Ensuring data security when using AI

Enabling data security when using AI solutions

From personalized product recommendations to powering autonomous vehicles, Artificial intelligence is rapidly transforming industries, powering innovations, and shaping our daily lives. But AI isn’t as self-sufficient as it seems, and its capabilities are only as good as its access to the data.

As Generative AI systems become more sophisticated and data-hungry, they rely on vast amounts of sensitive unstructured data, from customer data to internal reports, to function effectively. This increases the risk of misuse, exposure, or breaches, making robust data security not just important, but essential for safe and responsible AI adoption.

Recent surveys show that 96% of organizations are building governance for generative AI, and 82% worry about data leakage from these tools. For business leaders, the question is how to adopt AI tools into their business processes without exposing corporate secrets, confidential data, or customer information.

How AI solutions can compromise data security

Ensuring data security when using AI isn’t just about preventing breaches, it’s about understanding how sensitive information can unintentionally leak through everyday use. As AI becomes embedded into business workflows, risks arise not only during development and deployment, but also in how employees interact with these tools and how AI systems are structured to learn and respond.

For instance, an employee may paste confidential figures, customer information, or source code into a generative AI tool to get a quick analysis, unaware that this data could be stored, processed, or reused beyond their control. Meanwhile, AI models themselves can sometimes retain and reproduce fragments of sensitive training data. These aren’t edge cases, they’re systemic risks born from normal use in the absence of proper safeguards.

Unlike traditional cyberattacks, these leaks often happen without anyone realizing it. A recent Gartner report highlights that, without strict protocols, AI chatbots can inadvertently expose private data stored in enterprise systems. That’s why securing AI starts with embedding clear policies, access controls, and monitoring into how these tools are adopted across the organization.

Unlike traditional data security, which focuses on safeguarding static datasets, AI data security must address unique challenges:

• High data volume requirements: AI models rely on large-scale data, increasing the surface area for potential leaks.
• Unverified data sources: Data used by AI typically comes from multiple source, which are unverified sometimes, complicating security efforts.
• Adversarial threats: Malicious inputs can manipulate AI models, leading to incorrect outputs or compromised systems.
• Expanded attack surface: The complexity of AI systems, including models, APIs, and cloud infrastructure, creates more entry points for attackers.

Together, these factors demand a shift in how organizations approach data security when using AI. It involves robust strategies that ensure data integrity, confidentiality, and compliance with regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Real-world incidents, such as the exposure of 38TB of Microsoft data by AI researchers, highlight the stakes involved.