As organisations turn to AI to transform their operations and processes, the question of data privacy is becoming increasingly complex. Among the latest AI advancements is agentic AI – designed to autonomously execute tasks without human intervention.
We are already starting to see a number of agentic AI use cases emerge. For example, in the financial sector AI agents can analyse market trends, interpret trading signals, adjust strategies, and manage risks in real-time. In the public sector, AI agents are being used to streamline government services, from assisting the treasury department in responding to emails, to supporting the home office to make recommendations on migration cases. The rapid pace of adoption reflects a growing recognition of its strategic value: 83% of IT leaders say investing in agentic AI is essential to gaining and maintaining a competitive edge.
But for all its benefits, agentic AI’s reliance on vast amounts of personally identifiable data raises significant privacy concerns. More than half (53%) of IT leaders cite data privacy as their main barrier to implementing the technology. And as agentic AI continues to infiltrate both our personal and work lives, these concerns will intensify – especially when applied to industries like healthcare and finance, where data is particularly sensitive.
Safeguarding Sensitive and High-Value Data
For industries that handle a high volume of sensitive and personal data, securing critical and personally identifiable information is the foundation for protecting consumers and gaining their trust. Letting AI agents loose in such an environment without robust safeguards could have severe consequences. For example, banks and payment processors use AI to automate fraud detection. This requires vast amounts of data to drive accurate decision-making. Without the correct security and governance in place, deploying AI agents to make autonomous interventions can leave sensitive information exposed and used inappropriately, eroding trust.
To mitigate this risk, organisations must invest in secure, well-governed data platforms that employ comprehensive encryption and tokenisation. These measures should be applied consistently across all data environments, whether on-premises or cloud-based, and across diverse storage solutions. This is where a unified data platform can help organisations apply data governance across hybrid infrastructures.
Addressing data governance and security mandates
Securing sensitive data is only part of the equation. As governments worldwide tighten regulations to protect privacy rights, organisations must also navigate increasingly complex compliance data protection and sovereignty laws. The growing adoption of agentic AI only complicates this further, as these systems often require access to historical and cross-border data to operate effectively.
To navigate these challenges, enterprises must adopt a granular approach to data governance, supported by a zero-trust architecture – a security model that ensures no user or system is trusted by default. This requires precise mapping of where customer data is stored, enforcing strict access controls, and maintaining comprehensive audit trails to ensure compliance and accountability. By implementing these measures, companies can safeguard sensitive data, ensure compliance with cross-border regulations, and maintain accountability and trust in their AI-driven operations. Modern platforms that unify on-premises and cloud environments make it easier to implement these controls consistently, reducing compliance risk and improving visibility across data flows.
By investing in a modern data architecture, organisations can scale their AI capabilities responsibly – with built-in controls for security, compliance, and governance from the start. This creates a solid foundation not just for agentic AI, but for future technologies that will only increase in autonomy and complexity.
Embracing Agentic AI
Agentic AI is only going to become more embedded in how businesses operate and serve customers in the future.To succeed in this new era, businesses must put secure, well-governed data foundation at the core of their AI strategy.
This means investing in unified platforms, embedding strong privacy controls that bring data, models, and governance together in one place, implementing robust security for every AI interaction, and ensuring transparency and control over the decisions AI makes. Governance must extend across the entire lifecycle of both data and AI models. It’s not just a matter of protecting data – it’s about enabling AI agents to operate safely, transparently, and at scale. For all businesses, confidence in AI must begin with trusted data.
