Tuesday, October 8, 2024
Home Tech & Gadgets Should organizations include MLSecOps in their cybersecurity strategy?

Should organizations include MLSecOps in their cybersecurity strategy?

by Jeffrey Beilley
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As more organizations embrace Artificial Intelligence (AI) and Machine Learning (ML) to optimize their operations and gain a competitive edge, there is increasing focus on how to best secure this powerful technology. Central to this is the data used to train ML models, which fundamentally impacts how they behave and perform over time. As a result, organizations must pay close attention to what goes into their models and be constantly vigilant for signs of something sinister, such as data corruption.

Unfortunately, as ML models have grown in popularity, so has the risk of malicious backdoor attacks, where criminals use data poisoning techniques to feed ML models with compromised data, causing them to behave in unforeseen or malicious ways when triggered by specific commands. While such attacks can be time-consuming to execute (often requiring large amounts of poisoned data over many months), they can be incredibly damaging if successful. For this reason, it’s something that organizations need to protect against, especially in the foundational stages of any new ML model.

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