Security News
Security, orchestration, automation, and response platforms try to make analysts' lives easier by mapping out automated incident response playbooks that coordinate activities between security appliances. The AI comes in especially useful here given email's popularity as an attack vector.
A reporter interviews a Uyghur human-rights advocate, and uses the Otter. Ai, the automated transcription app that I had used to record the interview.
Humans have far greater difficulty identifying images of biometric spoofing attacks compared to computers performing the same task, according to research released by ID R&D. The research report finds that computers are more adept than people at accurately and quickly determining whether a photo is of an actual, live person versus a presentation attack. The study tested humans and machines by presenting them with the most common spoofing techniques: printed photos, videos, digital images, and 2D or 3D masks.
With the incorporation of artificial intelligence and machine learning tools into surveillance technologies, the definition of surveillance is changing to encompass tools that are more beneficial to the average person. Under this expanded definition, surveillance technology has far-ranging positive applications across business and retail sectors that will create safer and more enjoyable environments that benefit everyone - not just those behind the camera.
AI is not going to solve your cybersecurity problems, so can we stop pinning our hopes on it? Instead of seeking a "Magic box" to solve all our problems, organizations should be looking at how skilled personnel can work with AI to utilize the strengths of each to improve the other. Think how many possible company setups there are, applications you can run, people you can work with, economic factors that can impact a company, and so on.
Insurers' use of predictive analytics to fight fraud has reached an all-time high, according to an insurance fraud technology study by the Coalition Against Insurance Fraud and SAS. The study reveals that 80% of insurers use predictive modeling to detect fraud, up from 55% in 2018. "The shifts we've seen since the 2018 study emphasize the increasingly sophisticated technologies needed to foil insurance fraudsters' criminal exploits," said David Hartley, Director of Insurance Solutions at SAS. "Predictive modeling is up 25%. Text mining has nearly doubled, jumping from 33% to 65% in three years. These findings prove that, even as COVID-19 has fueled rampant fraud, insurers are agilely stretching their advanced analytics capabilities to counter rapidly changing threats."
Urban surveillance and public safety technologies are finding new use cases following the COVID-19 pandemic and increasing AI capabilities. ABI Research forecasts a CAGR of 11.6 % with 1.4 billion Closed-Circuit Television surveillance cameras in urban areas worldwide in 2030.
In this interview with Help Net Security, Scott Laliberte, Managing Director at Protiviti, talks about the implementation of AI and ML in cybersecurity programs, why this is a good practice and how it can advance cybersecurity overall. To adopt these new technologies, the organization must not only change its existing approaches, but also change the mindset of its people and its culture in order to really embrace them.
Many organizations are looking for AI to make sense of tremendous amounts of unstructured data that has been collected about people, transactions, systems, and social connections. Video surveillance systems are a major concern, with AI now being able to identify and track people from networks of connected camera systems.
To delay it, highly advanced AI-powered security that protects your entire suite is needed. AI is integral for any good email security solution.