AI for cybersecurity is a hot new thing—and a dangerous gamble

When I walked around the exhibition floor at this week’s massive Black Hat cybersecurity conference in Las Vegas, I was struck by the 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.

Many products being rolled out involve “supervised learning,” which requires firms to choose and label data sets that algorithms are trained on—for instance, by tagging code that’s malware and code that is clean.

Marty says that one risk is that in rushing to get their products to market, companies use training information that hasn’t been thoroughly scrubbed of anomalous data points. That could lead to the algorithm missing some attacks. Another is that hackers who get access to a security firm’s systems could corrupt data by switching labels so that some malware examples are tagged as clean code.

The bad guys don’t even need to tamper with the data; instead, they could work out the features of code that a model is using to flag malware and then remove these from their own malicious code so the algorithm doesn’t catch it.

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.

Beyond these issues. Forcepoint's Marty notes that with some very complex algorithms it can be really difficult to work out why they actually spit out certain answers. This “explainability” issue can make it hard to assess what’s driving any anomalies that crop up (see “The dark secret at the heart of AI”).

None of this means that AI and machine learning shouldn’t have an important role in a defensive arsenal. The message from Marty and others is that it’s really important for security companies—and their customers—to monitor and minimize the risks associated with algorithmic models.

That’s no small challenge given that people with the ideal combination of deep expertise in cybersecurity and in data science are still as rare as a cool day in a Las Vegas summer.

This article is taken from technologyreview.com

See Open Jobs in AI

Tech News

3-Hr-3
25
Jun

Pandemic Lessons in Recruitment

- The global pandemic affects all aspects of the business world including recruitment - But in dealing with the new challenges we have learned some invaluable lessons - From the criteria in which you use to select new candidates to the technologies that have become the driving force for how recruitment takes place.
Read the full article
2-HR-1
16
Jun

Effect of COVID-19 on the recruitment and onboarding of new employees

  • Getting the right candidate for an open position was never an easy task, but with the onset of Covid-19, this already difficult task has become a lot harder
  • All conventional means of onboarding need to be reevaluated and reorganized to meet current challenges.
  • But with the right technology and adjustment to the onboarding process, the effects can be mitigated.
Read the full article
10-tech-trends-1
15
Jun

Top 10 technology trends that will redefine a post COVID-19 world

If past bad experiences on a global scale as taught us anything, it’s that they have led to innovations that drive businesses. The COVID-19 will be no different. It has upended our daily lives and caused individuals, organizations and countries to rethink their attitude to tech. with the lockdown in many countries, almost everyone have resorted to tech for their food, job, entertainment and communication
Read the full article