As artificial intelligence (AI) becomes deeply embedded in everyday business operations, businesses are gaining transformational capabilities, but also face new and evolving risks to personal data. From model inversion to data addiction, digital frontiers are not only smart, they are more vulnerable.
The GDPR and UK GDPR already have clear data security obligations, but these frameworks are not designed with AI-specific threats in mind. However, under Article 32, organizations must “test, evaluate and evaluate security measures regularly.” If your business is deploying AI (whether it processes personal data or automates customer interactions), risk assessments should include new AI-specific breach scenarios.
Some of the dangers are obvious. Inadequate design, lack of monitoring, vulnerabilities in third-party AI components. But others are more opaque. Any chatbot that hallucinates personal medical information, or corrupted image classifiers that mistakenly identify someone as a criminal, can all fall under the legal definition of personal data breach. And that means serious regulation and reputational outcomes.
The fantasies of intelligence, the reality of risk
The complexity of AI models makes it difficult to protect. Model inversion attacks in which outsiders extract sensitive training data from AI systems are no longer theoretical. In fact, they pose a very realistic threat to data privacy. They can characterize identity, medical condition, or behavior from anonymized data.
Equally troublesome is the opacity of the IT supply chain. Many companies rely on third-party AI tools or frameworks, often open source, increasing the attack surface exponentially. A single component vulnerability can cascade across the system, making it difficult to determine responsibility when the blurred roles of “customers” and “suppliers” along the chain hit a blow.
In this environment, traditional data breach planning is not sufficient. Companies need new playbooks that take into account both familiar and AI-specific risks.
Practical Steps for Businesses: Preventing Panic
First, organizations need to maintain a live inventory of deployed AI tools, including the testing phase. From there, an AI-specific risk assessment should be performed beyond the general DPIA. This means analyzing both personal and impersonal data, how AI systems train, data to be touched, and where vulnerabilities lie.
Incident response plans need to be coordinated to reflect AI-specific threats. Who is responsible if an employee misuses the AI model or if the tool generates harmful hallucinations? Who will report the violation? These questions must be answered clearly in front An incident occurs.
On the other hand, suppliers must do more than build clever tools. They must demonstrate privacy by design, provide usage guidance, and be contractually committed to minimizing data and updating security. Suppliers need to protect their models from threats on the part of their customers, especially when client systems are compromised and used as vectors of attacks.
Contracts and cooperation are more important than ever
The boundaries between suppliers and customers are becoming increasingly fluid. In some cases, both may be joint controllers under data protection laws. This shared liability is essential to have clear terms and conditions of violation notification, minimum security standards and liability in the event of a data leak. It also calls for a joint spirit in risk assessment and incident response. This is especially true of high-risk AI deployments subject to the EU AI Act.
As AI's global regulatory regime begins to diverge, the risk of a cross-border compliance gap is rising between the strict EU AI laws, the UK's more principled approach, and the slower regimes in the US or Asia.
Accessing AI expertise can be challenging for small businesses. But resources exist. The ICO and the European Data Protection Commission (EDPB) regularly publish guidance. Government-supported tools are also under development, such as the UK's AI management essentials. What's important is that businesses are currently using these tools, not after the incident.
Conclusion: Sharing System, Sharing Responsibility
AI is more than just another IT tool. It is a complex and adaptive system that shapes the way data is collected, processed, and potentially leaked. Businesses and their suppliers need to work together to protect privacy and maintain trust from sourcing to post-deployment.
The UK and the EU are leading AI-specific data regulations, but globally operating companies must also track new laws in the US (such as state-level privacy bills) and the Asia-Pacific region where AI deployments are accelerating under various legal frameworks.
In a fragmented global AI environment, smart businesses do not wait for violations. Build cross-border resilience before innovation exceeds responsibility.
