Responsible AI Is Not Just for Subject Matter Experts—It’s Everyone’s Job

A single irresponsible decision recommendation from an otherwise perfect AI system can erase years of brand value, customer trust and shareholder value overnight. Why does this happen? Because technology alone doesn’t guarantee responsible outcomes for AI.

Technology is transforming society at an extraordinary pace. AI is now embedded in the services people use every day and as this technology evolves — becoming more capable, more autonomous, and increasingly agentic — new applications are emerging at remarkable speed. While some applications of AI offer the potential for significant benefits, trust in this technology remains fragile due to both technology limitations and human factors. 

Ensuring genuine adherence to Responsible AI principles – including to privacy, security and ethical principles – is not a compliance exercise. It is of paramount importance because it is about making sure that AI systems ultimately produce results that are consistent with human values, serve human needs and improve human lives. This human-centred approach can also have important secondary benefits for an organization, such as strengthening trust with its customers and employees and reducing regulatory risk. 

For decades, privacy and security have been the foundations of digital trust. Today, Responsible AI is emerging as a third pillar. The challenge for organizations is to bring them together into a cohesive framework that combines strong governance, technical controls and a culture of accountability at every level. Each of these disciplines brings a different focus:

• Privacy safeguards personal data and upholds ethical principles, especially in human-rights-based legislative frameworks such as the GDPR. Privacy compliance entails treating individuals fairly — as far as the processing of their personal data is concerned — and respecting their rights to their data and to a private life. 
• Security protects information systems and data assets, ensuring data confidentiality, availability and integrity. 
• Responsible AI builds on privacy and security principles but focuses on challenges unique to AI systems, including by incorporating an ethical dimension that requires careful consideration of how the technology may impact individuals and society.

Together, these disciplines must form a holistic trust framework. When separated, weaknesses in one undermine the overall outcome. A secure system that violates privacy cannot sustain public trust. Likewise, a technically advanced system will fail to create societal benefits if it is not designed to solve real problems.

The shared responsibility model for AI

So how can organizations ensure that the AI that they use, sell or develop is for people?

The journey towards Responsible AI does not begin with a blank slate. Privacy and security disciplines have long relied on structured processes, governance mechanisms, appropriate technical controls and accountability frameworks, as formalized in management systems based on internationally recognized standards. In many organizations, privacy, security and legal teams have therefore been tasked with addressing compliance with emerging AI laws, guidance, and broader Responsible AI principles, given their remit and expertise. Yet it is critical to recognize that responsibility cannot stop with compliance teams or AI specialists alone: accountability must be embedded across the entire organization in every role — from engineers writing code, to managers setting priorities, to executives approving strategies.

Some elements are critical to support this “shared responsibility model”:

• A culture of accountability. Clear expectations – reinforced by leadership and lived through day-to-day behaviors across teams – that everyone has a role to play in Responsible AI.
• Training and awareness. Equipping personnel at all levels with a level of knowledge tailored to their role and adequate to enable them to think through risks and possible ethical implication.
• Critical thinking and decision making. Encouraging personnel to question why AI is used, who it may impact, whether it is the right “tool” to use to solve a problem and if so, how it should be used to that end. Creating safe spaces and channels of communication where teams can raise concerns, ask questions and challenge assumptions. This bottom-up feedback can also generate valuable insights that can flow back into governance structures, helping leaders spot blind spots and continuously improve. 
• Escalation mechanisms. Clear processes to ensure that when an issue is identified by anyone across the organization, there are defined channels to raise them and ensure timely action.
• Transparency. Making Responsible AI principles visible and understandable across the organization, not simply hidden in policies. 

Without a culture that promotes ethical awareness, without training to support good judgment, and without accountability to back decisions, Responsible AI risks being reduced to a checklist exercise rather than becoming an operational reality.

The future of digital trust depends on the convergence of privacy, security, and Responsible AI. Algorithms will continue to evolve, but technology alone will never guarantee responsible outcomes. Human factors — such as culture, critical thinking and responsible decision making at every level — are necessary to make Responsible AI real. Organizations that embrace this convergence will not only be best placed to meet regulatory and societal expectations. They will also earn the most valuable asset in the digital economy: sustained trust.

Treasure Data’s approach

At Treasure Data, we view Responsible AI as an extension of our longstanding commitment to privacy, security and ethical integrity. It is not a separate initiative but a natural evolution of our governance framework. 

Our approach is cross-functional by design. Any new AI service that we intend to provide to our customers undergoes a privacy and ethics, security and – where appropriate – a legal review. We assess not only compliance with laws applicable to us but also how we can support our customers in meeting their own compliance obligations and in adopting a responsible use of AI. This dual focus allows us to address risks proactively while promoting trust and accountability throughout the value chain.

As far as our own use of AI tools is concerned, we have established company-wide initiatives to identify use cases that can return tangible value for the organization, prioritize and develop them, and test outcomes to measure effectiveness and return. The aim is ensuring that AI deployment is intentional and used where it can genuinely enhance processes, improve efficiency or create insight – always with human oversight and accountability. These initiatives, as well as any internal deployment of AI tools, are underpinned by a policy setting our principles and responsibilities at a direction level. This policy is complemented by standard operating procedures that define the operational steps required for responsible deployment, including mandatory internal reviews where risks thresholds are met.

By embedding Responsible AI into our governance framework, we aim to ensure that compliance, data protection, ethics and innovation evolve together. Learn more about our approach to trust for data and AI.