While it might be tempting to reduce face recognition to an inevitable Orwellian nightmare, its benefits cannot be realized unless we educate ourselves about how the technology really works, separate fact from fiction, and pass common sense regulation that set guidelines for use. Here are five popular misconceptions about face recognition and privacy to help set the record straight on this powerful, emerging technology.
Artificial intelligence (AI) presents a perfect solution to compensate for unmanned environments or those with limited staffing, or the loss of vigilance after looking at a screen too long. AI can help us not only watch continuously, but also feed systems that are able to sort, organize and categorize massive amounts of data in a way that human operators cannot. And it can do so far more reliably than traditional video analytics ever did.
New research revealed that while over half of organizations use artificial intelligence (AI) or machine learning in their security stack, nearly 60 percent are still more confident in cyberthreat findings verified by humans over AI.
Where disinformation was once communicated by telegram, the modern version of vast, coordinated campaigns are now disseminated through social media with bots, Twitterbots, and bot farms—at a scale humans could never perform. Now, disinformation campaigns can be lodged by a government to influence stock prices in another country, or by a private company to degrade brand presence and consumer confidence. What’s worse is that bots can facilitate these campaigns en masse.
Artificial Intelligence (AI) rests on the verge of transforming both business and society. Financial firm UBS forecasts that next year, the AI market will be worth $12.5 billion due to huge improvements and broader adoption of the technology. And BCG Henderson Institute found that though most leaders have not yet seen significant impact from their AI initiatives, they firmly expect to within the next five years.
Lots of security vendors talk about integrating innovative techniques using Artificial Intelligence. In cybersecurity, this often boils down to supervised or unsupervised anomaly detection of measures attributes. However, in many cases there is a big gap between the identification of anomalies and transforming them into actionable data.
There are lots of buzzwords floating around cybersecurity: machine learning, artificial intelligence, supervised and unsupervised learning … In many cases these advanced technologies are based on anomaly detection.
This month in Security magazine, we highlight COVID-19 and enterprise security's response. How has the pandemic changed business continuity plans, and what lessons have been learned? Also this month, we profile Chris Hallenbeck, CISO at Tanium, his view on metrics and information security. In addition, security experts discuss video analytics, how to make AI work within your cyber strategy and more.