Security News
Those are a proper understanding of what AI is capable of and how it should be used, and improvements to the security of AI. To understand how machine learning works and how to use it properly, it is important to bear in mind that although some ML models are very complex, the systems incorporating ML are still just a product of combining an understanding of a domain and its data. Model evasion attacks essentially exploit the fact that decision boundaries in the model are very complex and the capability of the model to interpolate between samples is limited, in a way leaving "Gaps" to be utilized for.
Ramamoorthy is firmly on the affirmative side for using AI to fight cybercrime. "Attackers use powerful techniques like AI to exploit unsuspecting end-users to gain access to privileged information by compromising said access points."
In a report released on Wednesday, consulting firm Deloitte describes two tools that can make AI tasks such as machine learning more private and secure. There are some technological obstacles to using HE and FL. Processing encrypted data with HE is slower than processing unencrypted data.
The UK data protection regulator has announced its intention to issue a fine of £17m to controversial facial recognition company Clearview AI. Clearview AI, as you'll know if you've read any of our numerous previous articles about the company, essentially pitches itself as a social network contact finding service with extraordinary reach, even though no one in its immense facial recognition database ever signed up to "Belong" to the "Service". Early in 2020, those behemoths firmly told Clearview AI, "Stop leeching image data from our services."
Commentary: AI is considered "World changing" by policymakers, but it's unclear how to ensure positive outcomes. According to a new Clifford Chance survey of 1,000 tech policy experts across the United States, U.K., Germany and France, policymakers are concerned about the impact of artificial intelligence, but perhaps not nearly enough.
Microsoft has introduced an AI-driven ransomware attack detection system for Microsoft Defender for Endpoint customers that complements existing cloud protection by evaluating risks and blocking actors at the perimeter. As human-operated ransomware attacks are characterized by a specific set of methods and behaviors, Microsoft believes that they can use a data-driven AI approach to detect these types of attacks.
In 2021, over 59 million Americans said they lost money as a result of phone scams, with an average reported loss of $502. "Fraud is a major consideration in the telecom industry," said Dr. Gadi Solotorevsky, CTO at Amdocs cVidya, an AI solutions provider. In past practice, the telecom industry handled fraud complaints manually.
Could an emphasis on ethics in AI go the same route? Or should organizations realize the risks-as well as their responsibilities-in putting powerful AI applications into use without addressing ethical concerns? Or is there another way to deal with yet another area of quality without the excessive burden? AI starts out with no understanding of the impact of factors such as race, so if programmers haven't limited how data can be used by the AI, you can wind up with racial data being used, thus creating AI bias.
According to Darktrace's 2021 Ransomware Threat Report [PDF], ransomware attacks are on the rise. Ransomware recovery company Coveware reports that the median number of employees among ransomware victims stood at 200 in Q2 2021, and has actually dropped since the end of 2020.
The EU Parliament has voted in favor of a resolution that essentially calls for the ban of AI-powered biometric mass surveillance technologies such as facial recognition systems in the continent. With today's decision, the European Parliament asks for a permanent ban on the automated recognition of individuals in public spaces, and the prohibition of predicting policing based on behavioral data.