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She Said Privacy/He Said Security · Yesterday · 35 min

How to Solve AI's Data Problem

Cillian Kieran is the Founder and CEO of Ethyca, where he leads its product vision, engineering strategy, and growth. A serial entrepreneur and privacy engineer with two decades of experience, he launched Ethyca in 2018 to bring privacy-by-design infrastructure to developers. Earlier, he built the digital consultancy CKSK to 100-plus employees across four countries, serving clients like PepsiCo, Heineken, and PlayStation. He pairs deep technical grounding with a track record of scaling data-intensive businesses. In this episode… As companies expand their use of AI, they need guardrails around how the technology is used by employees and how those systems use enterprise data. Because models are trained on data, bias can be introduced through this information, while systems also continue to use this data to perform different tasks. This creates risk, and managing it requires evaluating what data a model or tool can access, its intent when it uses that data, and the economic, ethical, and regulatory implications of that intended use. So, what controls do companies need to manage AI and company data safely? Because AI wants to satisfy a user's request, it will keep trying to access data unless clear boundaries define what it can use. Using AI responsibly requires managing the data behind it alongside the technology itself. To do this effectively, companies need to know what data they have collected, where it came from, whether they have sufficient consent, and what legal basis applies to its use. Once companies understand those elements, they can give AI access to the information it needs for a specific purpose while limiting what falls outside that use, allowing them to leverage the value of their data while minimizing risk. Governance also needs to move from written policies into controls that can be enforced on a system in real time. And while AI can streamline processes like privacy risk assessments by gathering information and comparing it against policies, past assessments, and relevant requirements, human privacy experts still need to review the output for accuracy and challenge AI when it's wrong. In this episode of She Said Privacy/He Said Security, Jodi and Justin Daniels talk with Cillian Kieran, Founder and CEO of Ethyca, about the importance of unifying AI governance and data governance. Cillian explains how bringing these two areas together helps companies stay in control of their data while allowing the technology to support the business. He discusses why companies are turning to open-source models for greater sovereignty and cautions that bringing a model in-house can still introduce risks. Cillian also addresses the tension between AI's need for data and privacy's data minimization principles, and he stresses why professionals need to think critically, question AI outputs, and avoid relying on AI as a crutch.

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Cillian Kieran is the Founder and CEO of Ethyca, where he leads its product vision, engineering strategy, and growth. A serial entrepreneur and privacy engineer with two decades of experience, he launched Ethyca in 2018 to bring privacy-by-design infrastructure to developers. Earlier, he built the digital consultancy CKSK to 100-plus employees across four countries, serving clients like PepsiCo, Heineken, and PlayStation. He pairs deep technical grounding with a track record of scaling data-intensive businesses.

In this episode…

As companies expand their use of AI, they need guardrails around how the technology is used by employees and how those systems use enterprise data. Because models are trained on data, bias can be introduced through this information, while systems also continue to use this data to perform different tasks. This creates risk, and managing it requires evaluating what data a model or tool can access, its intent when it uses that data, and the economic, ethical, and regulatory implications of that intended use. So, what controls do companies need to manage AI and company data safely?

Because AI wants to satisfy a user's request, it will keep trying to access data unless clear boundaries define what it can use. Using AI responsibly requires managing the data behind it alongside the technology itself. To do this effectively, companies need to know what data they have collected, where it came from, whether they have sufficient consent, and what legal basis applies to its use. Once companies understand those elements, they can give AI access to the information it needs for a specific purpose while limiting what falls outside that use, allowing them to leverage the value of their data while minimizing risk. Governance also needs to move from written policies into controls that can be enforced on a system in real time. And while AI can streamline processes like privacy risk assessments by gathering information and comparing it against policies, past assessments, and relevant requirements, human privacy experts still need to review the output for accuracy and challenge AI when it's wrong.

In this episode of She Said Privacy/He Said Security, Jodi and Justin Daniels talk with Cillian Kieran, Founder and CEO of Ethyca, about the importance of unifying AI governance and data governance. Cillian explains how bringing these two areas together helps companies stay in control of their data while allowing the technology to support the business. He discusses why companies are turning to open-source models for greater sovereignty and cautions that bringing a model in-house can still introduce risks. Cillian also addresses the tension between AI's need for data and privacy's data minimization principles, and he stresses why professionals need to think critically, question AI outputs, and avoid relying on AI as a crutch.

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