Data is the fuel that powers AI tools. The success of any deployment is heavily dependent on the availability and quality of relevant information. Naturally, this shines a light on the importance of data management and infrastructure strategy (as we explored in our last blog).
However, this is not the whole story. Beyond considerations around infrastructure and management approach, areas such as security, governance and compliance are also central pillars of the AI foundation. With many organisations adopting a hybrid AI strategy – combining on-premises and edge deployments with cloud flexibility, they’re able to balance performance, cost, and compliance while keeping data governance at the forefront.
Without well-established and regularly updated data governance controls, you risk hampering the overall success of any deployment and, more crucially, potential exposure of sensitive and confidential information.
Understanding the data risk
As the adoption of mainstream generative AI tools has accelerated, a number of high-profile examples of the potential data risks have emerged. Many of these are the result of users sharing sensitive or internally-facing information (such as pre-release product details) with publicly available models like ChatGPT. This information would then be fed into the model’s underlying data set, and resurfaced to different users, exposing confidential material.
Most business-ready AI deployments have largely mitigated this particular risk, with controls in place to prevent user prompts from being used for training. However, “shadow AI” - unauthorised tools that users feed information into – can still pose a significant risk in this regard.
Even when authorised, AI assistants and similar tools can cause data governance issues. They’re designed to draw on vast organisational datasets, allowing authorised users to search through internal data siloes quickly and efficiently. While this capability is valuable for finding information in a hurry, it can pose a challenge to organisations that rely on “security through obscurity” for their data governance strategy - burying sensitive information in hard-to-reach places, rather than actively controlling access. Without proper access controls, AI systems can easily uncover and present this hidden data to users who would otherwise be unable to find it.
As a result, poor data governance, lacking user education, and unenforced AI usage policies can present significant security risks – making it critical to address them as part of any business-wide AI strategy.
Securing your AI – and the data that powers it
Any successful long-term AI deployment must ultimately be built around strong governance controls and compliant data policies. This is becoming more critical with the rise in hybrid AI, as organisations blend cloud, edge, and on-premises AI. Getting this right includes outlining not only what data is available to AI models, how they can be accessed, and by whom, but ensuring every user is informed as to how the organisation expects them to leverage AI responsibly.
While this might feel like a significant undertaking, organisations already operating with robust data governance will already be doing lots of right things. These simply need to be considered and re-applied in the context of AI.
As a first step, any data earmarked for AI usage should be properly classified, tagged, and protected, ensuring that access is restricted based on user roles and sensitivity levels. Moving from a reactive security through obscurity approach to proactive data governance controls provides real protection — and supports wider compliance efforts.
It’s also essential to establish clear access levels. Determine where your AI tool sits within your organisational hierarchy and what information it should be able to access at a base level. Most AI deployments allow users to directly upload information as part of a request or prompt, which means that even a tool with a limited underlying dataset can still help users process more sensitive data, so long as that user is able to provide it directly to the AI.
In some cases, it may be possible to configure your AI deployment to inherit a user’s access privileges. When combined with a well-implemented role-based access control (RBAC) system, this allows top-level executives to access information from across the organisation, while entry-level users are restricted only to datasets required for their role.
Limiting AI tools to a deliberately restricted access profile can also reduce exposure. Even if sensitive data is left inadvertently exposed, the AI won’t be able to access or share it. Lenovo’s high-performance infrastructure – optimised for reliability by NVIDIA – features prevalidated configurations, ThinkShield™ security, integrated governance, and AI-powered data orchestration. This helps you manage large scale AI deployments with confidence, efficiency, and speed, while tracking data provenance to flag restricted or anomalous material.
Building AI into your compliance strategy
A secure, compliant AI framework starts with accountability. To ensure that your deployment aligns with regulatory requirements like GDPR, every stage of your data lifecycle, from collection and training to storage and deletion, must be auditable and transparently managed, especially when it’s being utilised by AI.
Regular data audits play a critical role here. These should confirm that permissions, data labelling, and retention policies remain in tune with your data governance strategy and any applicable laws. Audits should also check that AI models are using approved datasets, and that their outputs can be traced back to verified and legitimate sources to ensure regulatory compliance.
Your AI compliance strategy should also include clearly defined data retention and deletion policies, supported by tools that automate these processes wherever possible. AI surfacing old data isn’t just a productivity barrier for users – it can represent serious issues with data governance. Lenovo XClarity One provides full audit trails, automated retention policies, and compliance dashboards, ensuring your AI environment remains aligned with regulations like GDPR.
By embedding compliance and auditing directly into your data architecture, you create an AI governance model that can scale with evolving regulations and new technologies - protecting both your organisation and your customers, while still giving your users the benefit of AI-enabled productivity boosts.
With Lenovo Hybrid AI Advantage™ with NVIDIA, you can make smarter, more informed decisions with AI and move forward with confidence. Every solution in the portfolio is built on Lenovo’s Responsible AI framework, with integrated security and governance designed for robust protection, privacy, and data sovereignty. The result: AI that is tailored, secure, and scalable, empowering you to improve agility, drive productivity, and accelerate innovation.
Build your roadmap with an Ideation Workshop
Tackling data governance challenges ahead of AI adoption can feel like a significant deployment barrier, but this isn’t the case if you have the right expert guidance to hand. That’s why we’re working with experts from Lenovo and NVIDIA to deliver our AI Ideation Workshops. Delivered as a set of comprehensive workshops, we help you build a bulletproof AI strategy, ensuring your environment has all the necessary groundwork in place, including data governance, and helping you pilot your first deployment to a key use case that’s valuable for your users.
Book your Ideation Workshop today to start building a data governance strategy that protects your organisation, supports compliance, and powers the next generation of AI innovation.