Building trust in AI from the ground up: How you can secure the data behind it

As AI adoption accelerates, is your organisation keeping pace with the data risks? These risks are not just new challenges; they can exacerbate existing weaknesses in data security and governance. AI adoption magnifies these issues, bringing the underlying issues to the surface and causing organisations to question the overall integrity of the AI model due to the lack of data trust.

As AI-powered tools become more embedded in daily operations, security, data and information leaders now face two important priorities:

1. Training AI models on high-quality data. Without sufficient oversight, AI systems can reinforce bias, misinterpret information, or generate outputs that are difficult to rely on.
2. Applying governance and security controls to AI-powered workflows. As AI interacts with sensitive business data, organisations should enforce policies that can prevent compliance failures, security breaches, and unauthorised access.

Recent research highlights the scale of data-related risk facing Irish organisations, particularly in relation to third-party exposure.

In our 2026 PwC Digital Trust Insights Survey, over one-third (38%) of Irish respondents reported a data breach costing their organisation over €500,000, with third‑party breaches now the number one cyber threat for Irish respondents (48%).

Similarly, software supply chain compromise is also recognised as a significant threat with 28% citing it as a top concern.

As organisations rely more heavily on external platforms, suppliers, and software components, AI will inevitably inherit the risks present in those ecosystems. Without effective governance, AI can amplify third-party vulnerabilities rather than mitigate them.

Compounding the risk, many organisations still struggle to get control of their data estate. Unstructured content, legacy systems, and inconsistent access controls make it harder to enforce effective governance and security.

How much can you trust the data powering your AI? If data is inaccurate, unprotected or exposed to security gaps, AI models can generate misleading insights, introduce compliance risks, and compromise sensitive information. Before organisations can unlock AI’s potential, they should first mitigate key data risks, including:

• Data quality risks: AI models trained on unstructured, redundant, or outdated content can generate unreliable outputs, leading to poor decision-making.
• Data protection risks: Uncontrolled access permissions to sensitive data coupled with AI tools can expose sensitive data to unauthorised users, increasing security vulnerabilities.
• Data compliance risks: Without proper classification and oversight, AI-driven automation may process sensitive data in ways that violate privacy regulations.
• Data exposure risks: AI tools with insufficient access controls can increase the risk of insider threats, unauthorised data sharing, and leakage of sensitive information.

Without addressing these foundational risks, AI-driven tools may introduce more uncertainty than innovation. Many organisations often need a clearer strategy for governing the data that fuels AI, or they risk AI working against them instead of for them.

How can organisations embrace AI and manage data risk? Technology plays an integral role in mitigating risks and strengthening AI readiness. The proper tools can help organisations improve data visibility, enforce security policies, and maintain compliance without slowing down progress. A strong data governance and security framework enables AI models to operate on more precise, trusted data while helping reduce exposure to breaches and regulatory failures.

By addressing risks before scaling AI adoption, organisations can deploy AI models that drive growth while maintaining security and trust.

One of the biggest challenges organisations face when adopting AI is content sprawl — unstructured, redundant, and outdated information scattered across platforms. Without clear oversight, AI systems may surface or process content that is outdated, irrelevant, or sensitive.

As AI continues to scale, organisations will increasingly be differentiated not by how quickly they adopt new technologies, but by how effectively they manage the risks that come with them.

Taking the following steps can help build a strong foundation for AI-driven innovation while decreasing exposure to threats and regulatory concerns:

• Improve compliance and governance controls for AI workflows: Classify, label, and protect sensitive data, so AI models interact with governed and compliant information.
• Reduce data exposure and access sprawl: Enforce least-privilege access, monitor content usage, and secure AI-accessible data.
• Establish a trusted data foundation for AI models: Map, inventory, and clean up unstructured data, so AI models are trained on precise and authoritative sources.
• Enhance AI security and threat response: Centralise monitoring and accelerate detection and mitigation of AI-related security risks.
• Adopt a phased approach to AI governance: Start with targeted governance policies, train teams on Responsible AI use, and refine security controls before scaling AI adoption.

By proactively addressing these areas, organisations can confidently deploy AI at scale, while maintaining the trust that adoption and transformation ultimately depend on.

We’re here to help

At PwC, we bring cyber, data governance, and Responsible AI expertise — paired with hands‑on knowledge of the most advanced AI-powered tools —to reduce exposure and improve decision‑making. We start with a clear view of your data estate and third‑party risk, then design pragmatic controls and the operating model to sustain them. You get auditable governance aligned to EU regulations, faster incident response, and AI outcomes your organisation can stand behind. Contact us today.