This article first appeared in UC Advanced magazine issue #23.
Across industries, enterprises are scaling AI at record speed; automating workflows, enhancing customer experiences, and making decisions once left to humans. Yet as adoption accelerates, governance and visibility are falling dangerously behind. Prakash Mana, CEO of Cloudbrink, discusses the hidden costs.
The hidden costs of AI aren’t about compute power or budgets alone. They’re operational, ethical, and tied directly to security. Once algorithms are embedded across departments – analysing data, generating content, or supporting decisions – organisations often lose sight of one crucial question: Who is using AI, how, and where is the data going?
Without that clarity, risk seeps into every layer of the enterprise. In 2025, as AI moves from experimentation to infrastructure, visibility must become the first line of defense – the foundation that determines whether innovation remains an asset or becomes a liability.
When Hype Hides Real Risk
AI’s promise of efficiency, creativity, and speed dominates boardrooms, yet recent incidents show how invisible use can quickly spiral into crisis. In 2023, JPMorgan suspended internal chatbot access after employees uploaded sensitive client data into ChatGPT, proof that even highly regulated enterprises can slip. Meta AI faced GDPR scrutiny for its advertising practices, showing how unclear data lineage can become a compliance nightmare. And the CrowdStrike update that caused global flight delays reminded everyone that a single unmonitored system update can halt entire industries. These events underscore a shared truth: you can’t govern what you can’t see.
When unapproved tools connect through browser extensions or APIs, they quietly siphon data into external systems. When AI models make autonomous decisions without validation, they introduce instability. And when outputs can’t be traced to specific datasets or logic, compliance quickly erodes. Most companies only realise these gaps after a breach or audit; that’s when remediation is costly and trust is already damaged.
The Rise and Reach of Shadow AI
A decade ago, IT teams worried about “Shadow IT”, employees downloading unapproved apps. Today, that’s evolved into something far more complex: Shadow AI.
Across enterprises, employees now use chatbots, generative tools, and AI copilots to speed up work without security review. Marketers draft copy through public models, analysts query live data with AI scripts, and developers test code using third-party copilots. None of this is malicious; it’s driven by convenience, but every unvetted AI connection expands the organisation’s risk surface.
The challenge of Shadow AI is threefold. First, adoption happens quickly. Many tools require no installation, and a single browser plug-in or “free trial” API can start processing company data instantly. Second, these tools hide in plain sight, blending seamlessly into everyday workflows and making detection difficult even for advanced monitoring systems. Third, accountability becomes unclear. When an AI-generated financial projection leads to a costly decision, who is responsible: the user, the IT team, or the model provider?
Why Visibility Must Come First
Before enterprises can enforce Zero Trust or draft governance frameworks, they need sightlines. Visibility is the precondition for every safeguard.
That means understanding normal versus abnormal behavior; what APIs connect to external models, where data moves for training, and when model output shifts unexpectedly. For example, an AI-powered procurement tool that begins generating purchase orders outside business hours might not seem dangerous, but it could signal unauthorised automation or even model tampering.
Visibility turns AI from a black box into an auditable system; one where every interaction, dataset, and model decision can be traced.
From Security Metric to Business Imperative
Visibility is quickly becoming more than a technical metric. It’s a board-level measure of control and trust. Regulators are tightening frameworks such as the EU AI Act and the evolving U.S. privacy laws. Investors are now assessing companies on AI governance readiness. And customers increasingly expect transparency in how their data interacts with intelligent systems.
Enterprises that can trace AI activity don’t just avoid penalties – they earn credibility. They can explain how every automated decision was made, which data was used, and why the output can be trusted.
What Enterprises Need to Do Now
1 Map your AI footprint. Create an inventory of all tools, APIs, and models – approved or not – that interact with enterprise data.
2 Monitor continuously. Track data movement across endpoints, edge devices, and clouds to identify anomalies in real time.
3 Integrate AI governance into Zero Trust. Treat AI models as identities within your access framework, validating both users and the AI agents acting on their behalf.
4 Build awareness. Train employees to recognise red flags and report unauthorised AI use as they would a phishing attempt.
AI oversight cannot remain solely within IT. It must extend across departments so that CISOs and data-governance leads define guardrails; legal and compliance teams interpret regulatory impact; and department heads enforce responsible use within their teams.
When visibility becomes a shared responsibility, accountability follows naturally.
Seeing Is Securing
AI is the backbone of modern transformation, but as organisations embed it deeper into operations, the risks multiply unseen. Visibility is how they’ll protect data, uphold compliance, and maintain trust in every automated decision.
In an era defined by intelligent systems, seeing is securing – and visibility is what separates responsible innovation from reckless acceleration.
