Whitepaper

Your guide to data security in the age of AI

The foundations for safe and responsible AI adoption.

AI is rapidly becoming part of everyday work, helping organisations increase productivity, streamline processes and unlock new ways of working. But as adoption accelerates, so does the need to ensure the data powering AI is properly protected. Without the right foundations in place, AI can amplify existing data security challenges, making sensitive information easier to access, share or expose.

Preparing for AI isn’t just about choosing the right technology. Organisations need to understand where their sensitive data lives, who has access to it and how it is being shared. At the same time, they need visibility and control over how employees interact with both approved and unsanctioned AI tools, without creating unnecessary barriers to innovation.

Building these foundations before scaling AI can help organisations reduce the risk of data oversharing and leakage, strengthen governance and give employees the freedom to take advantage of AI more securely.

Our latest whitepaper explores how to build the right data security foundations for AI, from preparing and protecting your data to governing how AI is used across your organisation.

Download your copy to discover how to embrace the opportunities of AI while keeping your most valuable data secure.

The AI data security challenge in numbers

75%

of knowledge workers are already using AI at work

78%

of AI users are bringing their own AI tools to work

+80

of leaders cite sensitive data leakage as their main concern

Portrait of Paul Conaty

About the author

Paul Conaty leads CWSI’s Secure Data practice, providing strategic and practical guidance to organisations across Ireland and internationally. With over 20 years’ experience spanning engineering, technical and leadership roles, he supports organisations in strengthening data security, governance and compliance.

A recognised voice in cybersecurity and data protection, Paul works with both public and private sector organisations to reduce risk and improve confidence in how data is managed. His approach focuses on practical, scalable measures; from improving visibility and strengthening controls to enabling organisations to adopt new technologies, including AI, with greater assurance.