Sachin Agrawal, Managing Director at Zoho UK, discusses how, as organisations accelerate cloud adoption, customers are paying much closer attention to how their data is being handled.
Privacy is no longer just a legal requirement anymore, it’s becoming a real test of trust and plays a significant part in how customers judge the organisations they choose to work with.
In a cloud first world, being open about how data is collected and protected gives organisations a competitive advantage. Those who fall short risk losing trust fast, and once that trust is gone, it is incredibly hard to win back.
The shift to cloud-based systems has transformed how organisations operate. This change has unlocked scalability and cost-efficiency, but it has also created new expectations.
Customers today are more aware than ever of how their data might be used, and far less forgiving when organisations fail to protect it.
Privacy as a Cloud Differentiator
Privacy is increasingly becoming a crucial factor in the choice of cloud-based services. While performance and cost remain important, they no longer outweigh the concerns about how data is handled.
Transparency is now a major driver of trust, with 46% of organisations saying that clear communication about how data is used is the most effective way to build customer confidence, even more influential than compliance or breach protection. Customers want to know how their data is managed, who has access to it and whether it is being used for purposes beyond what they agreed to.
Organisations that can explain their privacy commitments in clear terms and support them up with transparent practices will stand out. Those that rely on vague policies or avoid giving straight answers risk losing to competitors who prioritise clarity and accountability.
This shift reflects how Software as a Service (SaaS) has evolved. SaaS has moved key responsibilities such as security and compliance from the customer to the vendor. At the same time, the market is feeling pressure from the so-called “SaaSpocalypse”, a dramatic sell-off in SaaS valuations driven by businesses reassessing how AI could impact their operational systems.
With pressure mounting on SaaS providers to remain relevant amid the anticipated impact of AI, organisations are choosing vendors more carefully. This is driving greater demand for SaaS providers to differentiate themselves, with robust privacy practices emerging as a key factor. In this environment, strong privacy practices are no longer optional – they are a core differentiator.
Responsible Cloud Data Management
Responsible cloud data management is essential for maintaining trust and ensuring that privacy commitments are upheld in practice. Some organisations still collect far more data than they need, often for commercial purposes, without considering the risks. In the cloud, this can create unnecessary exposure and make it harder to manage data safely.
Good cloud data management begins with minimisation. Organisations need to decide what data is genuinely necessary and avoid collecting anything that does not serve a clear purpose and one which enhances the customer experience.
Strong governance is equally important, and teams need to understand who owns the data, who can access it and how those decisions are monitored over time. Clear rules and accountability help ensure that data is handled consistently and responsibly across the organisation.
Security must be built into the cloud systems from the start. Access controls and regular monitoring are basic requirements, but responsibility doesn’t end with technology.
The rapid rise of AI within cloud platforms has significantly raised the stakes. However, Large language models (LLMs) often require substantial volumes of data to train effectively and deliver accurate outputs. Organisations should take a contextual approach to their AI systems, ensuring AI models only have access to the data they require to function effectively once deployed, and data which is contextually relevant to the organisation or industry. A variety of right-sized AI models for tasks should also be considered. There is a place for small and medium, as well as large AI models to optimise business outputs and some require more data than others.
Well governed data is what makes AI safer, more accurate and more reliable. It also ensures privacy for added data protection, avoiding sensitive or purely company-specific information making its way into the training of public AI models.
This is something that potentially reveals unique strategies that drive differentiation and competitiveness of one company being made available for other businesses to benefit from.
However, there is a critical distinction between responsibly governed AI training and uncontrolled operational use. In a workplace setting, particularly where teams deploy internal or personal LLMs to support productivity, these tools must be configured with strict parameters around the data they can access.
Without these boundaries, the risk of overexposure increases. AI-driven automation means data is processed faster and at a greater scale, so even a small governance gap can have consequences. AI must be carefully designed to understand not only what data is useful, but what data is off-limits.
This is where governance becomes essential in preventing the rise of shadow AI, which is when employees use unapproved AI tools or feeding sensitive company information into public LLMs without oversight. Clear internal policies monitored AI access points and defined data environments are critical to ensuring innovation does not outpace accountability.
When organisations minimise what they collect and set clear rules for how data is used, they create a safer cloud environment and are far more likely to earn and keep customer trust.
Building a Privacy First Culture
Technology alone cannot deliver trustworthy cloud practices. A privacy-first culture requires a shift in mindset across the entire organisation. Privacy must be part of everyday decision-making across teams, and not just for legal or IT teams.
When privacy becomes a shared organisational value, it influences how teams communicate and how customers are supported. This means making privacy central to planning and decision-making from the start.
Organisations can introduce mandatory AI usage guidelines, implement clear approval processes for new AI tools or conduct regular privacy impact assessments before deploying data-driven technologies.
Appointing privacy champions within departments can also be highly effective. These individuals act as local advocates for responsible data use, that can promote necessary training and ensure privacy considerations are embedded into everyday work.
Leadership must set clear expectations that privacy is a strategic priority, not a compliance task. Customer-facing teams should be able to explain data practices in simple terms, and internal processes should make it easy for employees to raise concerns early.
Transparency with customers is essential, and organisations need to clearly communicate about what data they collect, the purpose and what rights customers have. This isn’t just good practice, it improves outcomes.
Recent studies show that 97% of organisations reported that their transparency efforts are improving customer trust, showing that clarity isn’t just a value but a proven way to strengthen relationships.
Long‑term trust is driving up on the leadership agenda as they realise that it matters more than short‑term gains, and privacy sits at the heart of that.
Embedding privacy into everyday decisions, across the whole organisation, is what prevents these failures and strengthens trust over time. In a cloud-first world, privacy is the foundation that supports meaningful innovation.
