utility are critical areas that require continued attention and oversight.

What role should regulation and governance play in shaping responsible data practices, particularly with emerging technologies like synthetic data? How can organisations balance innovation with robust privacy and ethical standards?

Strong corporate governance plays a critical role in shaping responsible data practices, particularly in the context of emerging technologies such as synthetic data. 

Again, this goes far beyond simply meeting legal obligations, it’s about earning and maintaining trust with consumers, partners, and regulators.

Organisations must adopt a comprehensive risk management strategy, underpinned by clear governance structures that span all departments. Education is vital. 

Every team member should understand how to develop and use technologies effectively and responsibly, including how to engage with AI tools without becoming overly dependent on them.

Responsible innovation means designing with privacy, security and legal compliance in mind from the outset, not collecting data indiscriminately, but using it in a fair, transparent and ethical way. 

Synthetic data can be a key enabler in this process, supporting the development of advanced AI systems while reducing the risks associated with using real personal data.

Ultimately, organisations should view privacy and ethical data use not as a regulatory burden, but as a strategic advantage.

Investing in upskilling teams and embedding responsible practices into the innovation lifecycle is essential for building long-term resilience and trust.

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