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Should organizations consider MLSecOps in their cybersecurity strategy?

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As more and more organizations embrace Artificial Intelligence (AI) and Machine Learning (ML) to optimize their operations and gain a competitive advantage, there’s growing attention on how best to keep this powerful technology secure. At the center of this is the data used to train ML models, which has a fundamental impact on how they behave and perform over time. As such, organizations need to pay close attention to what’s going into their models and be constantly vigilant for signs of anything untoward, such as data corruption.

Unfortunately, as the popularity of ML models has risen, so too has the risk of malicious backdoor attacks that see criminals use data poisoning techniques to feed ML models with compromised data, making them behave in unforeseen or harmful ways when triggered by specific commands. While such attacks can take a lot of time to execute (often requiring large amounts of poison data over many months), they can be incredibly damaging when successful. For this reason, it is something that organizations need to protect against, particularly at the foundational stage of any new ML model.


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