Summarize AI recommendation poisoning is a growing threat where attackers manipulate data inputs to subtly influence AI outputs without triggering system failures. Recommendation systems are especially vulnerable due to open data ingestion and feedback loops that amplify biased signals over time. To mitigate this risk, enterprises must adopt strong data validation, continuous evaluation, and governance practices to ensure secure, reliable, and trustworthy AI systems. -------------------------...
