What Risks Does AI Pose to Confidentiality?

AI can pose significant risks to confidentiality because it increases both the complexity and the exposure of personal data within an organisation.

First, AI systems often process large volumes of sensitive information drawn from multiple sources. The more data that is aggregated, integrated and reused across systems, the greater the impact if something goes wrong, whether through unauthorised access, accidental disclosure or system failure.

Second, AI introduces new technical vulnerabilities. Machine learning models can be targeted by specialised attacks designed to extract information from them. In some cases, attackers may be able to infer details about individuals whose data was used to train a model, even if that data is not directly visible. This creates a risk that confidential information could be reconstructed or exposed indirectly through model outputs.

Third, AI ecosystems are typically complex and heavily reliant on third-party tools, open-source components and external suppliers. Each integration point increases the attack surface and can make it harder to maintain consistent security controls.

Finally, there is a human factor. AI projects often involve multidisciplinary teams, and security practices may not always be embedded from the outset, particularly where development is experimental or innovation-led. As such, organisations must adopt a holistic, risk-based approach to security, ensuring robust technical safeguards, supplier oversight, and governance frameworks are in place when deploying AI systems.