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AI and Privacy: What You Need to Know in 2026
The rules around AI and your data have shifted considerably in the past two years. Here is where things stand.
Two years ago, the conversation about AI and privacy was mostly theoretical. People worried about what might happen when AI systems had deep access to personal and professional data. In 2026, those concerns are no longer hypothetical. AI assistants read your emails, summarise your meetings, organise your files, and draft responses on your behalf. Agents book travel, manage calendars, and interact with third-party services using your credentials. The surface area for data exposure has grown enormously, and the regulatory landscape is still catching up.
This piece lays out what AI tools collect, how they use it, what the law currently requires, and what you should look for when choosing tools that handle your information.
What AI tools collect and how they use it
Most AI-powered tools process your data in two distinct phases, and the difference between them matters more than any marketing claim.
The first phase is inference. When you ask an AI assistant a question or feed it a document, it processes that input to generate a response. In many cases, the data you provide during inference is held only temporarily, discarded after the response is delivered. This is the phase most users think about, and it is also the phase where privacy risks are often manageable.
The second phase is training. Some providers use the data you submit during inference to improve their models. Your prompts, your documents, your queries become part of a dataset that shapes how the model behaves for every future user. This is where things get complicated. Training on user data means your information can, in principle, influence outputs served to strangers. It also means that data persists in ways that are difficult to audit or reverse.
The distinction between opt-in and opt-out training is critical. Some providers train on your data by default and require you to find a setting buried in an account page to stop it. Others never train on user data at all. A smaller number offer explicit opt-in, letting you choose to contribute data if you wish. The default matters because most people never change it.
Context windows present a related concern. Modern models accept increasingly large inputs, sometimes hundreds of thousands of tokens in a single session. When you paste a confidential contract into a context window, you are trusting not just the model but the entire infrastructure around it: the API layer, the logging systems, the monitoring tools, and whatever data retention policies the provider has in place.
The regulatory landscape in 2026
The legal framework around AI and privacy has developed rapidly, though unevenly.
The EU AI Act, which entered full application in stages from 2024 onwards, now imposes concrete obligations on providers of general-purpose AI systems. High-risk AI systems face transparency requirements, mandatory risk assessments, and rules about the data used for training. The Act works alongside the GDPR, which continues to be the most significant privacy regulation in the world. GDPR enforcement around AI has sharpened: data protection authorities in several EU member states have issued fines and orders related to AI training on personal data without adequate legal basis. The principle is clear, even if application varies by jurisdiction. Processing personal data for AI training requires a lawful basis, and legitimate interest claims are under increasing scrutiny.
In the United States, the federal landscape remains fragmented. There is no comprehensive federal privacy law equivalent to the GDPR. Instead, a patchwork of state-level legislation has emerged. California's CCPA and CPRA continue to set the pace, with provisions that cover automated decision-making and data minimisation. Texas, Virginia, Colorado, Connecticut, and more than a dozen other states now have their own privacy statutes, each with slightly different scopes, exemptions, and enforcement mechanisms. For businesses operating across multiple states, compliance is a genuine logistical challenge.
The UK has taken a somewhat different path, seeking to balance innovation with protection. The UK's data protection framework, built on the UK GDPR and the Data Protection Act 2018, applies to AI systems much as it does to any other form of data processing. The Information Commissioner's Office has published guidance on AI and data protection that emphasises transparency, fairness, and accountability. The approach is less prescriptive than the EU AI Act but still substantive.
Internationally, Brazil's LGPD, Japan's APPI, and similar frameworks in South Korea, India, and elsewhere are evolving to address AI-specific concerns. The direction of travel is consistent: more regulation, more specificity, and more enforcement.
What has changed since 2024
Several shifts make the privacy picture in 2026 meaningfully different from what it looked like two years ago.
First, agents have proliferated. In 2024, most AI interactions were conversational: you typed a prompt, you got a response. In 2026, AI agents operate with greater autonomy. They access your email, your file storage, your project management tools, your CRM. Each connection creates a new pathway for data to flow, and each pathway introduces the possibility of leakage or misuse. An agent that books a restaurant has access to your location, your dietary preferences, and your calendar. An agent that drafts client emails has access to your correspondence history. The more capable the agent, the more data it touches.
Second, more models are trained on user data than before. As competition in the AI space has intensified, some providers have quietly expanded their training data policies. Free tiers are particularly likely to use your data for model improvement. The economics are simple: if you are not paying, your data is part of the product.
Third, awareness has grown. Data breaches involving AI systems have made headlines. Researchers have demonstrated prompt injection attacks, where malicious instructions hidden in documents can manipulate AI agents into exfiltrating data. Context window poisoning, where an attacker places harmful content in a shared document that an AI later processes, is a recognised attack vector. These are no longer theoretical risks.
Practical risks to understand
Several categories of risk deserve attention from anyone using AI tools for work or personal purposes.
Data leakage through prompts is perhaps the most common concern. When you paste sensitive information into an AI tool, you are transmitting it to a server. If that server logs inputs, retains them for training, or is subject to a breach, your data is exposed. This applies equally to trade secrets, personal health information, financial records, and any other category of sensitive data. The risk is compounded when employees use consumer-grade AI tools for work tasks without organisational oversight.
Agent proliferation introduces systemic risk. Each AI agent that connects to a service requires credentials or API tokens. Each connection is a potential point of failure. If an agent's credentials are compromised, the attacker gains access to whatever the agent could access. The principle of least privilege, giving each agent only the minimum access it needs, is sound in theory but difficult to enforce when agents are designed to be helpful and broadly capable.
Third-party data sharing is another area of concern. Some AI tools send your data to subsidiary services for processing. An AI assistant might use one model for text generation, another for image analysis, and a third for speech recognition. Each service may have its own data retention and training policies. The chain of custody for your data can become opaque very quickly.
Finally, there is the question of data residency. Where is your data stored? Which jurisdiction's laws apply? For organisations subject to regulatory requirements around data sovereignty, these questions are not optional. An AI tool that processes data on servers in a jurisdiction with weaker privacy protections may create compliance problems regardless of what the provider's privacy policy says.
What to look for in AI tools you choose
Given these realities, a few criteria are worth evaluating when selecting AI tools for any purpose where data sensitivity matters.
Clear data policies come first. A provider's privacy policy should state, in plain language, whether your data is used for training, how long it is retained, who has access to it, and under what circumstances it might be shared with third parties. If the policy is vague or difficult to find, that is informative.
A commitment to not training on user data is a meaningful differentiator. Some providers make this commitment explicitly and back it with technical architecture. This is the approach taken by Fabric, where the AI assistant works across your files without using your data for model training. Your documents, notes, and files remain yours. The AI processes them to answer your questions and surface information, but the data does not leave your control or feed into a training pipeline.
Encryption matters, both in transit and at rest. Data should be encrypted when it moves between your device and the provider's servers, and it should be encrypted when it sits on those servers. End-to-end encryption, where the provider cannot read your data even if compelled, is the strongest form of protection, though it limits what server-side AI processing can do. Fabric's approach to privacy and security includes encryption and a design philosophy that keeps your data under your control.
Portability is often overlooked. Can you export your data from the tool? Can you do so in a standard format? If a provider makes it difficult to leave, your data is, in effect, held hostage. The ability to move your information freely is both a practical concern and a signal of the provider's attitude toward your autonomy. Tools that support connections to existing storage services like Google Drive and Dropbox give you flexibility without forcing migration.
Transparency about sub-processors and third-party services is important. If your data passes through multiple services during processing, you should be able to find out which ones and what policies govern each.
Audit logs and access controls matter for organisational use. You should be able to see who accessed what, and to restrict access based on roles or teams. Collaboration features should not come at the expense of access control.
The broader picture
Privacy in the context of AI is not a binary state. It exists on a spectrum, and the appropriate level of caution depends on the sensitivity of the data involved. A personal recipe collection does not need the same protections as a set of client contracts or medical records.
What has changed is the scale of exposure. AI tools are more capable, more integrated, and more numerous than they were even two years ago. They touch more of your data, more often, across more services. The convenience they offer is real, but so is the responsibility to understand what happens to your information once it enters an AI system.
Choosing tools that respect your data is not about being paranoid. It is about being informed. The best tools in this space are the ones that give you capability without requiring you to surrender control. They process your data to serve you, not to build a product that serves someone else.
Fabric is one example of this approach: a workspace where your files live in a cloud drive that streams on demand, where search understands what you mean rather than just matching filenames, where AI agents work on your behalf within your data, and where the entire system is built around the principle that your information belongs to you. Even the environmental programme, planting mangrove trees with every subscription, reflects a broader ethic of responsibility.
The regulatory environment will continue to evolve. New rules will emerge, existing rules will be tested, and enforcement will become more sophisticated. But regulation alone will not solve the problem. The most durable form of privacy protection is architectural: tools designed from the ground up to minimise data exposure, maximise user control, and operate transparently. That is what worth looking for, in 2026 and beyond.
Frequently asked questions
Do AI tools use my data to train their models?
Some do, some do not. Many AI providers use data submitted during inference to improve their models, particularly on free tiers. Others, including Fabric, commit to never training on user data. Check the provider's privacy policy for explicit statements about training data practices.
What is the difference between inference and training in AI?
Inference is when an AI model processes your input to generate a response. Training is when your data is used to improve the model itself. Privacy risks differ significantly between the two: inference data may be temporarily processed and discarded, while training data becomes part of the model's knowledge base.
How does the EU AI Act affect AI privacy?
The EU AI Act imposes transparency requirements, risk assessments, and data governance obligations on providers of AI systems. High-risk AI applications face the strictest requirements. The Act works alongside the GDPR to regulate how personal data is used in AI training and deployment.
Are there US federal AI privacy laws?
As of 2026, there is no comprehensive federal AI privacy law in the United States. Privacy is regulated through a patchwork of state laws, including California's CCPA/CPRA, along with sector-specific federal regulations. Several federal proposals have been introduced but none has passed.
What is prompt injection and why does it matter for privacy?
Prompt injection is an attack where malicious instructions are hidden in content that an AI system processes. If an AI agent reads a document containing hidden instructions, it could be manipulated into revealing sensitive information or performing unintended actions. It is a recognised security risk in AI systems that process untrusted input.
How can I tell if an AI tool is safe to use with sensitive data?
Look for clear data policies, explicit commitments about training data, encryption in transit and at rest, data portability options, and transparency about third-party sub-processors. Tools like Fabric that are designed with privacy as a core principle provide a higher level of assurance.
What is data residency and why does it matter for AI?
Data residency refers to the physical location where your data is stored and processed. Different jurisdictions have different privacy laws, so where your data sits determines which legal protections apply. This is particularly important for organisations subject to regulatory requirements around data sovereignty.
Can AI agents create new privacy risks?
Yes. AI agents that connect to your email, file storage, calendar, and other services create multiple pathways for data exposure. Each connection requires credentials, and each credential is a potential point of compromise. The more services an agent accesses, the larger the attack surface.
What should organisations do to manage AI privacy risks?
Organisations should establish clear policies about which AI tools are approved for use, what types of data can be processed through them, and what access controls are in place. Regular audits of AI tool usage, employee training on data handling, and a preference for tools with strong privacy architectures are all advisable.
Is it possible to use AI productively without sacrificing privacy?
Yes. Privacy and capability are not inherently in tension. Tools that process your data locally or within a controlled environment, that do not train on your data, and that give you full control over your information can deliver the benefits of AI without the privacy trade-offs. Fabric's AI assistant is designed to work this way.
