When ChatGPT launched in late 2022, the rush was immediate. Millions of people discovered they could automate tedious tasks, draft emails, debug code, and summarise documents in seconds. Many brought these tools into their jobs without asking permission. Why wouldn’t they? The technology was free, fast, and transformative. What they didn’t consider were the privacy implications, or that alternatives like private AI platforms even existed.
Then reality hit. Remember when, in 2023, Samsung employees accidentally leaked top secret data whilst using ChatGPT to help optimise their code and transcribe meetings? The sensitive information they’d pasted into prompts was now sitting on OpenAI’s servers, potentially being used to train future models.
Turns out “move fast and break things” hits differently when the things you’re “breaking” are company secrets. Unsurprisingly, Samsung banned ChatGPT immediately.
The incident exposed a critical gap that persists to this day. As AI adoption accelerates, employees continue using unsanctioned tools to get work done faster. This unauthorised use of AI tools outside IT oversight is called “shadow AI”.
So, what exactly is shadow AI?
Shadow AI refers to any AI tool or service that employees use without formal approval from IT, security, or leadership. It’s the AI equivalent of “shadow IT”, where workers adopt software that hasn’t been properly vetted, secured, or integrated into company systems.
This can take many forms. For example:
- A marketing team member might use a free AI writing assistant to draft social media posts containing unreleased product details.
- A developer could paste proprietary code into a public AI coding tool to debug faster.
- Finance staff might upload customer data to an AI spreadsheet tool for analysis.
- HR could use an unapproved chatbot to screen CVs containing personal information.
According to a 2025 report from Reco, small businesses face the highest shadow AI risk, with 27% of employees in companies with 11-50 workers using unapproved tools. And for businesses serious about AI data protection, it’s becoming one of the most urgent challenges to address.
The common thread is convenience trumping caution. These tools often deliver real value and help people work faster. But because they operate outside official channels, organisations lose visibility over what data is being shared, where it’s going, and how it’s being used.
That invisibility – combined with employee enthusiasm – is precisely what makes shadow AI so risky.
Luke Budka, our AI Director, hits the nail on the head:
“Workers at more than 90% of companies are using personal chatbot accounts for daily tasks, while only 40% of companies have official LLM subscriptions. This is because your colleagues are very good at working out innovative uses for the tech, don’t want to delay in the face of increasing AI pressure on their livelihoods and want to use the models that work for them (the tone of voice and interaction patterns are enough to push an employee from OpenAI to Gemini or vice versa for example).”
What are the potential risks and consequences of shadow AI?
Shadow AI creates exposure across three critical dimensions: security, operations, and business and financial continuity, including challenges like data leaks, compliance violations, bad code reaching production, and hidden costs that only surface when the damage is done.
Let’s take a deeper dive into what organisations risk when AI usage goes unmanaged.
1. Security and privacy risks
The most immediate dangers relate to security and privacy: what happens to your data once it leaves your control?
Data leakage and unauthorised storage
When employees upload sensitive information into unapproved AI tools, that data often gets stored on third-party servers. Worse, some platforms explicitly use customer inputs to train their models, meaning your company information could end up improving a competitor’s results. Personally identifiable information, trade secrets, customer data, and financial records. All of it can leak through AI prompts.
The Samsung case isn’t isolated. In the same year, Amazon warned employees to stop using ChatGPT after discovering engineers were sharing confidential code.
More recently, in February 2026, the European Parliament turned off built-in AI features on employee devices entirely, citing cybersecurity and data protection concerns. Their IT department couldn’t guarantee the security of AI tools that rely on cloud services sending data off-device, instead of processing locally.
Autonomy can also compound risk, which is why agentic AI is of more immediate concern. As Luke adds:
“If your employees are connecting agentic models with autonomy and read/write data access, computer use, and the ability to conduct web searches, to your servers, then your risk of data loss and cyber-attacks soar.”
Loss of access controls and audit trails
Shadow AI eliminates basic security hygiene. There’s no access control determining who can use public AI tools. No logging to track what prompts were submitted or what outputs were generated. No monitoring to catch suspicious activity.
So, when an AI-generated document containing confidential data gets shared externally via email or Slack, there’s often no way to trace how it happened.
Regulatory non-compliance
If you’re subject to GDPR or other data protection regulations, using public AI tools can put you in direct violation. These platforms may be hosted in foreign jurisdictions with different data laws, meaning you lose control over where your data lives and who can access it.
Remember: regulators don’t care that your employee was just trying to work faster! As the data controller, you’re still liable.
2. Technological and operational risks
Beyond security threats, shadow AI introduces both technical debt and operational headaches.
Operational fragility and vendor dependency
If someone integrates shadow AI into a critical business process and that tool suddenly goes down, changes its API, or gets discontinued, you face outages with no backup plan. There’s no SLA, no incident response, and no support contract.
Version drift and inconsistency
Everyone has their preferred AI model. Perhaps you’re a GPT groupie. Or maybe a Sonnet superfan. But different teams using different models lead to conflicting results. One department gets different answers than another because they’re using different tools with different training data.
That also means you can’t reproduce decisions or outputs because there’s no audit trail and limited visibility into who has used which models, for what.
3. Financial and business risks
The bottom-line impact of shadow AI often surprises organisations that thought they were just dealing with a technical problem.
Hidden and uncontrolled costs
Even when employees use free AI tools, costs accumulate in less obvious ways, such as productivity losses from chasing down data breaches or compliance issues, or legal fees from contract violations or regulatory investigations.
And when shadow AI does involve paid tools, rogue SaaS subscriptions can multiply across departments without oversight from your procurement team, while some employees may expense API costs that fly under the radar until finance spots the pattern.
Reputational damage
A data leak traced back to shadow AI undermines customer trust. Biased or harmful AI outputs published under your brand create a PR crisis. It’s really that simple: the market punishes companies that can’t demonstrate responsible AI use.
Loss of competitive advantage
If your company strategies, product plans, or customer insights leak into external AI models through shadow AI use, you’ve essentially handed your competitive advantage to anyone who can access those same public platforms.
“Sadly, AI labs use data inputs to train their models (unless you’re accessing them in certain ways, via their APIs for example). Even expensive subscriptions, like Anthropic’s $100 per month Max account, fall victim to this,” says Luke.
“How dangerous is it to let AI models train on your confidential data? Well, at a minimum, you’re breaching the contractual terms you’ve agreed with your customers, and you’re also augmenting the model’s knowledge with your IP. So, in future, anyone can ask a model for details you don’t want to be shared.”
Fragmented AI adoption and reduced ROI
When every team uses different unapproved tools, you simply can’t build coherent AI privacy and governance policies, centralise learnings, or implement transformation programmes that move the business forward.
How to assess your shadow AI risk
Before you can solve a shadow AI problem, you need to understand if you have one. Most organisations do; they just don’t know the extent yet.
Here’s a quick checklist of questions to ask:
1. Start with visibility
Do you know which AI tools your teams are actually using? Not which ones are approved, but which ones are being used right now. Can you see what data is being shared with these tools? If someone asked you to produce an audit trail of all AI interactions from the past month, could you do it?
2. Ask about controls
Who has the authority to approve new AI tools? Is there a process for vetting them, or do employees just sign up and start using whatever they find? Are there technical controls preventing sensitive data from leaving your environment, or is it based on trust and policy documents that nobody reads?
3. Consider your data classification
Do employees know what’s considered sensitive? Can they easily identify which information should never go into an external tool? If your data isn’t clearly labelled and your team isn’t trained, shadow AI risk multiplies.
4. Look at tool adoption
Are your approved tools slow, difficult to access, or missing key features? If the official route is painful and the shadow route is effortless, you’re essentially pushing people towards risky behaviour. The gap between what employees need and what they’re allowed to use is where shadow AI thrives.
The solution: what is a private AI environment?
A private AI environment is a controlled infrastructure where organisations can use AI models without exposing their data to public platforms. Unlike public AI tools where your prompts and data may be stored, shared, or used for training, a private AI platform keeps everything encrypted and within your security perimeter.
Think of it as the difference between shouting your questions in a crowded room versus having a confidential conversation in a quiet corner. Private AI gives you the power of generative AI without surrendering control over your information.
These environments typically run on dedicated infrastructure, either on-premises or in isolated cloud instances. You control access, you monitor usage, you own the audit trails. Most importantly, your sensitive information isn’t being fed into models that others can access or that vendors can use for their own purposes.
You’re probably thinking that sounds quite complex. Not at all. Private AI doesn’t mean building your models from scratch. Most organisations use private gen AI platforms that provide direct API access to one or more leading models (GPT, Claude, Gemini, etc.) but route everything through secured infrastructure with proper data handling guarantees.
In short: with private AI, you get the capabilities of cutting-edge models with the governance and data protection your business requires.
Public vs private AI platforms compared
The trade-offs between public and private AI ultimately come down to control versus convenience.
| Aspect | Public AI platforms | Private AI platforms |
| Setup | Immediate – sign up and start using | Depends – more or less immediate if using a SaaS platform like Definition. Potentially more setup if you’re hosting something internally. |
| Cost | Free or low-cost for basic usage | Higher upfront investment, longer term savings |
| Data control | Data leaves your environment, may be used for training | Data not used for training |
| Compliance | Limited guarantees, subject to provider terms | Full audit trails, configurable to meet regulatory requirements |
| Updates | Automatic, no control over timing | You control which models to deploy and when |
| Visibility | Minimal insight into data handling | Complete monitoring and logging of all interactions |
| Security | Shared infrastructure, provider-managed | Depends on deployment: self-hosted (you control security) or managed SaaS (provider controls security with your visibility) |
| Best for | Individual use, non-sensitive tasks | Regulated industries, sensitive data, enterprise governance |
For organisations handling sensitive data or operating in regulated industries, the choice becomes crystal clear. You can’t build a compliant, secure AI strategy on tools designed for consumer convenience.
Definition AI: a multi-model private AI environment
Not all private AI platforms are created equal. Here’s how Definition AI approaches the security features and capabilities that matter for a private AI environment.
Your data stays yours
Definition AI doesn’t process your data beyond what’s necessary to generate responses, never sends personally identifiable information to AI vendors, and deletes all your account data on request.
Encryption that works
Definition AI encrypts all data using AES-256 encryption and TLS protocols, with AWS Key Management handling encryption keys, and logically and physically segments all client data within a private cloud environment.
Control who gets in
Definition AI offers secure login with Google or Microsoft Single Sign-On, secures all API endpoints using OAuth2, and maintains rigorous identity and access management policies to control who can access systems.
Fortress-level infrastructure
Definition AI operates within an AWS Virtual Private Cloud (VPC), providing network-level isolation from other workloads and the public internet.
Security testing that goes deep
Definition AI conducts regular SAST and DAST testing in staging environments, performs code reviews focused on the OWASP Top Ten, and runs dependency checks to scan all third-party libraries for vulnerabilities.
Fast response when threats emerge
Definition AI maintains a two-hour threat SLA with solutions implemented within one business day and holds Cyber Essentials Plus certification through annual independent audits by the UK’s National Cyber Security Centre.
All the models you need in one secure place
Definition AI provides multi-model access to all leading AI models within a single secured environment, eliminating the gap between approved tools and actual needs that drives employees toward shadow AI.
This crushes the primary driver of shadow AI: the gap between approved tools and actual needs. Boom. Mic drop.
Let’s end on a thought from Luke:
“We know if you enable employees with AI that’s better than what they pay for and you make it more useful for them, then they will use it in place of personal subscriptions. And they will fly with it, working out new ways to do things better and faster. Redesigning age-old workflows and supercharging their outputs. And if you can sandbox the AI they use, then you can also control the risks.”
Check out Definition AI in action
Want to learn more about Definition AI? Check out our pricing and security information or, better yet, why not book a demo today?
Written by Senior PR and SEO Strategist, Matthew Robinson.
FAQs
How much do private AI platforms cost?
Private AI platform pricing varies significantly depending on the provider and model. Enterprise solutions like Microsoft Copilot require existing Microsoft 365 licenses plus per-user fees, whilst ChatGPT Enterprise typically demands custom contracts with minimum seat counts (often 150+ users), leading to annual costs exceeding £100,000. These arrangements also often hide additional expenses for system integration, data preparation, and metered usage that can inflate total costs by two to three times the initial price.
Definition AI takes a different approach with modular, usage-based pricing. Instead of charging per seat, the platform uses a one-off setup fee (£1,500 for up to 20 users, £7,500 for up to 1,000 users) followed by monthly hosting fees starting at £250–£500, plus variable costs based on actual AI consumption. This pay-as-you-go structure lets organisations give many employees access without paying for each individual user, making it more cost-effective than traditional subscription models whilst maintaining the security of a private AI environment.
See how much you could save using our Definition AI price comparison calculator.
Can employees still use their preferred AI models in a private AI environment?
Yes. One of the key advantages of platforms like Definition AI is multi-model access within a single secure environment.
Employees can switch between different models (GPT, Claude, Gemini, Luma, Veo, etc.) depending on their specific task requirements, all whilst staying within your organisation’s security perimeter. This flexibility actually reduces shadow AI risk because staff aren’t forced to seek out unauthorised tools to access the capabilities they need.
Do private AI platforms work for small businesses, or are they only for enterprises?
Private AI platforms are increasingly accessible to small and medium-sized businesses. Whilst enterprise solutions from major vendors often require minimum seat counts and six-figure commitments, newer platforms offer scaled pricing that make more sense for smaller teams.
What happens to data that’s already been shared with public AI platforms?
Unfortunately, once data has been submitted to public AI platforms, you can’t retrieve it. Some providers allow you to delete your account and request data removal, but there’s no guarantee that information hasn’t already been used for model training or cached in ways that persist.
This is why moving to a private AI environment is about preventing future exposure rather than reversing past incidents. If you’ve identified a serious data leak through shadow AI, you may need to treat it as a security incident requiring breach notification, depending on the sensitivity of the information and applicable regulations.
