Can chatbots leak your data: Keeping AI systems safe – The Statesman
Chatbots powered by generative AI are designed to learn from interactions. The more users share, the more these systems improve—but also, the more they retain. What feels like a private exchange can quietly turn into a digital footprint, stored, processed, and potentially exposed.
Neehar Pathare | August 1, 2025 4:17 pm
Image Source: Freepik
A project deadline is approaching. A manager opens a chatbot to speed things up. He asks it to summarise a client brief, shares internal timelines and even types in pricing details to get a faster draft. The conversation flows smoothly, the work gets done, and the chat is closed.
But the data doesn’t disappear.
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Chatbots powered by generative AI are designed to learn from interactions. The more users share, the more these systems improve—but also, the more they retain. What feels like a private exchange can quietly turn into a digital footprint, stored, processed, and potentially exposed.
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Chatbots that remember more than they should
Unlike conventional digital tools, generative AI chatbots are built on large language models that rely on enormous volumes of data to function. These systems not only interpret language and identify context but also generate nuanced responses. By doing so, they often process highly specific, sometimes confidential, as well as sensitive inputs. From internal reports and business strategies to personal details and client records, these inputs cover all. Every interaction contributes to the model’s refinement, but it also expands the digital memory these systems carry forward.
The concern lies in what happens to that memory. When sensitive data is retained—whether temporarily or for model training—it creates opportunities for leakage. The more these systems are integrated into everyday workflows, the more likely it becomes that critical information is unintentionally exposed or stored in ways users are not completely aware of.
The expanding threat landscape
The sophistication of generative AI also increases its surface area for attack. Among the most prevalent concerns is prompt injection. In this, a malicious input is designed to manipulate a chatbot. Following this, it makes the chatbot perform unintended actions or reveal information – it was meant to withhold. Since chatbots respond contextually, it usually becomes challenging to differentiate between a harmless prompt and one crafted to deceive the system.
Moreover, model inference attacks present another significant risk. These attacks involve adversaries repeatedly. This, in turn, helps in querying the system to reverse-engineer or infer the underlying data it was trained on. If the model has been exposed to sensitive internal information, this could result in the indirect disclosure of proprietary or private data through seemingly innocuous conversations.
Risks within the walls
It must be noted that all threats are not external. In many cases, the risk arises from within the organisation itself. Employees may use chatbots for tasks that involve confidential customer data, strategic documents, or unreleased product information. These details, once entered into the chatbot, can become part of its memory. This especially happens in the case where data governance rules are not clearly defined.
Even in the absence of malicious intent, this internal misuse can lead to regulatory violations. When employee inputs are not evaluated or controlled, sensitive data can be inadvertently included in future model training or exposed through unforeseen chatbot responses. The result is a silent yet serious breakdown in information security.
Designing chatbots that are safe by default
Addressing the risks of AI chatbot usage requires a multi-layered approach—starting with how data is handled. First and foremost, information shared with a chatbot must be encrypted in transit and at rest. This ensures that even if conversations are intercepted or accessed, the data remains unreadable to unauthorised actors.
Authentication protocols must also be strengthened. Multi-factor authentication for users and strict access controls for administrators ensure that only authorised individuals can interact with or manage chatbot systems. This is particularly important when chatbots are integrated with broader enterprise tools like CRMs or knowledge bases.
Monitoring and auditing should become continuous processes. By logging chatbot interactions and flagging anomalies, organisations can detect suspicious behaviour early. Establishing review protocols for chatbot-generated responses can also prevent the accidental exposure of sensitive information, especially in customer-facing applications.
Embedding governance and awareness
Securing chatbots is not just a technical challenge. Instead, it’s a governance issue. Firms must form clear usage guidelines that define which types of information can be shared with chatbots and under what conditions. Training programs should equip employees with the knowledge to use AI tools responsibly, and cybersecurity teams must remain actively involved in overseeing how these tools are deployed and maintained.
Additionally, regular audits, policy updates, and internal awareness campaigns must be done proactively. This ensures that AI safety is considered a continuous responsibility. This cultural transition – from viewing chatbot security as an organisational priority to understanding the same as a backend task – is essential to keeping pace with evolving threats.
On the whole, as AI chatbots continue to redefine how people interact with technology, the focus must expand beyond speed and convenience. In addition, securing them is not about limiting progress—it is about protecting the intelligence being built. Privacy, security, and ethical design must be baked into every layer of chatbot development.
Because in the world of conversational AI, it’s not what the system says that poses the greatest risk. It’s what it remembers.
The writer is MD, CEO & CIO, 63SATS Cybertech.
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