Hacking the Hackers: Adversarial AI and How to Fight It

Advances in Artificial Intelligence (AI) have led to smarter, more robust network security platforms that are quickly replacing legacy security solutions.

Hackers are all too aware of the potential. As is often the case with new technology, in the wrong hands, AI can be used as a powerful cybercrime tool

Adversarial AI Overview

Adversarial AI refers to AI technology that is used to cause harm in some way. In the case of network security, adversarial AI can include automated attacks and breaches.

One example is the automation of phishing attacks. Today’s AI can create more convincing, natural-language communications, resulting in more successful attacks. 

These communications can be through email, where AI can incorporate language aligned with the corporate culture of a given target. 

Adversarial AI can even be used by phone. AI can crawl social media accounts, grab snippets of recorded voices from sources like speeches, and then trick call recipients into thinking they are speaking with a trusted coworker or authority figure. This technology has also been used to bypass voice-activated security systems. 

Other adversarial AI targets include:

·   Chatbots (live help sessions and surveys, for instance)

·   AI-integrated malware

·   Text messaging systems

For every successful “traditional” type of cybercrime, there is likely an AI-enhanced version already in use or being developed.

Skilled hackers can create malware that mimics routinely-used system components, granting access to highly sensitive, proprietary data. 

Worst of all, adversarial AI is so unpredictable that security analysts aren’t usually able to prepare for these attacks. Even modern, supervised learning platforms are no match for the new and growing threat posed by adversarial AI. 

Enter Unsupervised AI

Combatting adversarial AI threats requires broad predictive capabilities. In other words, the only way to stop adversarial AI is by equipping your network with equally-capable technology. 

MixMode’s third-wave AI is meeting the challenge. Through its use of intelligent, unsupervised generative AI, the MixMode platform can accurately predict and prevent most attacks and respond immediately if an attacker does gain access.

How MixMode Works

MixMode creates a baseline of a network over a few days, developing a foundation of network knowledge the platform can use to detect anomalies. Specifically, unsupervised generative AI predicts the next five-minutes of network activity and then compares actual activity. 

MixMode saves time and human capital. Unsupervised AI, not surprisingly, requires no supervision. SecOps teams can trust MixMode to root out and respond to security threats as they happen. MixMode’s robust capabilities result in fewer false positives for teams to analyze, freeing up time for these professionals to focus on real threats. 

In essence, MixMode’s third-wave AI is sophisticated enough to mimic human behavior and detect suspicious activity even if it has yet to encounter the scenario at hand. This is in stark contrast to security solutions that are bound by the limitations of their coding. It is all but impossible for a coder to dream up all the possible ways a network might be attacked. 

Learn More

Read a case study about how MixMode was able to detect and stop a bad actor that had breached a company’s network before the hacker was able to access customer data. Work with one of MixMode’s network security experts to set up a demo and learn how our platform can better protect your valuable network assets.

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