Your Data and The Hidden Dangers of Third-Party AI

Your Data and The Hidden Dangers of Third-Party AI

Your company's most sensitive data is being fed into AI systems right now, and you probably have no idea it's happening.

Artificial intelligence has quietly embedded itself into everyday business operations. From document summarization and customer support chatbots to analytics, compliance checks, and code generation, third-party AI tools promise speed, insight, and efficiency. Marketing teams use AI to draft campaigns. Legal departments lean on it to review contracts. Developers rely on it to debug code and generate boilerplate faster than ever before.

But behind the productivity gains lies a growing and often misunderstood risk: What happens to your data once it enters someone else's AI system?

For many organizations, especially small and mid-sized businesses, the answer is uncomfortable. They don't really know.

The AI Data Sponge

Traditional software processes inputs and returns outputs. AI systems, particularly large language models, do something fundamentally different. They learn. They adapt. And in many cases, they retain.

When you paste a customer email into a chatbot for tone analysis, upload a financial document for summarization, or feed proprietary code into a debugging tool, you're not just using a service. You're contributing to a training dataset. Many AI providers explicitly reserve the right to use customer inputs to improve their models, a practice buried deep in terms of service agreements that few people read and fewer understand.

Even when vendors claim data isn't used for training, the reality is more nuanced. Data may still be logged, cached, stored for quality assurance, or analysed for system improvements. It may be processed by subcontractors in jurisdictions with weaker privacy protections. In some cases, it may be inadvertently exposed through model behaviours like prompt injection attacks or unintended memorization, where sensitive information surfaces in responses to unrelated queries.

The distinction between "processing" and "training" has become a semantic smokescreen. For all practical purposes, your data is no longer just yours the moment it crosses into a third-party AI environment.

The Invisible and Continuous Third-Party Risk

Traditional third-party risk management was built around vendors with defined access points such as a CRM system, a payment processor, an email platform. You could map the data flows, assess the security controls, and audit the relationship periodically.

AI changes the equation entirely. The risk is no longer static; it's dynamic and evolving. Every interaction with an AI tool is a potential data exposure event. Employees across departments are integrating AI into workflows independently, often without IT oversight or approval. Shadow AI is the new shadow IT, except the stakes are higher.

Consider the typical scenarios playing out daily in organizations worldwide. A sales manager pastes a list of prospects into an AI tool to generate personalized outreach emails. A finance team uploads quarterly results to an analytics platform for narrative summaries. A product manager shares internal roadmaps with an AI assistant to draft stakeholder updates. Each of these actions, performed with the best intentions and in pursuit of legitimate business goals, represents a moment where sensitive data leaves the organization's control.

And unlike a one-time vendor onboarding, these interactions happen continuously, across dozens of tools, by hundreds of users, often without a centralized record of what data was shared, where it went, or under what terms.

Data Leakage Doesn't Always Look Like a Breach

When we think of data security incidents, we imagine hackers, ransomware, and leaked databases. But with AI, data exposure can be far more subtle and far more insidious.

There's no alarm when an employee accidentally shares confidential information with a chatbot. There's no breach notification when proprietary strategies are fed into a market analysis tool that later incorporates those insights into responses for competitors. There's no incident report when a model trained on your customer support tickets begins suggesting solutions derived from your unique processes to other users.

This is passive leakage, the gradual erosion of informational advantage and competitive differentiation. It doesn't trigger regulatory obligations because no unauthorized access occurred. It doesn't make headlines because there's no single catastrophic event. But over time, the cumulative effect can be devastating, especially for businesses whose value lies in proprietary knowledge, customer relationships, or operational know-how.

Even more concerning is the permanence of the exposure. Once data enters an AI system and influences its training, it cannot be fully retracted. There's no "undo" button for machine learning. Your confidential information may persist in model weights, embedded in ways that are technically impossible to extract or delete, even if you later terminate the vendor relationship.

The Compliance Bind Spot

The Compliance Blind Spot

Some nations are leading the charge in creating AI-specific regulatory frameworks. The European Union's AI Act, which came into effect in 2024, represents the world's first comprehensive AI regulation, incorporating risk-based classifications and stringent data privacy requirements. Countries including Canada, Brazil, and several others are in various stages of considering or drafting their own AI governance legislation, recognizing that traditional data protection laws alone are insufficient.

But for South African organizations, the regulatory landscape presents a particular challenge. While we wait for AI-specific frameworks to emerge locally, we're left navigating compliance using POPIA (Protection of Personal Information Act), a framework designed for traditional data processing relationships where data custodians and processors have clear roles and responsibilities. AI complicates this model in ways that existing compliance structures struggle to address.

Can you exercise the "right to deletion" under POPIA when your data has been diffused across billions of neural network parameters? How do you conduct a legitimate data processing impact assessment when you can't predict what an AI model will learn or how it will generalize from your inputs? What does "purpose limitation" mean when an AI system trained on customer service interactions might later be repurposed for product development or market research?

South African organizations find themselves in a compliance grey zone, caught between regulatory obligations that assume data can be tracked, controlled, and erased, and technical realities that make those assumptions increasingly fictional. Legal teams are scrambling to retrofit POPIA's framework onto new technologies, often settling for checkbox compliance that satisfies the letter of the law while missing the spirit entirely.

This gap creates real legal and reputational risk. When a data subject requests deletion of their personal information, can you confidently confirm it's been removed from all AI systems you've used? When the Information Regulator asks where customer data is processed and stored, can you account for every AI tool in your environment? In your supplier’s environment? For most organizations, the honest answer is no.

Why SMEs Are at Greater Risk

Large enterprises have dedicated privacy teams, AI governance committees, and the resources to negotiate custom data processing agreements with vendors. Small and mid-sized businesses typically have none of these advantages.

SMEs are more likely to rely on free or freemium AI tools with unfavourable terms. They're less likely to have policies governing AI use or training to help employees recognize data sensitivity. They often lack the technical expertise to evaluate vendor security practices or the bargaining power to demand better contract terms. And because they're moving fast to stay competitive, they're more prone to adopting tools quickly without thorough vetting.

This creates a perfect storm of vulnerability. The very organizations that can least afford a data exposure incident are the ones most likely to experience one, not through malice but through simple lack of awareness and resources.

The democratization of AI, while empowering in many ways, has also democratized risk. Tools that were once accessible only to sophisticated enterprises with robust governance are now available to anyone with a credit card and an internet connection, often with minimal guardrails and maximum convenience.

What Good AI Third-Party Risk Management Looks Like

Addressing this challenge requires a fundamental shift in how organizations think about AI adoption, moving from opportunistic experimentation to strategic risk management.

First, visibility is essential. Organizations need to know what AI tools are in use across the business, who's using them, and what data is being shared. This requires both technical controls (monitoring for unauthorized AI integrations, data loss prevention systems that recognize AI endpoints) and cultural change (encouraging employees to disclose AI use rather than hiding it).

Second, vendor assessment must evolve beyond traditional questionnaires. The relevant questions for AI providers are different: How is training data segregated? Can you guarantee data won't be used for model improvement? What happens to our data if we terminate the relationship? Do you use sub-processors, and where are they located? What technical safeguards prevent model memorization of sensitive inputs? Organizations should demand clarity in contracts, with specific provisions around data use, retention, and deletion.

Third, data classification becomes critical. Not all information requires the same level of protection, and blanket prohibitions on AI use are neither practical nor productive. Instead, organizations should categorize data by sensitivity and establish clear policies: publicly available information may be shared freely, internal business data requires approved tools with contractual protections, confidential or regulated data may be prohibited from AI processing entirely.

Fourth, user education must be ongoing. Employees need to understand not just the rules but the reasoning behind them. Training should cover real scenarios: what happens when you paste a customer complaint into ChatGPT, why uploading a strategic plan to a free summarization tool is problematic, how to recognize when data should stay internal. This isn't about policing behaviour; it's about building judgment.

Finally, organizations should consider technical solutions that allow them to benefit from AI while maintaining data control. Self-hosted or on-premises AI models, privacy-preserving techniques like differential privacy or federated learning, and tools that anonymize data before external processing can all reduce exposure while preserving functionality.

The promise of AI is real, and the competitive pressure to adopt it is intense. But in the rush to automate, optimize, and innovate, we risk outsourcing not just tasks but the very information that gives our organizations their unique value and competitive edge.

The question isn't whether to use AI. It's whether we're using it with our eyes open, understanding the true nature of the bargain we're making. Because once your data enters the machine, it becomes part of something larger, something beyond your control, something that doesn't forget.

And in a world where data is the new currency, that's a transaction worth scrutinizing carefully before you click "submit."