AI
AI Adoption at Scale: Visibility is the First Line of Defence 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 A I. 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
Prakash Mana CEO
cloudbrink.com
...JPMorgan suspended internal chatbot access after employees uploaded sensitive client data into ChatGPT...
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