You’d be hard-pressed to find anyone in an organization that says machine learning technology hasn’t benefited them in one way or another. At the same time, there has to be some caution practiced when using this technology due to the inherent threats it can open you up to. Cloud security professionals are developing new ways to combat these threats.
IT teams are already pushed to the limit by keeping their environments secure, but due to the fact that artificial intelligence technologies use a broader data source, cloud security professionals have to be more aware of the risks associated with the ways applications work. For example, attack vectors are still being revealed, and they can end up corrupting systems, and algorithms can be lost or stolen.
Addressing Protections
One of the best places to start is to consider how your most sensitive data is accessed and stored. What restrictions do you have in place, and do you have tracking mechanisms for seeing who accesses your data? This is something that has to be high on your list of priorities because knowing how your data is collected and accessed is critical in getting the most out of your artificial intelligence technology, but it’s also critical in protecting you from threats.
For your sensitive data, which when corrupted or stolen could result in significant business losses, you need to have alert systems in place and prioritize added layers of security for this data.
The Knowns and Unknowns
More than one cloud security professional has commented that there is no codified security testing mechanism in place for machine learning technology. However, knowing which attacks are most likely can be of use for a better approach to cloud security strategies.
First, consider how threats are able to get around a spam filter. Your employees rely on the spam filter to weed out the bad stuff, giving them a sense of security that when an email hits their inbox, it’s likely safe to open and click on links. This is among the more common threats that attackers have the most success with.
Next, your machine learning is built to work with specific data in specific ways. Unfortunately, cybercriminals have figured out how to work their way into your algorithms and make the technology work in ways you don’t want it to. Often, the attack happens when a model uses the data you’re capturing from end users and other public sources.
A third area to look into involves encrypting data because attackers are stealing it from model storage that is unsecured. Cybercriminals are also reverse engineering through a barrage of queries, which gives them a pattern to follow. Rather than stealing something, they’re actually able to build predictable models of their own.
Working with a trusted agent can help you develop more robust protections. At Cloud Source, we work with you to take the burden of security off your shoulders and supply you with strong cloud security solutions. Contact us and let’s discuss how we can keep your environment more secure.