The Copilot Problem: Why Internal AI Assistants Are Becoming Accidental Data Breach Engines
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The Copilot Problem: Why Internal AI Assistants Are Becoming Accidental Data Breach Engines
"The breach didn't look like a breach. An employee asked an internal copilot a routine question. The answer was accurate, efficient yet disturbing. It referenced emails, legacy files and internal records the user didn't know still existed. No system was hacked. No policy was violated. No one asked the AI to do anything improper. What failed wasn't intent or behaviour, rather it was visibility."
"Third, inference and combine. Copilots connect fragments across systems. Sensitive context can emerge even when no single document is labelled sensitive. Once access exists, intent is irrelevant. If a system can see data, it can surface it. If it can surface it, it can expose it. If exposure occurs, governance has already failed, quietly. This is why internal copilots should be treated less like knowledgeable colleagues and more like unsupervised children. Anything not explicitly blocked should be assumed reachable. Barri"
An internal copilot answered a routine query by surfacing emails, legacy files and internal records the user did not know existed, revealing a visibility problem rather than a breach. Internal copilots function as interfaces over enterprise search, identity and permission systems and retrieve, connect and combine information the organization has already made accessible. Permission inheritance, pre-indexed retrieval across diverse repositories, and inference that links fragments can reveal sensitive context even when no single item is labelled sensitive. When access exists, intent is irrelevant and surfaced information can be exposed. Organizations must assume anything not explicitly blocked is reachable and strengthen visibility controls and governance.
Read at Securitymagazine
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