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AI for Cybersecurity is a hot new thing

AI for Cybersecurity is a hot new thing

Number of companies boasting about how they are using machine learning and artificial intelligence to help make the world a safer place.

But some experts worry vendors aren’t paying enough attention to the risks associated with relying heavily on these technologies. “What’s happening is a little concerning, and in some cases even dangerous,” warns Raffael Marty of security firm Forcepoint.
The security industry’s hunger for algorithms is understandable. It’s facing a tsunami of cyberattacks just as the number of devices being hooked up to the internet is exploding. At the same time, there’s a massive shortage of skilled cyber workers (see “Cybersecurity’s insidious new threat: workforce stress”).
Using machine learning and AI to help automate threat detection and response can ease the burden on employees, and potentially help identify threats more efficiently than other software-driven approaches.

Data dangers

But Marty and some others speaking at Black Hat say plenty of firms are now rolling out machine-learning-based products because they feel they have to in order to get an audience with customers who have bought into the AI hype cycle. And there’s a danger that they will overlook ways in which the machine-learning algorithms could create a false sense of security.

One versus many

In a session at the conference, Holly Stewart and Jugal Parikh of Microsoft flagged the risk of overreliance on a single, master algorithm to drive a security system. The danger is that if that algorithm is compromised, there’s no other signal that would flag a problem with it.
To help guard against this, Microsoft's Windows Defender threat protection service uses a diverse set of algorithms with different training data sets and features. So if one algorithm is hacked, the results from the others—assuming their integrity hasn’t been compromised too—will highlight the anomaly in the first model.

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