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For example, organizations are increasingly using prompt filtering, toxicity detection APIs and even proprietary guardrails for LLM applications. Failure to do so can result in reputational damage, as seen in multiple cases where chatbots generated offensive or misleading content. A 2024 McKinsey study found that 42% of enterprises deploying GenAI cited \u201ccontent integrity and governance\u201d as one of their top three operational risks. AI thrives on integrated data, but most enterprises are still working with fragmented systems.<\/p>\n<\/p>\n
Insider Risk Management in Microsoft Purview extends to Fabric lakehouses. It provides built-in risk indicators based on user activity, including potential data exfiltration. Organizations can use policies to detect risky actions such as exporting data. Data quality monitoring provides anomaly detection across all tables in a schema and data profiling at the table level. Anomaly detection automatically monitors freshness and completeness using historical data patterns, surfacing issues without manual configuration.<\/p>\n<\/p>\n